From 356231a4ee9030ac38745d4abb4902e407242e49 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Sun, 17 May 2026 22:20:32 +0300 Subject: [PATCH 01/31] =?UTF-8?q?add:=20FileInspector=20->=20FileInfo=20?= =?UTF-8?q?=D0=BF=D0=BE=D0=BB=D1=83=D1=87=D0=B5=D0=BD=D0=B8=D0=B5=20=D0=BC?= =?UTF-8?q?=D0=B5=D1=82=D0=B0=D0=B8=D0=BD=D1=84=D0=BE=D1=80=D0=BC=D0=B0?= =?UTF-8?q?=D1=86=D0=B8=D0=B8=20=D0=BE=20=D1=84=D0=B0=D0=B9=D0=BB=D0=B0?= =?UTF-8?q?=D1=85=20=D0=B1=D0=B5=D0=B7=20=D0=BF=D0=BE=D0=BB=D0=BD=D0=BE?= =?UTF-8?q?=D0=B9=20=D0=B7=D0=B0=D0=B3=D1=80=D1=83=D0=B7=D0=BA=D0=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Добавлен модуль file_inspector.py с классами: - FileInfo — pydantic-модель с метаинформацией о файле (format, width, height, duration, fps, sample_rate, bitrate, error_desc). Статус ok/partial/error определяется автоматически по наличию ключевых полей. - FetchPlan — план загрузки файла. Определяет сколько байт читать из начала (initial_head), размер докачки (expand_chunk), лимиты (max_head, min_head) и нужно ли читать хвост (need_tail). Метод from_content_type() строит план по MIME-типу и размеру файла. - RangeReader — базовый класс для чтения файлов частями. Единый интерфейс: async for chunks in reader. Свойства head, tail, head_size, tail_size. - RangeDownloader(HTTP) — загрузчик по URL. Retry при 429/500/502/503/504 с экспоненциальным backoff. Прогрессивная докачка с удвоением чанка. Определяет Content-Type из заголовков, строит план, читает head с докачкой и tail через Range-запросы. - RangeFileReader — читает локальный файл (anyio). Head, tail через seek, докачка по плану. - RangeBytesReader — читает из bytes/BytesIO/NamedBytesIO. Использует memoryview без копирования данных. Для маленьких файлов читает целиком. - FileInspector — высокоуровневая обёртка. Три метода: inspect_url(url) — удалённый файл inspect_file(path) — локальный файл inspect_bytes(data) — уже загруженные байты Возвращает FileInfo. Сохраняет last_file_info, head, tail после инспекции. Парсеры встроены в FileInspector как @classmethod. Поддерживают: Изображения: JPEG, PNG, GIF, WebP (VP8/VP8L/VP8X) Видео: MP4/MOV, AVI, MKV, WEBM, OGV Аудио: MP3, AAC, WAV, WMA, FLAC, OGG, M4A Добавлен метод bot.get_file_info(url) — получение метаинформации через FileInspector без полной загрузки файла. Примеры: 05_media_bot.py — добавлен /info метаинформация о replied-вложении через info = await bot.get_file_info(url) Тесты: - Параметризованные тесты на всех фикстурах из fixtures.json - Фикстуры можно расширять и править с помощью prepare_fixtures.py - inspect_url, inspect_file, inspect_bytes — сравнение результатов - Retry-логика: 503 → retry → успех, исчерпание retry, 404 без retry - Создание сессии когда не передана - HTML-страница → error - Пустой Content-Type → определение формата по байтам --- examples/05_media_bot.py | 98 +- maxapi/bot.py | 21 + maxapi/types/__init__.py | 2 + maxapi/types/file_info.py | 170 + maxapi/utils/file_inspector.py | 3403 +++++++++++++++++ tests/test_utils/fileinfo/__init__.py | 0 tests/test_utils/fileinfo/fixtures.json | 345 ++ tests/test_utils/fileinfo/prepare_fixtures.py | 310 ++ tests/test_utils/fileinfo/test_file_info.py | 555 +++ 9 files changed, 4898 insertions(+), 6 deletions(-) create mode 100644 maxapi/types/file_info.py create mode 100644 maxapi/utils/file_inspector.py create mode 100644 tests/test_utils/fileinfo/__init__.py create mode 100644 tests/test_utils/fileinfo/fixtures.json create mode 100644 tests/test_utils/fileinfo/prepare_fixtures.py create mode 100644 tests/test_utils/fileinfo/test_file_info.py diff --git a/examples/05_media_bot.py b/examples/05_media_bot.py index e1ad8439..67514e12 100644 --- a/examples/05_media_bot.py +++ b/examples/05_media_bot.py @@ -8,16 +8,19 @@ - Обработку входящих вложений: image, file, audio, video - Пересылку сообщений через message.forward() - SenderAction.SENDING_PHOTO / SENDING_VIDEO / SENDING_FILE +- FileInspector — получение метаинформации о файле без полной загрузки Команды: /photo — отправить тестовое изображение из файла /buffer — отправить изображение из буфера (байты) /upload — загрузить медиа заранее, затем отправить + /info — метаинформация о replied-вложении (FileInspector) Любой файл/фото/аудио/видео от пользователя пересылается обратно с описанием типа вложения. -Аналог Telegram: send_photo, send_document, send_audio, forward_message +Аналог Telegram: send_photo, send_document, send_audio, forward_message, +get_file Запуск: MAX_BOT_TOKEN=your_token python 05_media_bot.py @@ -30,11 +33,14 @@ import os import tempfile +import aiohttp + # Опционально: загрузка .env, если установлен python-dotenv with contextlib.suppress(ImportError): from dotenv import load_dotenv load_dotenv() + from maxapi import Bot, Dispatcher, F from maxapi.enums.sender_action import SenderAction from maxapi.filters.command import Command, CommandStart @@ -47,6 +53,7 @@ from maxapi.types.updates.message_created import MessageCreated logging.basicConfig(level=logging.INFO) +log = logging.getLogger() bot = Bot() dp = Dispatcher() @@ -77,6 +84,26 @@ def get_sample_image_path() -> str: return tmp_path +# ============================================================================ +# Хелперы +# ============================================================================ + + +def _get_first_attachment_url(attachments) -> str | None: + """Извлекает URL из первого вложения.""" + if not attachments: + return None + first = attachments[0] + if hasattr(first, "url") and first.url: + return first.url + return None + + +# ============================================================================ +# Команды +# ============================================================================ + + @dp.message_created(CommandStart()) async def on_start(event: MessageCreated) -> None: """Приветствие с описанием команд.""" @@ -85,7 +112,8 @@ async def on_start(event: MessageCreated) -> None: "Команды:\n" "/photo — фото из файла\n" "/buffer — фото из буфера\n" - "/upload — предзагрузка медиа\n\n" + "/upload — предзагрузка медиа\n" + "/info — метаинформация о replied-вложении\n\n" "Пришли мне любой файл, фото, аудио или видео — " "я расскажу, что получил, и перешлю обратно." ) @@ -123,6 +151,7 @@ async def cmd_buffer(event: MessageCreated) -> None: chat_id = event.message.recipient.chat_id if chat_id is None: return + await bot.send_action(chat_id=chat_id, action=SenderAction.SENDING_PHOTO) # Используем тот же минимальный PNG 1×1 пиксель для демонстрации. @@ -167,7 +196,53 @@ async def cmd_upload(event: MessageCreated) -> None: ) -# ── Обработка входящих вложений ──────────────────────────────────────────── +@dp.message_created(Command("info")) +async def cmd_info(event: MessageCreated) -> None: + """Получение метаинформации о файле через bot.get_file_info(). + + Ответьте командой на сообщение с вложением. + """ + chat_id = event.message.recipient.chat_id + if chat_id is None: + return + + replied_body = event.message.link.message if event.message.link else None + if not replied_body or not replied_body.attachments: + await event.message.answer( + "ℹ️ Ответьте этой командой на сообщение с файлом." + ) + return + + url = _get_first_attachment_url(replied_body.attachments) + if not url: + await event.message.answer("⚠️ Не удалось получить URL вложения.") + return + + await bot.send_action(chat_id=chat_id, action=SenderAction.SENDING_FILE) + + try: + info = await bot.get_file_info(url, timeout=10) + except Exception as e: + log.error("Ошибка инспекции: %s", e) + await event.message.answer("⚠️ Не удалось определить метаинформацию") + return + + if info.status == "error": + # info.status == "error" только если ничего не получилось определить, + # Даже минимально: info.format is None + # info.error_desc содерит описание ошибки + await event.message.answer(f"⚠️ {info.error_desc}") + return + + # Всё, что получилось узнать о файле — в строку через str(FileInfo) + answer = str(info) + + await event.message.answer(answer) + + +# ============================================================================ +# Обработка входящих вложений +# ============================================================================ @dp.message_created(F.message.body.attachments) @@ -198,13 +273,24 @@ async def on_attachment(event: MessageCreated) -> None: chat_id = event.message.recipient.chat_id if chat_id is None: return + await bot.send_action(chat_id=chat_id, action=action) # Информируем пользователя о полученном вложении count = len(attachments) - await event.message.answer( - f"Получено {count} вложение(й), тип: {label}. Пересылаю..." - ) + reply_txt = f"Получено {count} вложение(й), тип: {label}.\n\n" + + # Пытаемся получить метаинформацию через FileInspector + url = _get_first_attachment_url(attachments) + if url: + try: + info = await bot.get_file_info(url, timeout=3) + reply_txt += f"Первое вложение:\n{info}" + except aiohttp.ClientError: + # Не дошло даже до HTTP (нет сети, DNS) + reply_txt += "Не удалось получить подробности:\nСетевая ошибка" + reply_txt += "Пересылаю..." + await event.message.answer(reply_txt) # Пересылаем оригинальное сообщение обратно await event.message.forward(chat_id=chat_id) diff --git a/maxapi/bot.py b/maxapi/bot.py index 2748d503..f196fc6a 100644 --- a/maxapi/bot.py +++ b/maxapi/bot.py @@ -43,6 +43,7 @@ from .methods.send_message import SendMessage from .methods.subscribe_webhook import SubscribeWebhook from .methods.unsubscribe_webhook import UnsubscribeWebhook +from .utils.file_inspector import FileInspector from .utils.message import process_input_media if TYPE_CHECKING: @@ -81,6 +82,7 @@ from .types.attachments.video import Video from .types.chats import Chat, ChatMember, Chats from .types.command import BotCommand + from .types.file_info import FileInfo from .types.input_media import InputMedia, InputMediaBuffer from .types.message import Message, Messages, NewMessageLink from .types.updates.message_callback import MessageForCallback @@ -737,6 +739,25 @@ async def get_video(self, video_token: str) -> Video: return await GetVideo(bot=self, video_token=video_token).fetch() + async def get_file_info(self, url: str, *, timeout: int = 10) -> FileInfo: + """ + Получает метаинформацию о файле по URL. + + Аналог ``telegram.Bot.get_file``, но с расширенными полями + (размеры, длительность, битрейт и т.д.). Работает с внутренними + и внешними URL. Для загрузки используются HTTP Range-запросы + (обычно 2–128 КБ вместо полного файла). + + Args: + url: URL файла. + timeout: Таймаут HTTP-запроса в секундах. + + Returns: + FileInfo: Метаинформация о файле. + """ + inspector = FileInspector() + return await inspector.inspect_url(url, timeout=timeout) + async def send_callback( self, callback_id: str, diff --git a/maxapi/types/__init__.py b/maxapi/types/__init__.py index 65c76403..f85c0072 100644 --- a/maxapi/types/__init__.py +++ b/maxapi/types/__init__.py @@ -39,6 +39,7 @@ from ..types.updates.message_removed import MessageRemoved from ..types.updates.user_added import UserAdded from ..types.updates.user_removed import UserRemoved +from .file_info import FileInfo from .input_media import InputMedia, InputMediaBuffer __all__ = [ @@ -61,6 +62,7 @@ "DialogMuted", "DialogRemoved", "DialogUnmuted", + "FileInfo", "FromUserRef", "InputMedia", "InputMediaBuffer", diff --git a/maxapi/types/file_info.py b/maxapi/types/file_info.py new file mode 100644 index 00000000..0f2efe3b --- /dev/null +++ b/maxapi/types/file_info.py @@ -0,0 +1,170 @@ +from typing import Literal + +from pydantic import BaseModel, ConfigDict + + +class FileInfo(BaseModel): + """ + Метаинформация о медиафайле. + + Attributes: + url: URL или путь к источнику. + mime_type: MIME-тип (по сигнатуре или из заголовка). + file_name: Имя файла. + file_size: Размер в байтах. + width: Ширина кадра (изображение/видео). + height: Высота кадра (изображение/видео). + duration: Длительность в секундах (аудио/видео). + fps: Частота кадров (видео). + sample_rate: Частота дискретизации (аудио), Гц. + bitrate_nominal: Номинальный битрейт из метаданных, кбит/с. + bitrate_avg: Средний битрейт (размер / длительность), кбит/с. + error_desc: Описание ошибки или предупреждения. + format: Определённый формат контейнера/кодека (по сигнатуре). + """ + + model_config = ConfigDict(frozen=True) + + url: str + mime_type: str = "" + file_name: str = "" + file_size: int | None = None + width: int | None = None + height: int | None = None + duration: float | None = None + fps: float | None = None + sample_rate: int | None = None + bitrate_nominal: int | None = None + bitrate_avg: int | None = None + error_desc: str = "" + format: ( + Literal[ + "PNG", + "JPEG", + "GIF", + "WEBP", + "WEBP/VP8X", + "WEBP/VP8", + "WEBP/VP8L", + "MP4", + "AVI", + "MKV", + "WEBM", + "OGG", + "OGV", + "M4A", + "MP3", + "AAC", + "WAV", + "WMA", + "FLAC", + ] + | None + ) = None + + @property + def has_dimensions(self) -> bool: + """True, если известны ширина и высота.""" + return self.width is not None and self.height is not None + + @property + def is_image(self) -> bool: + """True, если MIME-тип относится к изображению.""" + return self.mime_type.startswith("image/") + + @property + def is_audio(self) -> bool: + """True, если MIME-тип относится к аудио.""" + return self.mime_type.startswith("audio/") + + @property + def is_video(self) -> bool: + """True, если MIME-тип относится к видео.""" + return self.mime_type.startswith("video/") + + @property + def file_size_human(self) -> str: + """Размер файла в человекочитаемом виде.""" + if self.file_size is None: + return "неизвестно" + if self.file_size < 1024: + return f"{self.file_size} байт" + if self.file_size < 1_048_576: + return f"{self.file_size / 1024:.0f} КБ" + if self.file_size < 1_073_741_824: + return f"{self.file_size / 1_048_576:.1f} МБ" + return f"{self.file_size / 1_073_741_824:.2f} ГБ" + + @property + def status(self) -> Literal["ok", "partial", "error", "unknown"]: + """ + Степень полноты метаданных. + + Returns: + ``ok`` — ключевые поля для типа медиа заполнены; + ``partial`` — часть полей отсутствует; + ``error`` — ошибка получения данных; + ``unknown`` — тип медиа не распознан. + """ + if self.error_desc and not self.format: + return "error" + + if self.is_image: + if self.width and self.height: + return "ok" + return "partial" + + elif self.is_audio: + if self.duration and self.sample_rate: + return "ok" + return "partial" + + elif self.is_video: + if self.duration and self.width and self.fps: + return "ok" + return "partial" + else: + return "unknown" + + def __eq__(self, other: object) -> bool: + """Сравнение без учёта ``url`` и ``file_name``.""" + if not isinstance(other, FileInfo): + return NotImplemented + s = self.model_dump() + o = other.model_dump() + del s["url"] + del o["url"] + del s["file_name"] + del o["file_name"] + return s == o + + def __str__(self) -> str: + """Форматированная строка для вывода пользователю.""" + # fmt: off + lines = [ f"Имя файла: {self.file_name}", + f"Размер: {self.file_size_human}", + ] + _FIELD_LABELS = ( + ("format", "Формат: {}"), + ("width", " Размеры: {}×"), + ("height", None), # добавляется к width + ("duration", " Длительность: {} сек"), + ("fps", " Частота кадров: {} к/с"), + ("sample_rate", " Аудио: {} Гц"), + ("bitrate_nominal", " Битрейт (номинальный): {} кбит/с"), + ("bitrate_avg", " Битрейт (средний): {} кбит/с"), + ) + # fmt: on + for field, tmpl in _FIELD_LABELS: + value = getattr(self, field, None) + if not value: + continue + if field == "height": + lines[-1] = lines[-1].rstrip(" пикс") + f"×{value} пикс" + elif tmpl: + lines.append(tmpl.format(value)) + + if self.error_desc: + lines.append(f"⚠️ {self.error_desc}") + + return "\n".join(lines) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py new file mode 100644 index 00000000..84434892 --- /dev/null +++ b/maxapi/utils/file_inspector.py @@ -0,0 +1,3403 @@ +from __future__ import annotations + +""" +Инспекция медиафайлов без полной загрузки. + +Модуль читает начало и при необходимости конец файла (локально, по HTTP +или из bytes) и извлекает метаданные: формат, размеры, длительность, +битрейт. Высокоуровневая точка входа — :class:`FileInspector`. +""" + +import asyncio +import logging +import mimetypes +import struct +from abc import ABC, abstractmethod +from io import BytesIO +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, cast +from urllib.parse import unquote, urlparse + +import aiohttp +import anyio +from aiohttp import ClientConnectionError, ClientResponse, ClientTimeout +from pydantic import BaseModel + +from ..connection.base import NamedBytesIO +from ..types.file_info import FileInfo + +if TYPE_CHECKING: + from collections.abc import AsyncIterator, Callable + + from multidict import CIMultiDictProxy + +logger = logging.getLogger("maxapi.fileinfo") + +# HTTP-статусы, при которых имеет смысл повторить запрос: +# 429 — Too Many Requests (сервер просит подождать) +# 500 — Internal Server Error (временная ошибка сервера) +# 502 — Bad Gateway (промежуточный прокси не справился) +# 503 — Service Unavailable (сервер перегружен или на обслуживании) +# 504 — Gateway Timeout (промежуточный прокси не дождался ответа) +DEFAULT_RETRY_STATUSES: tuple[int, ...] = (429, 500, 502, 503, 504) + +_FORMAT_TO_MIME: dict[str, str] = { + "JPEG": "image/jpeg", + "PNG": "image/png", + "GIF": "image/gif", + "WEBP": "image/webp", + "WEBP/VP8": "image/webp", + "WEBP/VP8L": "image/webp", + "WEBP/VP8X": "image/webp", + "MP4": "video/mp4", + "AVI": "video/x-msvideo", + "WEBM": "video/webm", + "OGG": "audio/ogg", + "OGV": "video/ogg", + "MP3": "audio/mpeg", + "AAC": "audio/aac", + "WAV": "audio/wav", + "WMA": "audio/x-ms-wma", + "M4A": "audio/mp4", + "FLAC": "audio/flac", + "MKV": "video/x-matroska", +} + +# ============================================================================ +# [ ] Структуры данных +# ============================================================================ + + +class FetchPlan(BaseModel): + """ + План частичного чтения файла. + + Attributes: + initial_head: Размер первого блока с начала файла. + expand_chunk: Размер блока докачки (0 — без докачки). + max_head: Верхняя граница размера head. + min_head: Нижняя граница размера head. + need_tail: Размер блока с конца (0 — хвост не нужен). + """ + + initial_head: int = 2048 + expand_chunk: int = 8196 + max_head: int = 128_000 + min_head: int = 2048 + need_tail: int = 0 + + @classmethod + def from_content_type( # noqa: C901 + cls, + content_type: str, + file_size: int | None = None, + ) -> FetchPlan: + """ + Строит план по MIME-типу и размеру файла. + + Args: + content_type: MIME-тип из заголовков или guess. + file_size: Размер файла в байтах, если известен. + + Returns: + FetchPlan: План чтения для данного типа контента. + """ + # Маленькие файлы качаем целиком + if file_size and file_size <= 64_000: + return cls( + initial_head=file_size, + max_head=file_size, + min_head=file_size, + ) + + # AVI: начало с докачкой + if content_type in ("video/x-msvideo", "video/msvideo"): + return cls( + initial_head=8192, + expand_chunk=4096, + max_head=256000, + ) + + # MP3: может иметь большой ID3v2 и иногда нужен хвост + if content_type in ("audio/mpeg", "audio/mp3"): + return cls( + initial_head=8192, + expand_chunk=8192, + max_head=32_768, + need_tail=8192, + ) + + # Видео с moov/seekhead в конце. + # Без хвоста не возможно определить длительность + if content_type in ( + "audio/ogg", + "video/ogg", + "application/ogg", + "video/ogv", + ): + return cls( + initial_head=8192, + expand_chunk=8192, + need_tail=8192, + ) + + # Видео mp4, если потоковое то все параметры записаны вконце + if content_type == "video/mp4": + return cls( + initial_head=8192, + expand_chunk=8192, + max_head=65_536, + need_tail=24_576, + ) + + # WMA: большой начальный кусок + if content_type in ("audio/x-ms-wma", "audio/wma"): + return cls( + initial_head=8192, + expand_chunk=8192, + ) + + # JPEG + if content_type == "image/jpeg": + if file_size: + # За счёт EXIF+preview информация о размере может быть глубже + # Кривая: резкий рост до ~10 КБ на маленьких файлах, + # плавный до ~30 КБ на средних, пологий до 64 КБ на больших. + # 10 КБ → 2.9 КБ + # 500 КБ → 17.5 КБ + # 1024 КБ → 25 КБ + needed = min(512 + int(24 * (file_size**0.5)), 65536) + else: + needed = 8192 + + return cls( + initial_head=needed, + expand_chunk=needed // 2, + ) + + # GIF/WebP Для оценки длительности анимации нужно >= 3% файла + if content_type in ("image/gif", "image/webp"): + needed = ( + max(20_240, (file_size or 0) // 25) if file_size else 20_240 + ) + needed = min(needed, 256_000) + return cls( + initial_head=needed, + expand_chunk=needed, # Вероятно никогда не будет использовано + max_head=needed, # Чтобы не урезалось + ) + + # По умолчанию для видео + if content_type.startswith("video/"): + return cls( + initial_head=8192, + expand_chunk=8192, + ) + + # По умолчанию + return cls() + + +class MediaChunks(BaseModel): + """Фрагменты файла, доступные парсеру.""" + + head: bytes = b"" + tail: bytes = b"" + file_size: int | None = None + is_complete: bool = False + fetched_head: int = 0 + fetched_tail: int = 0 + + +class FileMeta(BaseModel): + """HTTP-метаданные до чтения тела файла.""" + + url: str + content_type: str = "" + file_name: str = "" + file_size: int | None = None + + +# ============================================================================ +# [ ] Базовый класс RangeReader +# ============================================================================ + + +class RangeReader(ABC): + """Базовый интерфейс для чтения файлов по частям.""" + + def __init__( + self, + plan: FetchPlan, + content_type: str, + file_name: str, + file_size: int | None, + ): + self.plan = plan + self.content_type = content_type + self.file_name = file_name + self.file_size = file_size + self.head: bytes = b"" + self.tail: bytes = b"" + + @abstractmethod + def __aiter__(self) -> AsyncIterator[MediaChunks]: ... + + @abstractmethod + async def close(self): ... + + def _make_chunks(self) -> MediaChunks: + is_complete = ( + self.file_size is not None + and len(self.head) + len(self.tail) >= self.file_size + ) + return MediaChunks( + head=self.head, + tail=self.tail, + file_size=self.file_size, + is_complete=is_complete, + fetched_head=len(self.head), + fetched_tail=len(self.tail), + ) + + +# ============================================================================ +# [ ] RangeFileReader +# ============================================================================ + + +class RangeFileReader(RangeReader): + """Читает локальный файл: head, tail через seek, expand.""" + + def __init__( + self, + path: str, + *, + full_read_limit: int = 20_971_520, # 20 Мб + ): + self.path = path + file_path = Path(path) + + file_name = file_path.name + file_size = file_path.stat().st_size + content_type, _ = mimetypes.guess_type(path) + content_type = content_type or "application/octet-stream" + + if file_size < full_read_limit: + # Небольшие данные будем анализировать целиком + plan = FetchPlan( + initial_head=file_size, + expand_chunk=0, + max_head=file_size, + need_tail=0, + ) + else: + plan = FetchPlan.from_content_type(content_type, file_size) + logger.debug( + "FILE plan: initial=%s, expand=%s, tail=%s", + plan.initial_head, + plan.expand_chunk, + plan.need_tail, + ) + super().__init__(plan, content_type, file_name, file_size) + + async def __aiter__(self) -> AsyncIterator[MediaChunks]: + # 1. Tail + if self.plan.need_tail > 0: + async with await anyio.open_file(self.path, "rb") as f: + await f.seek( + max(0, (self.file_size or 0) - self.plan.need_tail) + ) + self.tail = await f.read() + yield self._make_chunks() + + # 2. Head + expand + async with await anyio.open_file(self.path, "rb") as f: + # Первый чанк + self.head = await f.read(self.plan.initial_head) + logger.debug( + "head len=%s, tail len=%s", len(self.head), len(self.tail) + ) + yield self._make_chunks() + + # Докачка + while ( + self.plan.expand_chunk > 0 + and len(self.head) < self.plan.max_head + ): + chunk = await f.read(self.plan.expand_chunk) + if not chunk: + break + self.head += chunk + yield self._make_chunks() + + async def close(self): + pass # anyio.open_file закрывается через async with + + +# ============================================================================ +# [ ] RangeBytesReader +# ============================================================================ + + +class RangeBytesReader(RangeReader): + """Читает из bytes/BytesIO без сети.""" + + def __init__( + self, + data: bytes | BytesIO | NamedBytesIO, + file_name: str = "", + *, + full_read_limit=20_971_520, # 20 Мб + ): + if isinstance(data, (BytesIO, NamedBytesIO)): + self._file_name = file_name or getattr(data, "name", "") + raw = data.getbuffer() # memoryview без копирования + else: + self._file_name = file_name + raw = memoryview(data) # bytes → memoryview + + self._file_size = len(raw) + content_type, _ = mimetypes.guess_type(self._file_name) + content_type = content_type or "application/octet-stream" + + if self._file_size < full_read_limit: + # Небольшие данные будем анализировать целиком + plan = FetchPlan( + initial_head=self._file_size, + expand_chunk=0, + max_head=self._file_size, + need_tail=0, + ) + else: + plan = FetchPlan.from_content_type(content_type, self._file_size) + logger.debug( + "BYTES plan: initial=%s, expand=%s, tail=%s", + plan.initial_head, + plan.expand_chunk, + plan.need_tail, + ) + super().__init__(plan, content_type, self._file_name, self._file_size) + self._raw = raw + + async def __aiter__(self) -> AsyncIterator[MediaChunks]: + # 1. Tail + if self.plan.need_tail > 0: + tail_start = max(0, self._file_size - self.plan.need_tail) + self.tail = bytes(self._raw[tail_start:]) + yield self._make_chunks() + + # 2. Head + expand (синхронно, без await) + pos = 0 + while pos < self._file_size: + chunk_size = ( + self.plan.expand_chunk if pos > 0 else self.plan.initial_head + ) + if chunk_size <= 0: + break + + end = min(pos + chunk_size, self._file_size) + self.head = bytes(self._raw[:end]) + pos = end + logger.debug( + "head len=%s, tail len=%s", len(self.head), len(self.tail) + ) + + yield self._make_chunks() + + if end >= self._file_size: + break + + async def close(self): + pass + + +# ============================================================================ +# [ ] RangeDownloader (HTTP) +# ============================================================================ + + +class RangeDownloader(RangeReader): + """ + Самодостаточный загрузчик: определяет тип файла, планирует стратегию, + скачивает данные не разрывая соединение. + + Использование: + async with RangeDownloader(url, session=session) as downloader: + async for chunks in downloader: + if has_enough(chunks): + await downloader.success() + break + """ + + def __init__( + self, + url: str, + *, + session: aiohttp.ClientSession | None = None, + headers: dict | None = None, + max_total: int = 256_000, + timeout: float = 30, + sock_connect: int = 5, + max_retries: int = 3, + retry_on_statuses: tuple[int, ...] = DEFAULT_RETRY_STATUSES, + retry_backoff_factor: float = 1.0, + ): + super().__init__( + plan=FetchPlan(), + content_type="", + file_name="", + file_size=None, + ) + self.original_url = url + self.max_total = max_total + + # Параметры соединения + self.timeout = ClientTimeout(total=timeout, sock_connect=sock_connect) + self.max_retries = max_retries + self.retry_on_statuses = retry_on_statuses + self.retry_backoff_factor = retry_backoff_factor + + # Сессия + self._own_session = session is None + self.session = session + + # Заголовки + self.headers = { + "User-Agent": ( + "Mozilla/5.0 (Windows NT 10.0; Win64; x64) " + "AppleWebKit/537.36 (KHTML, like Gecko) " + "Chrome/124.0.0.0 Safari/537.36" + ), + "Accept-Encoding": "identity", + } + if headers: + self.headers.update(headers) + + # Состояние + self._response: ClientResponse | None = None + self._closed: bool = False + self._fetched_meta: bool = False + self._meta: FileMeta | None = None + self._expand_count = 0 + + @property + def final_url(self) -> str: + return self._meta.url if self._meta else self.original_url + + # ======================================================================== + # Async Context Manager + # ======================================================================== + + async def __aenter__(self): + if self._own_session: + self.session = aiohttp.ClientSession(timeout=self.timeout) + return self + + async def __aexit__(self, *args): + await self.close() + if self._own_session and self.session: + await self.session.close() + self.session = None + + # ======================================================================== + # Итерация по чанкам + # ======================================================================== + + async def __aiter__(self) -> AsyncIterator[MediaChunks]: + if self._closed: + return + + # Получаем метаинформацию и строим план + if not self._fetched_meta: + await self._fetch_meta() + self._meta = cast(FileMeta, self._meta) + self.content_type = self._meta.content_type + self.file_name = self._meta.file_name + self.file_size = self._meta.file_size + self.plan = FetchPlan.from_content_type( + self._meta.content_type, + self._meta.file_size, + ) + self._fetched_meta = True + logger.debug( + "URL plan: initial_head=%s, expand=%s, tail=%s, max=%s", + self.plan.initial_head, + self.plan.expand_chunk, + self.plan.need_tail, + self.plan.max_head, + ) + + # 1. Tail + if self.plan.need_tail > 0: + self.tail = await self._fetch_chunk( + self.plan.need_tail, + tail=True, + ) + yield self._make_chunks() + + # 2. Head + if self.plan.initial_head > 0: + self.head = await self._fetch_chunk( + self.plan.initial_head, + tail=False, + ) + yield self._make_chunks() + + # 3. Докачка + self._expand_count = 0 # Сброс перед докачкой + while ( + self.plan.expand_chunk > 0 and len(self.head) < self.plan.max_head + ): + chunk = await self._expand_head() + if not chunk: + break + self.head += chunk + yield self._make_chunks() + + # ======================================================================== + # Управление + # ======================================================================== + + async def close(self): + if not self._closed: + if self._response: + self._response.release() + self._response = None + self._closed = True + + # ======================================================================== + # Private: Meta + # ======================================================================== + + async def _fetch_meta(self): + """Получает метаинформацию с retry.""" + response = await self._request_with_retry(self.original_url) + async with response: + final_url = str(response.url) + http_headers = response.headers + content_type = http_headers.get("Content-Type", "") + file_name = self._extract_filename(http_headers, self.original_url) + + try: + file_size = ( + int(http_headers.get("Content-Length", "0")) or None + ) + except ValueError: + file_size = None + + self._meta = FileMeta( + url=final_url, + content_type=content_type, + file_name=file_name, + file_size=file_size, + ) + + # ======================================================================== + # Private: Fetch with retry + # ======================================================================== + + async def _fetch_chunk( + self, + size: int, + *, + tail: bool = False, + ) -> bytes: + """ + Читает head или tail с retry и циклом до нужного размера. + + Args: + size: сколько байт читать. + tail: True — Range-запрос с конца, False — начало файла. + """ + if not self._meta: + raise RuntimeError("Метаинформация не загружена.") + + response = await self._request_with_retry( + self._meta.url, + allow_range=tail, + range_bytes=size if tail else None, + ) + + if tail: + async with response: + if response.status not in (200, 206): + logger.debug( + "Range не поддерживается: %s", response.status + ) + return b"" + return await self._read_response(response, size) + else: + # Head: сохраняем соединение для докачки + self._response = response + data = await self._read_response(response, size) + + # Проверка: если tail повторяет начало head, + # то Range не поддерживается + if ( + self.tail + and len(data) >= len(self.tail) + and data[: len(self.tail)] == self.tail + ): + logger.debug("Range не поддерживается: tail == head") + self.tail = b"" + + logger.debug( + "Скачан %s: %s байт", "tail" if tail else "head", len(data) + ) + + return data + + async def _read_response( + self, response: ClientResponse, size: int + ) -> bytes: + actual = min(size, self.max_total) + data = b"" + while len(data) < actual: + chunk = await response.content.read(actual - len(data)) + if not chunk: + break + data += chunk + return data + + async def _expand_head(self) -> bytes: + """Докачивает дополнительный кусок к head с удвоением размера.""" + if not self._response or getattr(self._response, "closed", False): + return b"" + + allowed = self.max_total - len(self.head) - len(self.tail) + if allowed <= 0: + return b"" + + # Удвоение от начального expand_chunk + chunk_size = min( + self.plan.expand_chunk * (2**self._expand_count), + allowed, + ) + self._expand_count += 1 + + try: + chunk = await self._response.content.read(chunk_size) + if chunk: + logger.debug("Докачано: %s байт", len(chunk)) + return chunk + except Exception: + return b"" + + async def _request_with_retry( + self, + url: str, + *, + allow_range: bool = False, + range_bytes: int | None = None, + ) -> ClientResponse: + """GET-запрос с retry при серверных ошибках.""" + if not self.session: + raise RuntimeError( + "Сессия не установлена. " + "Используйте async with RangeDownloader(...)" + ) + last_exception = None + + for attempt in range(self.max_retries + 1): + try: + headers = dict(self.headers) + if allow_range and range_bytes: + headers["Range"] = f"bytes=-{range_bytes}" + + response = await self.session.get(url, headers=headers) + except ClientConnectionError as e: + last_exception = e + if attempt < self.max_retries: + delay = self.retry_backoff_factor * (2**attempt) + logger.debug( + "Retry %s/%s через %.1fс для %s (connection error)", + attempt + 1, + self.max_retries, + delay, + url, + ) + await asyncio.sleep(delay) + continue + raise + + # Серверные ошибки — retry + if response.status in self.retry_on_statuses: + response.release() + if attempt < self.max_retries: + delay = self.retry_backoff_factor * (2**attempt) + logger.debug( + "Retry %s/%s через %.1fс для %s (HTTP %s)", + attempt + 1, + self.max_retries, + delay, + url, + response.status, + ) + await asyncio.sleep(delay) + continue + raise aiohttp.ClientResponseError( + status=response.status, + message=( + f"Server error after {self.max_retries} " + f"retries: HTTP {response.status}" + ), + headers={}, # type: ignore + request_info=response.request_info, + history=(), + ) + + # Клиентские ошибки — не retry + if not response.ok: + response.release() + raise aiohttp.ClientResponseError( + status=response.status, + message=f"HTTP {response.status}", + headers=response.headers, + request_info=response.request_info, + history=response.history, + ) + + return response + + raise last_exception # type: ignore + + # ======================================================================== + # Private: Helpers + # ======================================================================== + + @staticmethod + def _extract_filename( + headers: dict[str, Any] | CIMultiDictProxy[str], + url: str, + ) -> str: + """Извлекает имя файла из Content-Disposition или URL.""" + disp = headers.get("Content-Disposition", "") + if "filename=" in disp: + for part in disp.split(";"): + p = part.strip() + if p.startswith("filename="): + name = p.split("=", 1)[1].strip("\"'") + return unquote(name) + + path = urlparse(url).path + name = path.rstrip("/").rsplit("/", 1)[-1] + return unquote(name) or "unknown" + + +# ----------------------------------------------------------------------------- +# [ ] FileInspector +# ----------------------------------------------------------------------------- + + +class FileInspector: + """ + Высокоуровневая обёртка: получает части файла и парсит метаданные + в FileInfo. + + Поддерживает три источника: + 1. URL — скачивает по HTTP через RangeDownloader. + 2. Локальный файл — читает через RangeFileReader. + 3. bytes/BytesIO — читает через RangeBytesReader. + + Использование: + inspector = FileInspector() + info = await inspector.inspect_url("https://example.com/video.mp4", + session=session) + info = await inspector.inspect_file("/path/to/video.mp4") + info = await inspector.inspect_bytes(downloaded_bytes) + """ + + def __init__(self): + self._last_reader: RangeReader | None = None + self.last_file_info: FileInfo | None = None + + @property + def last_head(self) -> bytes: + """Байты head последней инспекции.""" + + return self._last_reader.head if self._last_reader else b"" + + @property + def last_tail(self) -> bytes: + """Байты tail последней инспекции.""" + + return self._last_reader.tail if self._last_reader else b"" + + # ======================================================================== + # Публичные методы + # ======================================================================== + async def inspect_url( + self, + url: str, + *, + session: aiohttp.ClientSession | None = None, + timeout: int = 30, # noqa: ASYNC109 + max_total: int = 256_000, + max_retries: int = 3, + retry_on_statuses: tuple[int, ...] = DEFAULT_RETRY_STATUSES, + retry_backoff_factor: float = 1.0, + ) -> FileInfo: + """ + Инспектирует удалённый файл по URL. + + Args: + url: URL файла. + session: Общая aiohttp-сессия (создаётся при ``None``). + timeout: Таймаут HTTP-запроса в секундах. + max_total: Максимальный объём скачанных данных (байт). + max_retries: Число повторных попыток при ``retry_on_statuses``. + retry_on_statuses: HTTP-статусы, при которых повторять запрос. + retry_backoff_factor: Множитель задержки между попытками + (1.0 → 1с, 2с, 4с). + + Returns: + FileInfo: Результат инспекции (в т.ч. при сетевой ошибке). + """ + + try: + async with RangeDownloader( + url, + session=session, + timeout=timeout, + max_total=max_total, + max_retries=max_retries, + retry_on_statuses=retry_on_statuses, + retry_backoff_factor=retry_backoff_factor, + ) as reader: + return await self._inspect(reader, url=url) + except aiohttp.ClientError as e: + logger.error("Сетевая ошибка: %s", e) + self.last_file_info = self._build_file_info( + url=url, + error_desc=f"Сетевая ошибка: {e}", + ) + return self.last_file_info + except Exception as e: + logger.exception("Ошибка инспекции: %s", e) + self.last_file_info = self._build_file_info( + url=url, + error_desc=str(e), + ) + return self.last_file_info + + async def inspect_file( + self, + path: str, + *, + full_read_limit: int = 20_971_520, # 20 Мб + ) -> FileInfo: + """ + Инспектирует локальный файл. + + Args: + path: Путь к файлу. + full_read_limit: Файлы меньше этого размера читаются целиком. + + Returns: + FileInfo: Результат инспекции. + """ + try: + file_path = anyio.Path(path) + if not await file_path.exists(): + self.last_file_info = self._build_file_info( + url=path, + error_desc="Файл не найден", + ) + return self.last_file_info + reader = RangeFileReader( + str(await file_path.resolve()), full_read_limit=full_read_limit + ) + return await self._inspect(reader, url=path) + except Exception as e: + logger.exception("Ошибка инспекции файла: %s", e) + self.last_file_info = self._build_file_info( + url=path, + error_desc=str(e), + ) + return self.last_file_info + + async def inspect_bytes( + self, + data: bytes | BytesIO, + *, + file_name: str = "", + full_read_limit: int = 20_971_520, # 20 Мб + ) -> FileInfo: + """ + Инспектирует уже загруженные байты. + + Args: + data: Содержимое файла. + file_name: Имя файла (для guess MIME по расширению). + full_read_limit: Буферы меньше этого размера читаются целиком. + + Returns: + FileInfo: Результат инспекции. + """ + if isinstance(data, BytesIO): + file_name = file_name or getattr(data, "name", "") + + reader = RangeBytesReader( + data, file_name, full_read_limit=full_read_limit + ) + return await self._inspect(reader, url="") + + # ======================================================================== + # Private: общая логика инспекции + # ======================================================================== + + async def _inspect( + self, + reader: RangeReader, + *, + url: str, + ) -> FileInfo: + """Общая логика для любого источника (RangeReader).""" + self._last_reader = reader + dims = {} + async for chunks in reader: + # Проверка: не HTML + if self._looks_like_html(chunks.head, reader.content_type): + self.last_file_info = self._build_file_info( + url=url, + mime_type=reader.content_type, + file_name=reader.file_name, + file_size=reader.file_size, + error_desc="Файл не является медиа (HTML-страница)", + ) + return self.last_file_info + + # Парсим + dims = ( + self.parse_media_dimensions( + chunks.head, + chunks.tail, + reader.content_type, + reader.file_size, + ) + or {} + ) + + # Уточняем content_type если octet-stream + content_type = reader.content_type + fmt = dims.get("format") + if ( + fmt + and _FORMAT_TO_MIME.get(fmt) + and ( + not content_type + or content_type == "application/octet-stream" + ) + ): + content_type = _FORMAT_TO_MIME[fmt] + + # Достаточно ли данных + if self._is_complete(dims, content_type): + logger.debug( + "Использовано финально head_len=%s, tail_len=%s", + len(chunks.head), + len(chunks.tail), + ) + + self.last_file_info = self._build_file_info( + url=url, + mime_type=content_type, + file_name=reader.file_name, + file_size=reader.file_size, + dims=dims, + ) + return self.last_file_info + + # Цикл кончился — не хватило данных + self.last_file_info = self._build_file_info( + url=url, + mime_type=reader.content_type, + file_name=reader.file_name, + file_size=reader.file_size, + dims=dims or None, + error_desc="Недостаточно данных для определения параметров", + ) + return self.last_file_info + + # ======================================================================== + # Private: хелперы + # ======================================================================== + + @staticmethod + def _looks_like_html(head: bytes, content_type: str) -> bool: + """Определяет, похож ли ответ на HTML-страницу.""" + if content_type.startswith("text/html"): + return True + if len(head) < 16: + return False + head_lower = head[:512].lower().lstrip() + return head_lower.startswith((b" bool: + """Проверяет, достаточно ли данных для этого типа контента.""" + if not dims: + return False + if content_type.startswith("image/"): + return bool(dims.get("width") and dims.get("height")) + if content_type.startswith("audio/"): + return bool(dims.get("duration") and dims.get("sample_rate")) + if content_type.startswith("video/"): + if dims.get("error_desc") == ( + "Для определения duration необходим " + "конец файла (tail) или файл целиком" + ): + return True + return bool( + dims.get("duration") and dims.get("width") and dims.get("fps") + ) + return bool(dims) + + @staticmethod + def _build_file_info( + url: str = "", + mime_type: str = "", + file_name: str = "", + file_size: int | None = None, + dims: dict | None = None, + error_desc: str = "", + ) -> FileInfo: + """Собирает FileInfo из параметров.""" + if dims is None: + dims = {} + + # Вычисляем производные поля + bitrate_avg = None + if file_size and dims.get("duration"): + bitrate_avg = round(file_size / dims["duration"] * 8 / 1024) + + duration = dims.get("duration") + if duration is not None: + duration = round(duration, 1) if duration < 10 else round(duration) + + return FileInfo( + url=url, + mime_type=mime_type, + file_name=file_name, + file_size=file_size, + width=dims.get("width"), + height=dims.get("height"), + format=dims.get("format"), + duration=duration, + fps=dims.get("fps"), + sample_rate=dims.get("sample_rate"), + bitrate_nominal=dims.get("bitrate_nominal") or dims.get("bitrate"), + bitrate_avg=bitrate_avg, + error_desc=error_desc or dims.get("error_desc", ""), + ) + + @classmethod + def parse_media_dimensions( + cls, + head: bytes, + tail: bytes | None, + content_type: str | None = None, + file_size: int | None = None, + ) -> dict | None: + """ + Извлекает метаданные из фрагментов файла. + + Args: + head: Байты с начала файла. + tail: Байты с конца (если нужны для формата). + content_type: MIME-тип, опционально + file_size: Полный размер файла, если известен. + + Returns: + Словарь полей для :class:`FileInfo` или ``None``. + """ + if len(head) < 2: + return None + + if not content_type or content_type == "application/octet-stream": + # Если сервер не определил тип файла + # Проверим все типы по содержанию + result = cls._parse_image_dimensions(head, content_type, file_size) + if result: + return result + result = cls._parse_video_dimensions( + head, tail, content_type, file_size + ) + if result: + return result + result = cls._parse_audio_dimensions( + head, tail, content_type, file_size + ) + if result: + return result + + if content_type.startswith("image/"): + return cls._parse_image_dimensions(head, content_type, file_size) + + if content_type.startswith("video/"): + return cls._parse_video_dimensions( + head, tail, content_type, file_size + ) + + if content_type.startswith("audio/"): + return cls._parse_audio_dimensions( + head, tail, content_type, file_size + ) + + return None + + @classmethod + def _parse_image_dimensions( + cls, data: bytes, content_type: str, file_size: int | None + ) -> dict | None: + """ + Парсит метаданные изображений, определяя формат по сигнатурам, + с fallback на content_type. + + Args: + data: начальные байты файла (head). + content_type: MIME-тип из заголовков HTTP. + file_size: размер файла в байтах (опционально). + + Returns: + dict с ключами format, width, height или None. + """ + + # WEBP — сигнатура RIFF WEBP + if cls._webp_check(data): + return cls._webp_parse(data, file_size) + + # PNG — сигнатура 8 байт + IHDR + if cls._png_check(data): + return cls._png_parse(data) + + # JPEG — сигнатура \xFF\xD8 + if cls._jpg_check(data): + return cls._jpeg_parse(data) + + # GIF — сигнатура GIF87a / GIF89a + if cls._gif_check(data): + return cls._gif_parse_info(data, file_size) + + # Fallback на content_type, если байты не распознаны + # Это полезно, если файл обрезан или сигнатура нестандартна + if content_type == "image/webp": + return cls._webp_parse(data, file_size) + if content_type == "image/png": + return cls._png_parse(data) + if content_type == "image/jpeg": + return cls._jpeg_parse(data) + if content_type == "image/gif": + return cls._gif_parse_info(data, file_size) + + return None + + @classmethod + def _parse_video_dimensions( # noqa: C901 + cls, + head: bytes, + tail: bytes | None, + content_type: str, + file_size: int | None, + ) -> dict | None: + """ + Парсит метаданные видео, определяя формат по сигнатурам, + с fallback на content_type. + + Args: + head: начальные байты файла. + tail: конечные байты файла (опционально, для moov/seekhead). + content_type: MIME-тип из заголовков HTTP. + file_size: размер файла в байтах (опционально). + + Returns: + dict с ключами format, width, height, fps, duration или None. + """ + + # MP4 / MOV — сигнатура ftyp или moov + if cls._mp4_check(head): + return cls._mp4_parse_info(head, tail) + + # AVI — сигнатура RIFF AVI + if cls._avi_check(head): + return cls._avi_parse_info(head) + + # MKV / WebM — сигнатура EBML + if cls._webm_mkv_check(head): + result = cls._webm_mkv_parse_info(head) + # format определяется по содержанию, + # но если есть content_type, берём из него. + if content_type: + if not result: + result = {} + if content_type == "video/webm": + result["format"] = "WEBM" + elif content_type == "video/x-matroska": + result["format"] = "MKV" + return result + + # OGV / OGG — сигнатура OggS + if cls._ogg_ogv_check(head): + return cls._ogg_parse_info(head, tail, file_size) + + # Fallback на content_type, если байты не распознаны + # Это полезно, если файл обрезан или сигнатура нестандартна + if content_type in ("video/mp4", "video/quicktime"): + return cls._mp4_parse_info(head) + if content_type in ("video/x-msvideo", "video/msvideo"): + return cls._avi_parse_info(head) + if content_type == "video/webm": + result = cls._webm_mkv_parse_info(head) + if result: + result["format"] = "WEBM" + return result + if content_type == "video/x-matroska": + result = cls._webm_mkv_parse_info(head) + if result: + result["format"] = "MKV" + return result + if content_type in ("video/ogg", "application/ogg", "video/ogv"): + return cls._ogg_parse_info(head, tail, file_size) + + return None + + @classmethod + def _parse_audio_dimensions( # noqa: C901 + cls, + head: bytes, + tail: bytes | None, + content_type: str, + file_size: int | None, + ) -> dict | None: + """ + Парсит метаданные аудио, определяя формат по сигнатурам, + с fallback на content_type. + + Args: + head: начальные байты файла. + tail: конечные байты файла (опционально, для Vorbis/Opus). + content_type: MIME-тип из заголовков HTTP. + file_size: размер файла в байтах (опционально). + + Returns: + dict с ключами format, duration, sample_rate, bitrate или None. + """ + + if cls._mp3_check(head): + return cls._mp3_parse_info(head, tail, file_size) + + # WAV (RIFF WAVE) + if cls._wav_check(head): + return cls._wav_parse_info(head) + + # FLAC (fLaC) + if cls._flac_check(head): + return cls._flac_parse_info(head) + + # OGG / OPUS / SPEEX (OggS) + if cls._ogg_ogv_check(head): + return cls._ogg_parse_info(head, tail, file_size) + + # AAC (ADTS) + if cls._aac_check(head): + return cls._aac_parse_info(head, file_size) + + # M4A (ftyp) + if cls._m4a_check(head): + return cls._m4a_parse_audio_info(head) + + # WMA / ASF + if cls._wma_check(head): + return cls._wma_parse_info(head) + + # Fallback на content_type, если байты не распознаны + # Это полезно, если файл обрезан или сигнатура нестандартна + if content_type in ("audio/mpeg", "audio/mp3"): + return cls._mp3_parse_info(head, tail, file_size) + if content_type == "audio/mp4": + return cls._m4a_parse_audio_info(head) + if content_type in ("audio/ogg", "application/ogg"): + return cls._ogg_parse_info(head, tail, file_size) + if content_type in ("audio/aac", "audio/x-aac"): + return cls._aac_parse_info(head, file_size) + if content_type in ("audio/wav", "audio/x-wav", "audio/wave"): + return cls._wav_parse_info(head) + if content_type in ("audio/x-ms-wma", "audio/wma"): + return cls._wma_parse_info(head) + + return None + + # ========================================================================= + # [ ] Методы определения формата по сигнатуре + # ========================================================================= + + # --- image --- + @staticmethod + def _png_check(data: bytes) -> bool: + return ( + len(data) >= 24 + and data[:8] == b"\x89PNG\r\n\x1a\n" + and data[12:16] == b"IHDR" + ) + + @staticmethod + def _webp_check(data: bytes) -> bool: + return len(data) >= 12 and data[8:12] == b"WEBP" + + @staticmethod + def _jpg_check(data: bytes) -> bool: + return len(data) > 2 and data[:2] == b"\xff\xd8" + + @staticmethod + def _gif_check(data: bytes) -> bool: + return len(data) >= 10 and data[:6] in (b"GIF87a", b"GIF89a") + + # --- audio --- + @staticmethod + def _mp3_check(data: bytes) -> bool: + return data.startswith(b"ID3") or ( # MP3 (ID3v2) + # MP3 (без тегов, начинается с фрейма sync word 0xFFE0) + len(data) >= 2 and data[0] == 0xFF and (data[1] & 0xFE) == 0xE0 + ) + + @staticmethod + def _wav_check(data: bytes) -> bool: + return ( + len(data) >= 12 + and data.startswith(b"RIFF") + and data[8:12] == b"WAVE" + ) + + @staticmethod + def _flac_check(data: bytes) -> bool: + return data.startswith(b"fLaC") + + @staticmethod + def _ogg_ogv_check(data: bytes) -> bool: + # Для Ogg нужно проверить тип потока внутри (Opus, Vorbis и т.д.) + # Обычно это делается через _ogg_parse_info, который смотрит внутрь + return data.startswith(b"OggS") + + @staticmethod + def _aac_check(data: bytes) -> bool: + return len(data) >= 2 and data[0] == 0xFF and (data[1] & 0xF6) == 0xF0 + + @staticmethod + def _m4a_check(data: bytes) -> bool: + """Определяет, является ли MP4-файл аудио (M4A) по бренду.""" + if len(data) >= 12 and data[4:8] == b"ftyp": + brand = data[8:12] + return brand in (b"M4A ", b"M4B ", b"M4P ", b"F4A ", b"F4B ") + return False + + @staticmethod + def _wma_check(data: bytes) -> bool: + return data.startswith( + b"\x30\x26\xb2\x75\x8e\x66\xcf\x11\xa6\xd9\x00\xaa\x00\x62\xce\x6c" + ) + + # --- video --- + @staticmethod + def _mp4_check(data: bytes) -> bool: + """MP4/MOV/M4A — ftyp или сразу moov/mdat (старый QuickTime).""" + if len(data) < 4: + return False + if data[4:8] == b"ftyp": + brand = data[8:12] + # Явно видео бренды или общие (isom может быть и аудио) + # fmt: off + return brand in { + b"mp42", b"isom", b"iso2", b"avc1", b"mp41", b"iso5", + b"iso6", b"msnv", b"ndsc", b"ndsh", b"ndsm", b"ndsp", + b"ndss", b"ndxc", b"ndxh", b"ndxm", b"ndxp", b"ndxs"} + # Старый QuickTime: начинается с moov или mdat + return data[0:4] in (b"moov", b"mdat", b"wide", b"skip", b"free") + + @staticmethod + def _avi_check(data: bytes) -> bool: + return len(data) > 8 and data[0:4] == b"RIFF" and data[8:12] == b"AVI " + + @staticmethod + def _webm_mkv_check(data: bytes) -> bool: + return len(data) > 3 and data[1:4] == b"\x45\xdf\xa3" + + # ========================================================================= + # [ ] Парсеры: изображения + # ========================================================================= + + @classmethod + def _png_parse(cls, data: bytes) -> dict[str, Any] | None: + if not cls._png_check(data): + return + w, h = struct.unpack(">II", data[16:24]) + return {"width": w, "height": h, "format": "PNG"} + + @staticmethod + def _webp_parse( # noqa: C901 + data: bytes, file_size: int | None = None + ) -> dict[str, Any] | None: + """ + Парсит метаданные из WEBP-файла. + + Поддерживает форматы: + - WEBP/VP8X (расширенный, с анимацией/альфа-каналом) + - WEBP/VP8 (стандартный lossy) + - WEBP/VP8L (lossless) + + Args: + data: Начальные байты файла + (рекомендуется ≥ 32 КБ для анимированных) + file_size: Полный размер файла в байтах (опционально, + для апроксимации длительности) + + Returns: + dict с ключами: format, width, height, duration, fps + None, если формат не распознан + """ + # Базовая проверка заголовка RIFF WEBP + if len(data) < 12 or data[0:4] != b"RIFF" or data[8:12] != b"WEBP": + return None + + result: dict[str, Any] = {"format": "WEBP"} + + # Переменные для накопления данных об анимации + total_ms = 0 + frame_count = 0 + + pos = 12 + while pos + 8 <= len(data): + chunk_type = data[pos : pos + 4] + chunk_size = struct.unpack("= payload_start + 10: + # Размеры в VP8X хранятся как (value - 1) + width = ( + int.from_bytes( + data[payload_start + 4 : payload_start + 7], "little" + ) + + 1 + ) + height = ( + int.from_bytes( + data[payload_start + 7 : payload_start + 10], "little" + ) + + 1 + ) + result.update( + {"width": width, "height": height, "format": "WEBP/VP8X"} + ) + + # 2. ANMF: Кадр анимации (только если чанк полный) + elif ( + chunk_type == b"ANMF" + and is_chunk_complete + and chunk_size >= 16 + ): + frame_count += 1 + # Длительность кадра: + # 3 байта little-endian по смещению 12 от начала payload + dur_offset = payload_start + 12 + duration_raw = int.from_bytes( + data[dur_offset : dur_offset + 3], "little" + ) + + # Конвертация длительности WEBP (ms * 1000 / 1001) обратно в мс + # Если 0, то по спецификации это часто + # интерпретируется как 100мс + duration_ms = ( + round(duration_raw * 1001 / 1000) + if duration_raw > 0 + else 100 + ) + total_ms += duration_ms + + # 3. VP8 : Lossy изображение (первый кадр или статика) + elif chunk_type == b"VP8 " and len(data) >= payload_start + 30: + # Проверяем, что это ключевой кадр (frame tag) + # Frame tag находится в начале payload. + # Если бит 0 равен 0, это key frame. + if payload_start + 10 <= len(data): + frame_tag = data[payload_start] + if (frame_tag & 0x01) == 0: + width = ( + struct.unpack( + "= 5 + ): + if payload_start + 4 <= len(data): + bits = struct.unpack( + "> 14) & 0x3FFF) + 1 + if "width" not in result: + result.update( + { + "width": width, + "height": height, + "format": "WEBP/VP8L", + } + ) + + # Переход к следующему чанку (выравнивание по четному байту) + next_pos = payload_end + if next_pos % 2 != 0: + next_pos += 1 + + # Защита от бесконечного цикла при битых данных + if next_pos <= pos: + break + pos = next_pos + + # Если размеры не найдены, файл невалиден для наших целей + if "width" not in result or "height" not in result: + return result + + # --- Расчет длительности и FPS --- + if frame_count > 0: + result["frames"] = frame_count + scanned_duration_sec = total_ms / 1000.0 + + # Экстраполяция, если файл обрезан (data < file_size) + if file_size and file_size > len(data) and len(data) > 0: + ratio = file_size / len(data) + # Предполагаем равномерное распределение данных + estimated_duration_sec = scanned_duration_sec * ratio + result["duration"] = estimated_duration_sec + if estimated_duration_sec > 0: + result["fps"] = round( + (frame_count * ratio) / estimated_duration_sec, 3 + ) + result["error_desc"] = ( + "Длительность и частота кадров определены " + "экстраполирвоанием (приблизительно)" + ) + else: + result["duration"] = scanned_duration_sec + if scanned_duration_sec > 0: + result["fps"] = round( + frame_count / scanned_duration_sec, 3 + ) + + return result + + @staticmethod + def _gif_parse_info( + data: bytes, file_size: int | None = None + ) -> dict[str, Any]: + """ + Извлекает метаданные из GIF по заголовку. + + Аргументы: + data: Первые байты файла (рекомендуется ≥ 20 КБ). + file_size: Полный размер файла. + + Возвращает: + dict с 'format', 'width', 'height', 'bitrate' или None. + """ + if len(data) < 10 or data[:6] not in (b"GIF87a", b"GIF89a"): + return {} + + # Логические размеры изображения + width, height = struct.unpack(" len(data): + break + delay_cs = struct.unpack(" 0: + # Длительность по просканированным кадрам + scanned_duration_sec = total_cs / 100.0 + + data_size = len(data) + # Если известен полный размер файла — экстраполируем + if file_size and file_size > data_size: + ratio = file_size / data_size + result["duration"] = scanned_duration_sec * ratio + result["error_desc"] = ( + "Длительность и частота кадров определены " + "экстраполирвоанием (приблизительно)" + ) + else: + # Файл маленький или размер неизвестен — возвращаем как есть + result["duration"] = scanned_duration_sec + + # FPS считаем по просканированным кадрам + if scanned_duration_sec > 0: + result["fps"] = round(frame_count / scanned_duration_sec, 3) + + return result + + @staticmethod + def _jpeg_parse(data: bytes) -> dict | None: + if len(data) < 2 or data[:2] != b"\xff\xd8": + return None + + pos = 2 + while pos < len(data) - 1: + if data[pos] != 0xFF: + pos += 1 + continue + marker = data[pos + 1] + if ( + marker in (0xC0, 0xC1, 0xC2) # SOF0, SOF1, SOF2 + and pos + 9 <= len(data) + ): + h, w = struct.unpack(">HH", data[pos + 5 : pos + 9]) + return {"width": w, "height": h, "format": "JPEG"} + # Пропускаем сегменты + if marker not in (1, *tuple(range(208, 218))): + if pos + 4 <= len(data): + segment_length = struct.unpack( + ">H", data[pos + 2 : pos + 4] + )[0] + pos += 2 + segment_length + else: + break + else: + pos += 2 + return None + + # ========================================================================= + # [ ] Парсеры: видео + # ========================================================================= + + @classmethod + def _mp4_parse_info( + cls, data: bytes, tail: bytes | None = None + ) -> dict | None: + """ + Парсит размеры и длительность из MP4/MOV файла. + + Ищет атом moov в head и tail. Для файлов с moov в конце + (потоковая запись) нужен tail. + """ + result = None + if cls._mp4_check(data): + result = {"format": "MP4"} + + # Ищем moov в head + dims = cls._mp4_find_moov(data) + if dims and result: + result.update(dims) + return result + + # Ищем moov в tail + if tail: + dims = cls._mp4_find_moov(tail) + if dims and result: + result.update(dims) + return result + + return result + + @classmethod + def _mp4_find_moov(cls, data: bytes) -> dict | None: + """Ищет атом moov в данных и парсит его.""" + idx = data.find(b"moov") + if idx < 4: + return None + + # Размер атома перед moov + size = struct.unpack(">I", data[idx - 4 : idx])[0] + + # Берём moov — либо полный, либо до конца данных + moov_start = idx - 4 + moov_end = min(moov_start + size, len(data)) + moov_data = data[moov_start + 8 : moov_end] # +8 пропускаем size+type + + return cls._mp4_moov_parse(moov_data) + + @classmethod + def _mp4_moov_parse(cls, data: bytes) -> dict | None: + result: dict[str, int | float | str] = {} + pos = 0 + while pos + 8 <= len(data): + size = struct.unpack(">I", data[pos : pos + 4])[0] + atom_type = data[pos + 4 : pos + 8] + + if size == 0: + break + header_size = 16 if size == 1 else 8 + if size == 1: + if pos + 16 > len(data): + break + size = struct.unpack(">Q", data[pos + 8 : pos + 16])[0] + + if atom_type == b"mvhd": + duration = cls._m4a_parse_mvhd_duration(data[pos : pos + size]) + if duration is not None: + result["duration"] = duration + result["format"] = "MP4" + elif atom_type == b"trak": + trak_data = data[pos + header_size : pos + size] + dims = cls._mp4_parse_trak_for_dims(trak_data) + if dims and cls._mp4_valid_video_dims(dims): + result.update(dims) + + pos += size + if size < header_size: + break + return result or None + + @classmethod + def _mp4_parse_trak_for_dims(cls, data: bytes) -> dict | None: + """Ищет tkhd внутри trak""" + pos = 0 + while pos + 8 <= len(data): + size = struct.unpack(">I", data[pos : pos + 4])[0] + atom_type = data[pos + 4 : pos + 8] + + if size == 0: + break + if size == 1: + if pos + 16 > len(data): + break + size = struct.unpack(">Q", data[pos + 8 : pos + 16])[0] + header_size = 16 + else: + header_size = 8 + + if atom_type == b"tkhd": + return cls._mp4_parse_tkhd( + data[pos + header_size : pos + size] + ) + + pos += size + if size < header_size: + break + return None + + @staticmethod + def _mp4_parse_tkhd(data: bytes) -> dict | None: + """Парсит tkhd atom. Версии 0 и 1.""" + if len(data) < 80: # Минимальный размер для версии 0 + return None + + version = data[0] + # flags = data[1:4] + + if version == 0: + # creation_time (4), modification_time (4), track_id (4), + # reserved (4), duration (4), reserved (8), layer (2), + # alternate_group (2), volume (2), reserved(2), matrix (36) + # width (4), height (4) + # Смещение до width: 4 bytes version/flags + 72 bytes полей = 76 + if len(data) < 84: + return None + w_fixed = struct.unpack(">I", data[76:80])[0] + h_fixed = struct.unpack(">I", data[80:84])[0] + # Значения в формате 16.16 fixed point + return { + "width": w_fixed >> 16, + "height": h_fixed >> 16, + "format": "MP4", + } + elif version == 1: + # creation_time (8), modification_time (8), track_id (4), + # reserved (4), duration (8), reserved (8), layer (2), + # alternate_group(2), volume(2), reserved (2), matrix (36), + # width (4), height (4) + # Смещение c учетом version/flags: 92 байта + if len(data) < 100: + return None + w_fixed = struct.unpack(">I", data[92:96])[0] + h_fixed = struct.unpack(">I", data[96:100])[0] + return { + "width": w_fixed >> 16, + "height": h_fixed >> 16, + "format": "MP4", + } + return None + + @staticmethod + def _mp4_valid_video_dims(result: dict | None) -> bool: + """Проверяет, что размеры видео реалистичны.""" + if not result: + return False + w = result.get("width") or 0 + h = result.get("height") or 0 + return ( + # Некоторые файлы имеют 0x40000000 (16384.0) как "не задано" + not (w == 16_384 and h == 16_384) + # Минимальное видео — хотя бы 2×2 + and not (w < 2 or h < 2) + # 16K с запасом на будущее + and not (w > 16_384 or h > 16_384) + ) + + @classmethod + def _avi_parse_info(cls, data: bytes) -> dict | None: + """ + Парсит AVI файл и возвращает словарь с метаинформацией. + + Извлекает параметры видео (ширина, высота, FPS, длительность) + и аудио (частота дискретизации, битрейт) из RIFF AVI структуры. + + Примечание о битрейте: + Формат AVI (особенно с кодеками типа M-JPEG, DivX, Xvid) обычно + использует переменный битрейт (VBR) или не хранит точное значение + среднего битрейта в заголовке. Поле dwMaxBytesPerSec в главном + заголовке часто содержит лишь приблизительную оценку или пиковое + значение, которое не отражает реальный размер файла. + + Args: + data: сырые байты начала файла (минимум 56 байт для avih) + + Returns: + dict с ключами width, height, fps, duration, sample_rate, + bitrate, format + или None если файл не является корректным AVI + """ + if len(data) < 12: + return None + + # Проверка сигнатуры RIFF AVI + if data[0:4] != b"RIFF" or data[8:12] != b"AVI ": + return None + + result = { + "width": None, + "height": None, + "fps": None, + "sample_rate": None, + "duration": None, + "bitrate": None, # Номинальный (из заголовка avih) + "format": "AVI", + "total_frames": None, + "file_size": len(data), # Размер данных, которые у нас есть + } + + pos = 12 + end = len(data) + + while pos + 8 <= end: + chunk_id = data[pos : pos + 4] + chunk_size = struct.unpack_from(" end - pos - 8: + break + + next_pos = pos + 8 + chunk_size + if chunk_size % 2 != 0: + next_pos += 1 + + if ( + chunk_id == b"LIST" + and pos + 12 <= end + and data[pos + 8 : pos + 12] == b"hdrl" + ): + cls._parse_hdrl(data, pos + 12, pos + 8 + chunk_size, result) + break # hdrl всегда первый, дальше можно не искать + + if next_pos <= pos: + break + pos = next_pos + + # Вычисляем длительность + if result["total_frames"] and result["fps"]: + result["duration"] = int(result["total_frames"] / result["fps"]) + + # Удаляем служебные ключи + result.pop("total_frames", None) + result.pop("file_size", None) + + return result + + @classmethod + def _parse_hdrl(cls, data: bytes, start: int, end: int, result: dict): + """ + Парсит LIST hdrl, извлекает avih и потоки strl. + + Args: + data: байты данных + start, end: границы чанка hdrl + result: словарь для заполнения (изменяется in-place) + """ + pos = start + while pos + 8 <= end: + chunk_id = data[pos : pos + 4] + chunk_size = struct.unpack_from(" end - pos - 8: + break + + next_pos = pos + 8 + chunk_size + if chunk_size % 2 != 0: + next_pos += 1 + + chunk_end = min(pos + 8 + chunk_size, end) + + if chunk_id == b"avih": + cls._parse_avih(data, pos + 8, chunk_end, result) + elif ( + chunk_id == b"LIST" + and pos + 12 <= end + and data[pos + 8 : pos + 12] == b"strl" + ): + cls._parse_strl(data, pos + 12, chunk_end, result) + + if next_pos <= pos: + break + pos = next_pos + + @staticmethod + def _parse_avih(data: bytes, start: int, end: int, result: dict): + """ + Парсит MainAVIHeader (56 байт). + + Структура: + - dwMicroSecPerFrame (0-3): микросекунд на кадр + - dwMaxBytesPerSec (4-7): ОБЩИЙ максимальн. битрейт файла (видео+аудио) + - dwPaddingGranularity (8-11): выравнивание + - dwFlags (12-15): флаги + - dwTotalFrames (16-19): общее количество кадров + - dwInitialFrames (20-23): начальные кадры + - dwStreams (24-27): количество потоков + - dwSuggestedBufferSize (28-31): рекомендуемый размер буфера + - dwWidth (32-35): ширина + - dwHeight (36-39): высота + - dwReserved[4] (40-55): зарезервировано + """ + if end - start < 56: + return + + # Номинальный битрейт - ОБЩИЙ для всего файла + max_bytes_per_sec = struct.unpack_from(" 0: + result["bitrate"] = int((max_bytes_per_sec * 8) / 1000) + + # Общее количество кадров + result["total_frames"] = struct.unpack_from(" 0: + result["width"] = width + if height > 0: + result["height"] = height + + @classmethod + def _parse_strl(cls, data: bytes, start: int, end: int, result: dict): + """ + Парсит LIST strl, ищет strh (заголовок потока) и strf (формат потока). + """ + pos = start + while pos + 8 <= end: + chunk_id = data[pos : pos + 4] + chunk_size = struct.unpack_from(" end - pos - 8: + break + + next_pos = pos + 8 + chunk_size + if chunk_size % 2 != 0: + next_pos += 1 + + chunk_end = min(pos + 8 + chunk_size, end) + + if chunk_id == b"strh": + cls._parse_strh(data, pos + 8, chunk_end, result) + elif chunk_id == b"strf": + cls._parse_strf(data, pos + 8, chunk_end, result) + + if next_pos <= pos: + break + pos = next_pos + + @staticmethod + def _parse_strh(data: bytes, start: int, end: int, result: dict): + """ + Парсит AVISTREAMHEADER (56 байт). + + Для видео (vids): + - dwRate/dwScale = FPS + - rcFrame = размер кадра + + Для аудио (auds): + - dwRate = частота дискретизации (предварительно) + - Более точная частота будет в strf (nSamplesPerSec) + """ + if end - start < 56: + return + + stream_type = data[start : start + 4] + dw_scale = struct.unpack_from(" 0: + result["width"] = w + if h > 0: + result["height"] = h + + # FPS = dwRate / dwScale + if dw_scale > 0 and dw_rate > 0: + result["fps"] = round(dw_rate / dw_scale, 2) + + elif stream_type == b"auds": + # Предварительная частота из strh (может быть неточной) + # Приоритет будет у strf (nSamplesPerSec) + if result["sample_rate"] is None and dw_rate > 0: + # Проверяем, похоже ли на стандартную частоту + # fmt: off + standard_rates = { + 8000, 11025, 12000, 16000, 22050, 24000, + 32000, 44100, 48000, 88200, 96000, 192000, + } + # fmt: on + if dw_rate in standard_rates: + result["sample_rate"] = dw_rate + + @staticmethod + def _parse_strf(data: bytes, start: int, end: int, result: dict): + """ + Парсит WAVEFORMATEX (аудио) или BITMAPINFOHEADER (видео). + + Для аудио WAVEFORMATEX (минимум 16 байт): + - wFormatTag (0-1): тип формата + - nChannels (2-3): количество каналов + - nSamplesPerSec (4-7): ЧАСТОТА ДИСКРЕТИЗАЦИИ наиболее точное значение + - nAvgBytesPerSec (8-11): средний байтрейт ТОЛЬКО аудио потока + - nBlockAlign (12-13): выравнивание блока + - wBitsPerSample (14-15): бит на сэмпл (для PCM) + """ + if end - start < 16: + return + + format_tag = struct.unpack_from(" 0: + result["sample_rate"] = sample_rate + + @classmethod + def _webm_mkv_parse_info(cls, data: bytes) -> dict | None: # noqa: C901 + """Парсит размеры и длительность из MKV/WebM файла.""" + if len(data) < 4: + return None + + width = cls._webm_read_ebml_uint(data, b"\xb0") # PixelWidth + height = cls._webm_read_ebml_uint(data, b"\xba") # PixelHeight + duration = cls._webm_read_duration_seconds(data) + + # Видео: fps — пробуем FrameRate, затем DefaultDuration + fps = cls._webm_read_ebml_float(data, b"\x23\x83\xe3") + + # Если FrameRate нет, считаем из DefaultDuration (наносекунды на фрейм) + if fps is None: + default_duration = cls._webm_read_ebml_uint( + data, b"\x23\xe3\x83" + ) # DefaultDuration + if default_duration and default_duration > 0: + fps = 1_000_000_000 / default_duration # Конвертируем в fps + + # Аудио: sample_rate + sample_rate = cls._webm_read_ebml_float(data, b"\xb5") + + # Номинальный битрейт (опционально) + bitrate_nominal_raw = cls._webm_read_ebml_uint(data, b"\x25\x86\x88") + bitrate_nominal: int | None = None + if bitrate_nominal_raw is not None and bitrate_nominal_raw > 0: + bitrate_nominal = bitrate_nominal_raw // 1000 + + doc_type = cls._webm_read_ebml_string(data, b"\x42\x82") + + if doc_type == "webm": + format = "WEBM" + elif doc_type == "matroska": + format = "MKV" + else: + format = cls._webm_ebml_detect_format_by_codecs(data) + + result: dict[str, Any] = {"format": format} + + if width and height: + result["width"] = width + result["height"] = height + if duration is not None: + result["duration"] = duration + if fps is not None and fps > 0: + result["fps"] = round(fps, 3) + if sample_rate is not None and sample_rate > 0: + result["sample_rate"] = round(sample_rate) + result["bitrate_nominal"] = bitrate_nominal + + return result if len(result) > 1 else None + + @staticmethod + def _webm_read_ebml_size_vint( + data: bytes, start_pos: int + ) -> tuple[int, int] | None: + """Читает EBML VINT для size и возвращает (длина_vint, значение).""" + if start_pos >= len(data): + return None + + first = data[start_pos] + mask = 0x80 + vint_len = 1 + while vint_len <= 8 and (first & mask) == 0: + mask >>= 1 + vint_len += 1 + + if vint_len > 8 or start_pos + vint_len > len(data): + return None + + value = first & (mask - 1) + for i in range(1, vint_len): + value = (value << 8) | data[start_pos + i] + + return vint_len, value + + @classmethod + def _webm_read_ebml_element_value( + cls, data: bytes, element_id: bytes, parser: Callable + ) -> Any: + """ + Универсальный поиск и чтение EBML элемента. + + Args: + data: сырые байты. + element_id: ID элемента (bytes). + parser: функция (raw_bytes) -> parsed_value или None. + + Returns: + Распарсенное значение или None если элемент не найден. + """ + pos = 0 + while True: + idx = data.find(element_id, pos) + if idx < 0: + return None + + size_meta = cls._webm_read_ebml_size_vint( + data, idx + len(element_id) + ) + if not size_meta: + pos = idx + 1 + continue + + size_len, value_len = size_meta + value_start = idx + len(element_id) + size_len + value_end = value_start + value_len + if value_len <= 0 or value_end > len(data): + pos = idx + 1 + continue + + result = parser(data[value_start:value_end]) + + if result is not None: + return result + pos = idx + 1 + + @classmethod + def _webm_read_ebml_uint( + cls, data: bytes, element_id: bytes + ) -> int | None: + """Ищет uint-элемент по ID и возвращает его value.""" + + def _parse(raw: bytes) -> int | None: + return int.from_bytes(raw, "big") + + return cls._webm_read_ebml_element_value(data, element_id, _parse) + + @classmethod + def _webm_read_ebml_float( + cls, data: bytes, element_id: bytes + ) -> float | None: + """Ищет float-элемент по ID и читает 4/8-байтовое значение.""" + + def _parse(raw: bytes) -> float | None: + if len(raw) == 4: + return struct.unpack(">f", raw)[0] + if len(raw) == 8: + return struct.unpack(">d", raw)[0] + return None + + return cls._webm_read_ebml_element_value(data, element_id, _parse) + + @classmethod + def _webm_read_ebml_string( + cls, data: bytes, element_id: bytes + ) -> str | None: + """Читает строковый элемент.""" + + def _parse(raw: bytes) -> str | None: + try: + return raw.decode("ascii") + except UnicodeDecodeError: + return None + + return cls._webm_read_ebml_element_value(data, element_id, _parse) + + @staticmethod + def _webm_ebml_detect_format_by_codecs(data: bytes) -> str | None: + """ + Если DocType не найден, определяем формат по кодекам. + + WebM поддерживает только: + - Видео: V_VP8, V_VP9, V_AV1 + - Аудио: A_VORBIS, A_OPUS + + Всё остальное — MKV. + """ + if len(data) < 8 or data[:4] != b"\x1a\x45\xdf\xa3": + return None + + # Всё, что не WebM → MKV + webm_codecs = (b"V_VP8", b"V_VP9", b"V_AV1", b"A_VORBIS", b"A_OPUS") + + # Ищем любое упоминание кодеков + for i in range(len(data) - 6): + chunk = data[i : i + 6] + if ( + chunk.startswith((b"V_", b"A_", b"S_")) + and chunk not in webm_codecs + ): + return "MKV" + + return "WEBM" + + @classmethod + def _webm_read_duration_seconds(cls, data: bytes) -> int | None: + """Считывает WebM duration (сек) из Duration + TimecodeScale.""" + duration_raw = cls._webm_read_ebml_float(data, b"\x44\x89") # Duration + if duration_raw is None: + return None + + timecode_scale = cls._webm_read_ebml_uint( + data, b"\x2a\xd7\xb1" + ) # TimecodeScale + if not timecode_scale: + timecode_scale = 1_000_000 # default 1ms + + # Duration в единицах TimecodeScale, конвертируем в секунды + return round(duration_raw * (timecode_scale / 1_000_000_000)) + + # ========================================================================= + # [ ] Парсеры: аудио + # ========================================================================= + + @classmethod + def _mp3_parse_info( # noqa: C901 + cls, + head: bytes, + tail: bytes | None = None, + file_size: int | None = None, + ) -> dict[str, Any] | None: + """ + Извлекает метаданные из MP3 по заголовку (начало или конец файла). + + Args: + head: начальные байты файла (рекомендуется ≥32 КБ). + tail: последние байты файла (для поиска Xing/VBRI/ID3v1). + file_size: полный размер файла. + + Returns: + dict с 'format', 'duration', 'sample_rate', 'bitrate' или None. + """ + if len(head) < 4: + return None + + result: dict[str, Any] = {"format": "MP3"} + sample_rate: int | None = None + bitrate: int | None = None + duration: int | None = None + + if file_size and len(head) >= file_size: + # Весь файл в head + tail = head[-8192:] # Берём конец head как tail + + # === Поиск фрейма: сначала в head, затем в tail === + frame_data = None + frame_pos = None + tag_size = None + + for data in (head, tail): + if not data: + continue + + start = 0 + # Пропускаем ID3v2 только в head + if data is head and len(data) >= 10 and data[:3] == b"ID3": + tag_size = ( + ((data[6] & 0x7F) << 21) + | ((data[7] & 0x7F) << 14) + | ((data[8] & 0x7F) << 7) + | (data[9] & 0x7F) + ) + start = 10 + tag_size + + pos = cls._mp3_find_frame_header(data, start) + if pos is not None: + frame_data = data + frame_pos = pos + break + + if frame_pos is None: + return result + + # Парсим заголовок фрейма (big-endian) + frame_data = cast(bytes, frame_data) + header = struct.unpack(">I", frame_data[frame_pos : frame_pos + 4])[0] + + version_id = (header >> 19) & 0b11 + layer = (header >> 17) & 0b11 + bitrate_idx = (header >> 12) & 0b1111 + sample_rate_idx = (header >> 10) & 0b11 + + # Извлекаем bitrate + br_table = cls._mp3_bitrate_table(version_id, layer) + if 0 <= bitrate_idx < len(br_table) and br_table[bitrate_idx]: + bitrate = br_table[bitrate_idx] + + # Извлекаем sample_rate + mp3_sample_rate_table = { + 0b11: [44100, 48000, 32000, None], # MPEG-1 + 0b10: [22050, 24000, 16000, None], # MPEG-2 + 0b00: [11025, 12000, 8000, None], # MPEG-2.5 + } + sr_table = mp3_sample_rate_table.get(version_id) or [None] * 4 + if 0 <= sample_rate_idx < len(sr_table) and sr_table[sample_rate_idx]: + sample_rate = sr_table[sample_rate_idx] + + # Xing/VBRI — сначала там где нашли фрейм, затем в других данных + xing_info = cls._mp3_parse_xing_vbri(frame_data, frame_pos) + if not xing_info: + other = tail if frame_data is head else head + if other: + other_pos = cls._mp3_find_frame_header(other, 0) + if other_pos is not None: + xing_info = cls._mp3_parse_xing_vbri(other, other_pos) + + if xing_info and "duration_ms" in xing_info: + duration = round(xing_info["duration_ms"] / 1000) + elif xing_info and "frames" in xing_info and sample_rate: + samples_per_frame = ( + 1152 if layer == 0b01 else (576 if layer == 0b10 else 384) + ) + total_samples = xing_info["frames"] * samples_per_frame + duration = round(total_samples / sample_rate) + elif file_size and bitrate: + # Fallback для CBR: оценка по размеру + audio_size = file_size + # Вычитаем ID3v2 (в head) + if tag_size: + audio_size -= 10 + tag_size + # Вычитаем ID3v1 (в tail) + if tail and len(tail) >= 128 and tail[-128:-124] == b"TAG": + audio_size -= 128 + if audio_size > 0: + duration = round((audio_size * 8) / (bitrate * 1000)) + if not (1 <= duration <= 24 * 3600): + duration = None + + if bitrate: + result["bitrate"] = bitrate + if sample_rate: + result["sample_rate"] = sample_rate + if duration: + result["duration"] = duration + + return result if len(result) > 1 else None + + @staticmethod + def _mp3_find_frame_header(data: bytes, start: int) -> int | None: + """Поиск заголовка первого аудио-фрейма после пропуска тегов.""" + for i in range(max(0, start), min(len(data) - 4, start + 65536)): + if data[i] == 0xFF and (data[i + 1] & 0xE0) == 0xE0: + # Проверка валидности версии и слоя + version = (data[i + 1] >> 3) & 0x03 + layer = (data[i + 1] >> 1) & 0x03 + bitrate_idx = (data[i + 2] >> 4) & 0x0F + sample_rate_idx = (data[i + 2] >> 2) & 0x03 + # Отбрасываем зарезервированные значения + if ( + version != 0b01 + and layer != 0b00 + and bitrate_idx != 0b1111 + and sample_rate_idx != 0b11 + ): + return i + return None + + @staticmethod + def _mp3_bitrate_table(version_id: int, layer: int) -> list[int | None]: + """Таблицы битрейтов по спецификации MPEG.""" + # fmt: off + if version_id == 0b11 and layer == 0b01: # MPEG-1 Layer III + return [ + None, + 32, 40, 48, 56, 64, 80, 96, 112, 128, 160, 192, 224, 256, 320, + None, + ] + elif version_id != 0b11 and layer == 0b01: # MPEG-2/2.5 Layer III + return [ + None, + 8, 16, 24, 32, 40, 48, 56, 64, 80, 96, 112, 128, 144, 160, + None, + ] + elif version_id == 0b11 and layer == 0b10: # MPEG-1 Layer II + return [ + None, + 32, 48, 56, 64, 80, 96, 112, 128, 160, 192, 224, 256, 320, 384, + None, + ] + elif version_id != 0b11 and layer == 0b10: # MPEG-2/2.5 Layer II + return [ + None, + 8, 16, 24, 32, 40, 48, 56, 64, 80, 96, 112, 128, 144, 160, + None, + ] + elif version_id == 0b11 and layer == 0b11: # MPEG-1 Layer I + return [ + None, + 32, 64, 96, 128, 160, 192, 224, 256,288, 320, 352, 384,416,448, + None, + ] + elif version_id != 0b11 and layer == 0b11: # MPEG-2/2.5 Layer I + return [ + None, + 32, 48, 56, 64, 80, 96, 112, 128, 144, 160, 176, 192, 224, 256, + None, + ] + return [None] * 16 + # fmt: on + + @staticmethod + def _mp3_parse_xing_vbri(data: bytes, frame_pos: int) -> dict | None: + """Парсинг Xing/VBRI заголовка для точной длительности VBR.""" + if frame_pos + 100 > len(data): + return None + + version = (data[frame_pos + 1] >> 3) & 0b11 + offset = 21 if version == 0b11 else 36 + + pos = frame_pos + offset + if pos + 8 <= len(data) and data[pos : pos + 4] in (b"Xing", b"Info"): + flags = struct.unpack(">I", data[pos + 4 : pos + 8])[0] + result = {} + if flags & 0x01 and pos + 12 <= len(data): # Frames flag + result["frames"] = struct.unpack( + ">I", data[pos + 8 : pos + 12] + )[0] + if flags & 0x02 and pos + 16 <= len(data): # Bytes flag + result["bytes"] = struct.unpack( + ">I", data[pos + 12 : pos + 16] + )[0] + if flags & 0x08 and pos + 20 <= len(data): # Duration flag + result["duration_ms"] = struct.unpack( + ">I", data[pos + 16 : pos + 20] + )[0] + return result or None + + # VBRI на смещении +32 + pos = frame_pos + 32 + if pos + 14 <= len(data) and data[pos : pos + 4] == b"VBRI": + frames = struct.unpack(">I", data[pos + 10 : pos + 14])[0] + if frames > 0: + return {"frames": frames} + + return None + + @classmethod + def _m4a_parse_audio_info(cls, data: bytes) -> dict | None: + result = cls._mp4_parse_info(data) or {} + out = {"format": "M4A"} + if result.get("duration") is not None: + out["duration"] = result["duration"] + return out + + @staticmethod + def _m4a_parse_mvhd_duration(data: bytes) -> int | None: + """Парсит длительность из mvhd атома.""" + if len(data) < 20: + return None + + version = data[8] # Смещение 8 байт от начала атома + + if version == 0: + # timescale (4 bytes) at offset 20 + if len(data) < 24: + return None + timescale = struct.unpack(">I", data[20:24])[0] + # duration (4 bytes) at offset 24 + if len(data) < 28: + return None + duration = struct.unpack(">I", data[24:28])[0] + else: # version 1 + # timescale (4 bytes) at offset 20 + if len(data) < 24: + return None + timescale = struct.unpack(">I", data[20:24])[0] + # duration (8 bytes) at offset 24 + if len(data) < 32: + return None + duration = struct.unpack(">Q", data[24:32])[0] + + if timescale > 0: + return duration / timescale + + return None + + @classmethod + def _ogg_parse_info( # noqa: C901 + cls, + head: bytes, + tail: bytes | None, + file_size: int | None = None, + ) -> dict | None: + """ + Извлекает метаданные из OGG/OGV файла. + + Args: + head: Первые байты файла (заголовок). + tail: Последние байты файла (хвост для поиска гранулы). + content_type: MIME-тип файла (audio/ogg или video/ogg). + + Returns: + dict с ключами format, duration, sample_rate, width, height, fps + или None если формат не распознан. + Без tail возвращает format + размеры/fps/sample_rate без duration. + """ + # 1. Базовая валидация сигнатуры Ogg + if len(head) < 27 or head[:4] != b"OggS": + return None + + # 2. Парсим заголовки всех потоков (Vorbis/Theora) из BOS-страниц + streams = cls._ogg_parse_all_streams(head) + if not streams: + return None + + has_video = any( + s["type"] in ("theora", "vp8", "vp9", "av1") for s in streams + ) + has_audio = any( + s["type"] in ("vorbis", "opus", "flac", "speex") for s in streams + ) + is_audio = has_audio and not has_video + result: dict = {"format": "OGG" if is_audio else "OGV"} + + # Собираем всё что можно без tail + for stream in streams: + if stream["type"] == "vorbis": + if sample_rate := stream.get("sample_rate"): + result["sample_rate"] = sample_rate + elif stream["type"] == "theora": + if w := stream.get("width"): + result["width"] = w + if h := stream.get("height"): + result["height"] = h + fps_num = stream.get("fps_num") + fps_den = stream.get("fps_den") + if fps_num and fps_den and fps_den > 0: + result["fps"] = round(fps_num / fps_den, 3) + + # Если нет tail — возвращаем что есть + if not tail or len(tail) < 27: + if file_size and len(head) >= file_size: + # Весь файл в head — ищем последнюю гранулу в head + tail = head[-8192:] # Берём конец head как tail + else: + result["error_desc"] = ( + "Для определения duration необходим " + "конец файла (tail) или файл целиком" + ) + return result + + # 3. Проходим по каждому найденному потоку для извлечения метаданных + # С tail — пытаемся получить длительность + for stream in streams: + serial = stream.get("serial") + stream_type = stream["type"] + + # Извлекаем финальную гранулу именно для этого типа потока. + granule = cls._ogg_extract_last_granule(tail, serial, stream_type) + if not granule or granule <= 0: + continue + + # --- Аудио-поток (Vorbis) --- + if stream_type == "vorbis": + sample_rate = stream.get("sample_rate") + if sample_rate and sample_rate > 0: + if "sample_rate" not in result: + result["sample_rate"] = sample_rate + duration = granule / sample_rate + if 1 <= duration <= 86400: + result["duration"] = duration + + # --- Видео-поток (Theora) --- + elif stream_type == "theora": + # Считаем длительность из видео, + # только если она ещё не задана аудио + if "duration" not in result: + duration = cls._ogg_calculate_duration(stream, granule) + if duration and 1 <= duration <= 86400: + result["duration"] = duration + + # Добавляем видео-характеристики + if w := stream.get("width"): + result["width"] = w + if h := stream.get("height"): + result["height"] = h + + fps_num = stream.get("fps_num") + fps_den = stream.get("fps_den") + if fps_num and fps_den and fps_den > 0: + result["fps"] = round(fps_num / fps_den, 3) + + # 4. Фоллбэк: если длительность не найдена, + # пытаемся получить её из любого Vorbis-потока + # (Полезно, если serial в хвосте смещён + # или страница с точным serial обрезана) + if "duration" not in result: + for stream in streams: + if stream["type"] == "vorbis" and stream.get("sample_rate"): + granule = cls._ogg_extract_last_granule( + tail, None, "vorbis" + ) + if granule and granule > 0: + dur = granule / stream["sample_rate"] + if 1 <= dur <= 86400: + result["duration"] = dur + if "sample_rate" not in result: + result["sample_rate"] = stream.get( + "sample_rate" + ) + break + + return result if len(result) > 1 else None + + @staticmethod + def _ogg_parse_all_streams(data: bytes) -> list: # noqa: C901 + """ + Извлекает метаданные всех потоков из BOS-страниц Ogg. + + Аргументы: + data: Байты файла (начало или конец). + + Возвращает: + Список словарей с метаданными потоков (Theora/Vorbis), + отсортированный по приоритету (видео > аудио). + """ + pos = 0 + streams: dict[int, dict] = {} + + while pos + 27 <= len(data): + # Ищем сигнатуру страницы Ogg + if data[pos : pos + 4] != b"OggS": + pos += 1 + continue + + header_type = data[pos + 5] + serial = struct.unpack(" len(data): + break + + # Считаем полный размер страницы + page_size = 27 + segments_count + for i in range(segments_count): + page_size += data[seg_table_start + i] + if page_size > len(data): + pos += 27 + segments_count + continue + + # Обрабатываем только BOS-страницы с идентификационными пакетами + if (header_type & 0x02) and page_seq == 0: + payload_start = seg_table_end + if payload_start + 7 <= len(data): + pkt_type = data[payload_start] + + # Theora: 0x80 + "theora" + if ( + pkt_type == 0x80 + and payload_start + 34 <= len(data) + and data[payload_start + 1 : payload_start + 7] + == b"theora" + ): + # Извлекаем размеры (макроблоки → пиксели) + frame_w = struct.unpack( + ">H", data[payload_start + 10 : payload_start + 12] + )[0] + frame_h = struct.unpack( + ">H", data[payload_start + 12 : payload_start + 14] + )[0] + width = frame_w << 4 + height = frame_h << 4 + + # Извлекаем FPS + fps_num = struct.unpack( + ">I", data[payload_start + 22 : payload_start + 26] + )[0] + fps_den = struct.unpack( + ">I", data[payload_start + 26 : payload_start + 30] + )[0] + + # Используем дефолтный shift=6 (наиболее частый случай) + granule_shift = 6 + + # Валидация параметров + if ( + 0 < fps_den <= 10000 + and 0 < fps_num <= 600000 + and 16 <= width <= 8192 + and 16 <= height <= 8192 + ): + streams[serial] = { + "type": "theora", + "serial": serial, + "priority": 0, # видео имеет высший приоритет + "width": width, + "height": height, + "fps_num": fps_num, + "fps_den": fps_den, + "granule_shift": granule_shift, + } + + # Vorbis: 0x01 + "vorbis" + elif ( + pkt_type == 0x01 + and payload_start + 16 <= len(data) + and data[payload_start + 1 : payload_start + 7] + == b"vorbis" + ): + sample_rate = struct.unpack( + "= len(data): + break + + return sorted(streams.values(), key=lambda x: x["priority"]) + + @staticmethod + def _ogg_extract_last_granule( + tail: bytes, + expected_serial: int | None = None, + stream_type: Literal["vorbis", "theora"] | None = None, + ) -> int | None: + """ + Находит последнюю валидную гранулу для указанного serial в хвосте файла + + Аргументы: + tail: Байты хвоста файла. + expected_serial: Ожидаемый serial потока (None = любой). + stream_type: Тип потока для гибкой валидации гранулы. + + Возвращает: + Значение гранулы или None, если не найдено. + """ + pos = tail.rfind(b"OggS") + while pos >= 0: + if pos + 27 <= len(tail): + version = tail[pos + 4] + header_type = tail[pos + 5] + granule = struct.unpack(" 0 + if stream_type == "vorbis": + # Аудио: гранула = сэмплы, макс ~24ч при 192kHz = 16.6 млрд + granule_ok = granule_ok and granule < 16_600_000_000 + else: + # Видео: гранула = кадры, макс ~24ч при 120fps = 10 млн + granule_ok = granule_ok and granule < 10_000_000 + + if ( + page_end <= len(tail) + and (expected_serial is None or serial == expected_serial) + and granule_ok + ): + return granule + pos = tail.rfind(b"OggS", 0, pos) + + return None + + @staticmethod + def _ogg_calculate_duration(stream: dict, granule: int) -> float | None: + """ + Считает длительность в секундах на основе гранулы и параметров потока. + + Аргументы: + stream: Словарь с метаданными потока. + granule: Значение гранулы из последней страницы. + + Возвращает: + Длительность в секундах или None при ошибке. + """ + if stream["type"] == "theora": + fps_num = stream.get("fps_num") + fps_den = stream.get("fps_den") + if not fps_num or not fps_den or fps_den <= 0: + return None + shift = stream.get("granule_shift", 6) + # Декодируем Theora granule: (keyframes << shift) | offset + frames = (granule >> shift) + (granule & ((1 << shift) - 1)) + fps = fps_num / fps_den + if fps <= 0 or fps > 120: + return None + return frames / fps + + elif stream["type"] == "vorbis": + sr = stream.get("sample_rate") + if not sr or sr <= 0: + return None + # Для Vorbis гранула = количество сэмплов + return granule / sr + + return None + + @staticmethod + def _aac_parse_info( # noqa: C901 + data: bytes, file_size: int | None = None + ) -> dict | None: + """ + Оценивает длительность AAC-файла (ADTS) по начальному участку (~2 КБ). + + Args: + data: Первые байты файла (рекомендуется ≥2048). + file_size: Полный размер файла в байтах + (опционально, для экстраполяции) + + Returns: + dict с ключами: + - 'format': 'AAC', + - 'duration': длительность в секундах (int, опционально) + - 'sample_rate': частота дискретизации в Гц (int, опционально) + - 'bitrate': битрейт в kbps + (int, опционально, None если не найден) + или None, если формат не распознан. + """ + pos = 0 + total_samples = 0 + frame_count = 0 + sample_rate = None + bitrate = None + first_frame_pos = None + # fmt: off + aac_sample_rate_table = [ + 96000, 88200, 64000, 48000, 44100, 32000, + 24000, 22050, 16000, 12000, 11025, 8000, 7350, + ] + # Таблица битрейтов для ADTS (MPEG-4 AAC), индекс 0-14, 15=свободный + aac_bitrate_table = [ + None, + 8000, 16000, 24000, 32000, 48000, 64000, 80000, 96000, + 112000, 128000, 160000, 192000, 224000, 256000, 320000, + ] + # fmt: on + # Пропускаем ID3v2-тег, если есть + if data[:3] == b"ID3" and len(data) >= 10: + id3_size = ( + ((data[6] & 0x7F) << 21) + | ((data[7] & 0x7F) << 14) + | ((data[8] & 0x7F) << 7) + | (data[9] & 0x7F) + ) + pos = min(id3_size + 10, len(data)) + + while pos + 7 <= len(data): + # Нули в середине восстановленного файла — данных больше нет + if data[pos : pos + 8] == b"\x00" * 8: + break + + # Поиск синхрослова ADTS: 0xFFF (12 бит) + if data[pos] == 0xFF and (data[pos + 1] & 0xF0) == 0xF0: + b1, b2, b3, b4, b5 = ( + data[pos + 1], + data[pos + 2], + data[pos + 3], + data[pos + 4], + data[pos + 5], + ) + + protection_absent = b1 & 0x01 + header_size = 7 if protection_absent else 9 + + # Разбор полей заголовка + profile = (b2 >> 6) & 0x03 + sf_idx = (b2 >> 2) & 0x0F + channel_config = ((b2 & 0x01) << 2) | ((b3 >> 6) & 0x03) + bitrate_idx = (b3 >> 2) & 0x0F # 4 бита битрейт-индекса + + # Валидация: отсев ложных синхрослов + if sf_idx >= 13 or channel_config > 8 or profile > 3: + pos += 1 + continue + + current_sr = ( + aac_sample_rate_table[sf_idx] + if 0 <= sf_idx < len(aac_sample_rate_table) + else None + ) + if not current_sr: + pos += 1 + continue + + # Длина фрейма (13 бит, включает заголовок) + frame_len = ( + ((b3 & 0x03) << 11) | (b4 << 3) | ((b5 >> 5) & 0x07) + ) + + # Базовая валидация длины + if frame_len < header_size or frame_len > 4096: + pos += 1 + continue + + # Эвристика: отсев ложных срабатываний + if (current_sr >= 32000 and frame_len < 90) or ( + current_sr >= 16000 and frame_len < 60 + ): + pos += 1 + continue + + # Фиксируем sample_rate и bitrate из первого валидного фрейма + if sample_rate is None: + sample_rate = current_sr + first_frame_pos = pos + if bitrate is None and 0 <= bitrate_idx < len( + aac_bitrate_table + ): + br = aac_bitrate_table[bitrate_idx] + if br: + bitrate = br // 1000 # Конвертируем в kbps + + total_samples += 1024 # AAC-LC/Main/SSR: 1024 семпла на фрейм + frame_count += 1 + + if frame_count >= 50: + break + + pos += frame_len + continue + pos += 1 + + result: dict[str, Any] = {"format": "AAC", "bitrate": bitrate} + duration = 0 + if sample_rate and sample_rate > 0: + result["sample_rate"] = sample_rate + # Базовая длительность по найденным фреймам + duration = total_samples / sample_rate + result["duration"] = duration + + if total_samples == 0: + return result + + # 📈 Экстраполяция по полному размеру файла + if frame_count >= 3 and file_size and first_frame_pos is not None: + parsed_bytes = pos - first_frame_pos + if parsed_bytes > 0 and frame_count > 0: + avg_frame_size = parsed_bytes / frame_count + remaining_bytes = file_size - first_frame_pos + estimated_frames = remaining_bytes / avg_frame_size + estimated_samples = estimated_frames * 1024 + if sample_rate: + estimated_duration = estimated_samples / sample_rate + if estimated_duration > duration * 1.3: + result["duration"] = estimated_duration + + return result + + @staticmethod + def _wav_parse_info( # noqa: C901 + data: bytes, total_size: int | None = None + ) -> dict | None: + """ + Извлекает метаданные из WAV-файла по начальному участку заголовков. + + Args: + data: Первые байты файла (заголовок RIFF/WAVE). + total_size: Полный размер файла в байтах (опционально). + + Returns: + dict с ключами: + - 'format': 'WAV', + - 'duration': длительность в секундах (int, опционально) + - 'sample_rate': частота дискретизации в Гц (int, опционально) + - 'bitrate': битрейт в kbps (int | None если не найден) + или None, если файл не является валидным WAV. + """ + if len(data) < 12 or data[:4] != b"RIFF" or data[8:12] != b"WAVE": + return None + + byte_rate = None + data_size = None + data_start = None + sample_rate = None + bitrate = None # По умолчанию None + pos = 12 + limit = len(data) + + while pos + 8 <= limit: + cid = data[pos : pos + 4] + csize = struct.unpack("= 16 and payload + 12 <= limit: + # fmt chunk: offset 4-7 = sample_rate, offset 8-11 = byte_rate + sample_rate = struct.unpack( + " 0: + bitrate = round((byte_rate * 8) / 1000) + elif cid == b"data": + data_size = csize + data_start = payload + + # Переход к следующему чанку с выравниванием + pos = payload + csize + (csize % 2) + + result: dict[str, Any] = {"format": "WAV", "bitrate": bitrate} + if sample_rate and sample_rate > 0: + result["sample_rate"] = sample_rate + if not byte_rate or byte_rate == 0: + return result + + # Определяем размер аудиоданных + audio_bytes = None + if data_size and data_size != 0xFFFFFFFF: + audio_bytes = data_size + elif total_size is not None and data_start is not None: + audio_bytes = total_size - data_start + elif total_size is not None: + audio_bytes = total_size - 44 + + if audio_bytes and audio_bytes > 0: + result["duration"] = audio_bytes / byte_rate + + return result + + @staticmethod + def _wma_parse_info(data: bytes) -> dict[str, Any] | None: # noqa: C901 + """ + Извлекает метаданные из WMA/ASF-файла по заголовку. + + Аргументы: + data: Первые байты файла (рекомендуется ≥8192 для надёжности). + + Возвращает: + dict с ключами: + - 'format': 'WMA' + - 'duration': длительность в секундах (int, опционально) + - 'sample_rate': частота дискретизации в Гц (int, опционально) + - 'bitrate': битрейт в kbps (int | None если не найден) + или None, если файл не является валидным ASF/WMA. + """ + # === GUIDs в little-endian (стандарт ASF) === + asf_header_guid = ( + b"\x30\x26\xb2\x75\x8e\x66\xcf\x11\xa6\xd9\x00\xaa\x00\x62\xce\x6c" + ) + file_props_guid = ( + b"\xa1\xdc\xab\x8c\x47\xa9\xcf\x11\x8e\xe4\x00\xc0\x0c\x20\x53\x65" + ) + stream_props_guid = ( + b"\x91\x07\xdc\xb7\xb7\xa9\xcf\x11\x8e\xe6\x00\xc0\x0c\x20\x53\x65" + ) + audio_stream_guid = ( + b"\x40\x9e\x69\xf8\x4d\x5b\xcf\x11\xa8\xfd\x00\x80\x5f\x5c\x44\x2b" + ) + + wma_format_tags = (0x0161, 0x0162, 0x0163) # WMA v1, v2, Pro + + # Валидация заголовка ASF + if len(data) < 30 or data[:16] != asf_header_guid: + return None + + result: dict[str, Any] = {"format": "WMA"} + sample_rate: int | None = None + duration: int | None = None + bitrate: int | None = None # По умолчанию None + + # === 1️⃣ File Properties Object → длительность === + idx_fp = data.find(file_props_guid) + if idx_fp >= 0 and idx_fp + 88 <= len(data): + play_duration_100ns = struct.unpack( + " 0: + duration = dur + + # === 2️⃣ Stream Properties Object → sample_rate и bitrate === + search_start = 0 + while True: + idx_sp = data.find(stream_props_guid, search_start) + if idx_sp == -1: + break + + if idx_sp + 24 <= len(data): + obj_size = struct.unpack( + " len(data): + search_start = idx_sp + 1 + continue + + stream_type = data[idx_sp + 24 : idx_sp + 40] + if stream_type == audio_stream_guid: + # Сканируем Type-Specific Data на предмет WAVEFORMATEX + for offset in range(60): # Увеличили диапазон поиска + if idx_sp + 72 + offset + 8 > len(data): + break + fmt_tag = struct.unpack( + " 0: + bitrate = round( + (avg_bytes_per_sec * 8) / 1000 + ) + break + if sample_rate or bitrate: + break + + search_start = idx_sp + 1 + + # === Сбор результата === + if duration: + result["duration"] = duration + if sample_rate: + result["sample_rate"] = sample_rate + result["bitrate"] = bitrate # Всегда добавляем, даже если None + + return ( + result + if ( + "duration" in result + or "sample_rate" in result + or bitrate is not None + ) + else None + ) + + @staticmethod + def _flac_parse_info(head: bytes) -> dict | None: + """ + Парсит заголовок FLAC файла для получения sample rate, + длительности и каналов. + + Примечание по битрейту: + FLAC является форматом сжатия без потерь с переменным битрейтом (VBR). + В заголовке FLAC отсутствует поле 'nominal bitrate' (номинальный + битрейт), характерное для форматов с постоянным битрейтом (CBR) или + некоторых других кодеков. + Поэтому битрейт рассчитывается как среднее значение (average bitrate) + по формуле: (Размер файла в битах) / (Длительность в секундах), + если известен полный размер файла (file_size). + + Ищет первый METADATA_BLOCK_HEADER типа STREAMINFO (type 0). + """ + if not head.startswith(b"fLaC"): + return None + + pos = 4 + while pos + 4 <= len(head): + # Читаем заголовок метаданных (4 байта) + # Byte 0: Bit 7 = Last Block Flag, Bits 0-6 = Block Type + header_byte = head[pos] + is_last = (header_byte & 0x80) == 0x80 + block_type = header_byte & 0x7F + + # Bytes 1-3: Length of metadata block data + block_size = int.from_bytes( + head[pos + 1 : pos + 4], byteorder="big" + ) + + pos += 4 + + if block_size > len(head) - pos: + # Если данных недостаточно, прерываемся + break + + if block_type == 0: # STREAMINFO + if block_size < 34: + return None + + streaminfo_data = head[pos : pos + 34] + + # Парсим 8 байт с середины STREAMINFO + # (смещение 10 от начала блока данных) + # Структура этих 8 байт: + # 20 bits: Sample Rate + # 3 bits: Channels - 1 + # 5 bits: Bits Per Sample - 1 + # 36 bits: Total Samples + + # Объединяем 8 байт в одно большое число для удобного сдвига + # битов или парсим побайтово. Давайте побайтово для наглядности + + # Байты 10-12 содержат начало Sample Rate и часть Channels + # Байты 12-13 содержат конец Channels + # и Bits Per Sample и начало Total Samples + + # Проще всего взять 8 байт + # начиная с offset 10 внутри блока STREAMINFO + # Смещение в потоке данных: pos + 10 + raw_8_bytes = streaminfo_data[10:18] + val = int.from_bytes(raw_8_bytes, byteorder="big") + + # Sample Rate: первые 20 бит + sample_rate = val >> 44 + + # Channels: следующие 3 бита + channels_code = (val >> 41) & 0x07 + channels = channels_code + 1 + + # Bits Per Sample: следующие 5 бит + # bits_per_sample_code = (val >> 36) & 0x1F + # bits_per_sample = bits_per_sample_code + 1 + + # Total Samples: последние 36 бит + total_samples = val & ((1 << 36) - 1) + + duration = None + if sample_rate > 0 and total_samples > 0: + duration = total_samples / sample_rate + + return { + "sample_rate": sample_rate, + "channels": channels, + "duration": duration, + "format": "FLAC", + } + + # Переходим к следующему блоку + pos += block_size + + if is_last: + break + + return None diff --git a/tests/test_utils/fileinfo/__init__.py b/tests/test_utils/fileinfo/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_utils/fileinfo/fixtures.json b/tests/test_utils/fileinfo/fixtures.json new file mode 100644 index 00000000..ed61d54f --- /dev/null +++ b/tests/test_utils/fileinfo/fixtures.json @@ -0,0 +1,345 @@ +{ + "png_transparency": { + "head_b64": "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", + "url": "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png", + "mime_type": "image/png", + "file_size": 224566, + "width": 800, + "height": 600, + "format": "PNG" + }, + "jpeg_camponotus": { + "head_b64": "/9j/4AAQSkZJRgABAgEAZQBlAAD/4RA+RXhpZgAASUkqAAgAAAAKAA8BAgAGAAAAhgAAABABAgAXAAAAjAAAABIBAwABAAAAAQAAABoBBQABAAAAowAAABsBBQABAAAAqwAAACgBAwABAAAAAgAAADEBAgAcAAAAswAAADIBAgAUAAAAzwAAABMCAwABAAAAAgAAAGmHBAABAAAA5AAAAMADAABDYW5vbgBDYW5vbiBFT1MgNDAwRCBESUdJVEFMAPB4DwAQJwAA8HgPABAnAABBZG9iZSBQaG90b3Nob3AgQ1MzIFdpbmRvd3MAMjAwOTowMToyNyAxNToyOTowOAAAGwCaggUAAQAAAC4CAACdggUAAQAAADYCAAAiiAMAAQAAAAEAAAAniAMAAQAAAJABAAAAkAcABAAAADAyMjEDkAIAFAAAAD4CAAAEkAIAFAAAAFICAAABkQcABAAAAAECAwABkgoAAQAAAGYCAAACkgUAAQAAAG4CAAAEkgoAAQAAAHYCAAAHkgMAAQAAAAUAAAAJkgMAAQAAAEkAAAAKkgUAAQAAAH4CAACGkgcACAEAAIYCAAAAoAcABAAAADAxMDABoAMAAQAAAAEAAAACoAQAAQAAAAgHAAADoAQAAQAAALAEAAAFoAQAAQAAAKADAAAOogUAAQAAAI4DAAAPogUAAQAAAJYDAAAQogMAAQAAAAIAAAABpAMAAQAAAAAAAAACpAMAAQAAAAEAAAADpAMAAQAAAAAAAAAGpAMAAQAAAAAAAAAAAAAAAQAAAKAAAAALAAAAAQAAADIwMDk6MDE6MTkgMTU6MDg6MjAAMjAwOTowMToxOSAxNTowODoyMABqUgcAAAABADvrBgAAAAEAAAAAAAMAAACWAAAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIBTOwBtAwAAAI0nAEYCAAAAAAIAAQACAAQAAABSOTgAAgAHAAQAAAAwMTAwAAAAAAAABgADAQMAAQAAAAYAAAAaAQUAAQAAAA4EAAAbAQUAAQAAABYEAAAoAQMAAQAAAAIAAAABAgQAAQAAAB4EAAACAgQAAQAAABgMAAAAAAAASAAAAAEAAABIAAAAAQAAAP/Y/+AAEEpGSUYAAQIAAEgASAAA/+0ADEFkb2JlX0NNAAH/7gAOQWRvYmUAZIAAAAAB/9sAhAAMCAgICQgMCQkMEQsKCxEVDwwMDxUYExMVExMYEQwMDAwMDBEMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMAQ0LCw0ODRAODhAUDg4OFBQODg4OFBEMDAwMDBERDAwMDAwMEQwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAz/wAARCABrAKADASIAAhEBAxEB/90ABAAK/8QBPwAAAQUBAQEBAQEAAAAAAAAAAwABAgQFBgcICQoLAQABBQEBAQEBAQAAAAAAAAABAAIDBAUGBwgJCgsQAAEEAQMCBAIFBwYIBQMMMwEAAhEDBCESMQVBUWETInGBMgYUkaGxQiMkFVLBYjM0coLRQwclklPw4fFjczUWorKDJkSTVGRFwqN0NhfSVeJl8rOEw9N14/NGJ5SkhbSVxNTk9KW1xdXl9VZmdoaWprbG1ub2N0dXZ3eHl6e3x9fn9xEAAgIBAgQEAwQFBgcHBgU1AQACEQMhMRIEQVFhcSITBTKBkRShsUIjwVLR8DMkYuFygpJDUxVjczTxJQYWorKDByY1wtJEk1SjF2RFVTZ0ZeLys4TD03Xj80aUpIW0lcTU5PSltcXV5fVWZnaGlqa2xtbm9ic3R1dnd4eXp7fH/9oADAMBAAIRAxEAPwCq6qFWurWk8CFTtAWaHZty7WQUJuhVy5oVVwgp6hJMxydzkIOTOem0m1Ochkpi7VIFGlspMXqtaY5Vh50VTIeSdVJBrZToheVCYUS/VNuU9NXi1bVVsKw3J0grN3wl6pQMV4yN22+e6B6uqAbFDehwrxlp0GXx3RRk+azBYpesU042aPM06YyfNP8AaR4rL9fzSF58UPbX/eg//9CNh0VK12qsWvACoXWarNAda0drlUedUSx8oBMqUBHEyBUHuUxXa4expf3hnuOn8ke5VnWDXy0KQCDNlu1TgqsbdVbb6QxHWNh8ktu3P27GyGse9jW7vdub72PTuG1kpgAlYtkKvbQSj79s1uB9Rtjag0kbjuE7nbhXs/tfTRqmNtbubqPyfFO4THdgMxLZxbaXDsgkEcroLMLd2VG/APgniQYjFzNyaUezFe3gIJreOyctosSmJT7XeCiQUUK3JbkyZJVrlxTtJUE4SpXEX//RyrsjzVG26Sgvvc46IZLz2KpRi6RkzNkpi4IR3+BThlju0BPpZxMX2wZBgjggwVO3PdkVh2cwZI1HrsIGSyP9I7/Dt/8ADDf+upxhOdyEWrpztwMa9ikKWyk0TiPgWVuF1LoixoIgxOyxjvdXY3/tv/R2I2Njvfo0/ozoXRM/8X/3561G9HZaDO5jnCC5h2kj+U36D/7bEV1Y6e+puZ/RbTsGYQGhj/zK8lrAGVsf+ZcjfbdYfHZ57OfbRXXiXU7XtJIcGgh0md1Lm+92/wDwjXrf6ZjOOHi2P13+yzyc6XVh39r9GrXWicHp5Aca33PbU2JB19z9hH/BtXP4/U3ObdVY8VMyG+m/a3idHX1/R2WNanayix6Ql3t6f7DI4QbenA9lvNx62AMYIY0QweQ+imdjgqO2WnlremT2VZ3SfJdc7EaeyicIHsjxFHCHjndJ8kF/SvJdo7AaeyE7pjT2R4yjhDxD+lHwQndMPgu3f0lp7ILuk+SPGVvth4s9OcOxUHYLx2XZnpI8EKzpMdkeNHth/9Ki3ognhHb0ZoHC6IUMCl6TQOFUbhLzh6O3wSHSG+C6L0mcQl6DfBJTg19LA7I7OnNHZa3pAKYrakpzmYYb2RH4dF1L6LmCyqwbbK3cEK+WBQtNVNTr73tppZ9O2whjB/We+GpIeZzfq11B2CenYuRVkYO4GlmZuFuPE/zORU13r1bHOr9K1ixev/V37JbUzDa6zbWG3CIBLWs/S/yPWa7/ALcXV19ebnPNXQ8V/USDDst80YjD/LvsHqXbf9FVX7/zFndU6Vni91ePlDK6rZUMq0kiraKQ/wB1ch7fQ/nK/Ttb/wBxf9HZYpIk3qxzAI0djpN/2rpOHknV1lLN39YD03f9JiD1LreB0+9uGd+V1CwxVgY43Wkn6Is/Mo/t/pP+CXF4n1k65XgN6bg3MawM2tsFe26vXdZ6Vw/fc57vU2+qxdH9SOnUY9N7zROcdptzvUbc2xloNjWUOZ7qPaP1mt/6b/S/6NNMQLv7EiV0B9rrYWTda+zGzKm4+dS1lllLHb2Guz+btqt/wjWva+i3/R31/wDFq2GBU+tMdiCjrNYLj08kZTW8vw7SG5Tf5X2d3p5df/F2LR2Dlrg5pgtcNQQfc1zf5LmppXhGGBS9NpT7SFIOhJTA0NKgcdqsBwTOSU1Tjt8FB2K0q1PiniUlP//T6GAltUgwRpr5pnBzeBI8lUbdqDQmMcJwJE+KmKu/cJKR6+CYcosxpCBkX7IrpAflWA+kx30QBob74+jj1O+l/pv5in9IlSrRZecMd9eNUz7RnXguoxgdvtB2uycmz/tPh1/n3f4T+ao32Kv+wcO+1uR1QnqeQ36HrT9nr/k42FPp7P8Awx69v+kVjAwacOt5Nhvy8gh+Zlv+na8aN49tWPV9DGxa/wBFj1/9uIlmTXVo6UfJHmln2ho0a3RrRoAPBrR7WrD690Pp1rrerEWjqJNNVNrLXN2uLmY1fpsZ/IctNufilxBeB8dEDqOVjuuwKt4DBf8AaLDOgZjsdZJ/666pIWo08t0/6t5OCxuRcfSqt3WCxvtuFbNxaxjnbvs3rMb+a37R/pPQ/m10X1byW3YjnVsbXU4y1rA4gPbNd7bL3/ztvtps9Sz9K/1Fh9ayeoV2BttZOATFTmOLHip259dd9RPpMf8A1P5z/Ce9WPqaRkW2W7XM9GWsa1x9MNc0B59P6Pq2u2+/b/gUZa6rY6Gnq3GstIeA9jgWva76JaRte138lzVndFD8aizplhLm4BDcWx30n4tm5+GXj82yrZbjO/4lXy0umDMcqrbX6fUMa3teyzGefNv63j/+e8pn/XE1e24CW1SbW6JKUEfBJCwYn2p4cmOiSmGxS2aJe7uluICKn//U6IWB2/0/pVgTMtDh/wAHt/kot1z938y2oFoO5sEa8b+W/wBZDJaXDeASNQY/KnF9jSIInT7x9FVW0syLH+pJ9Mz9HUEgj847nf1fapaud7SHNmCZO2f621M+6yyQ8h2sgxqFD2BujnCe4MH8ElInuyrLLGVhlRaTse6XkgH6TW1wx39vZ/1xTxqnVMdua5+Rcd7i+N7yNPzQPZV9Cqtrdlf7inXq7c0tAiNeT8XIoa4s3NguPJI1akpA4tIaLG+nJIBcx4E+G6UnY4eXMaW2bI3Q0kCROzdoiujcRM/yhz96cY1UGHDxI7ye6Smi7Aosduexhg/oyWgHd4OncsrN6U4Zj72l1ddLWl20NHued7m7LNvt9n5q6FuEySWuDSeT3+G791V6sP1crIfa8WbHhzR2AgcN/Oc1JTynWR1LJpZTk4tzfTJdTlMaS7X9/wBAP3s/kK19T3OpGQbg92Xa4B9jmP2itn83W+3bs90+o9dQMe5x3MePgTGn9VTdiXAF5s08iijxa92RZT9Bjb3uBLhXIgj+smyNl1bSSansLbqnOaSG2M9zA+Ppsd/Nv2/mI/p2Rv37idYIn/pJg5zzucARMcQm0m2NOS2xrWktruIBOO9w3tMe6vZo5+2PpIjiY7N11DjB/FMPTFkbGAzJ3CSNNujvb7kzqa3sDXta5rezvc3RFTIamJAga68IbnvfqzUdiBof7SKKqnNL2bWW9nmD/wBHRSc1hkOg7ufcJn+skhCXv42HTnROSIktgeJhNpJcbZd3a8NMabdzC1gTPDazLtjgTq90zu+Ut2pKf//V6ESkawTJT/cn7Kq2mOkJmtE6hSHKl3SUsWSNOEtpAiT5og4S7JKYNc4CB3TNbt1ifipBSPCSkQDy6QiMYfztfNSb8k5SQxFe2duk8pnVE67tFNN27pKRua4CGnTwQ/TMQ4kgcIqSSkBqkRJ+9TY0sbH5VIpklI7K2vd7hKnXUWN2MA2pd0ZnCKms6gTBTtY5ogHTsEZ30kzuElP/2f/tEpBQaG90b3Nob3AgMy4wADhCSU0EBAAAAAAABxwCAAACAAAAOEJJTQQlAAAAAAAQ6PFc8y/BGKGie2etxWTVujhCSU0ELwAAAAAASviPAQBIAAAASAAAAAAAAAAAAAAA0AIAAEACAAAAAAAAAAAAABgDAABkAgAAAAHAAwAAsAQAAAEADycBAGxsdW4AAAAAAAAAAAAAOEJJTQPtAAAAAAAQAGVmZgABAAEAZWZmAAEAAThCSU0EJgAAAAAADgAAAAAAAAAAAAA/gAAAOEJJTQQNAAAAAAAEAAAAHjhCSU0EGQAAAAAABAAAAB44QklNA/MAAAAAAAkAAAAAAAAAAAEAOEJJTQQKAAAAAAABAAA4QklNJxAAAAAAAAoAAQAAAAAAAAACOEJJTQP1AAAAAABIAC9mZgABAGxmZgAGAAAAAAABAC9mZgABAKGZmgAGAAAAAAABADIAAAABAFoAAAAGAAAAAAABADUAAAABAC0AAAAGAAAAAAABOEJJTQP4AAAAAABwAAD/////////////////////////////A+gAAAAA/////////////////////////////wPoAAAAAP////////////////////////////8D6AAAAAD/////////////////////////////A+gAADhCSU0EAAAAAAAAAgAAOEJJTQQCAAAAAAAEAAAAADhCSU0EMAAAAAAAAgEBOEJJTQQtAAAAAAAGAAEAAAABOEJJTQQIAAAAAAAQAAAAAQAAAkAAAAJAAAAAADhCSU0EHgAAAAAABAAAAAA4QklNBBoAAAAAA0UAAAAGAAAAAAAAAAAAAASwAAAHCAAAAAgASQBNAEcAXwAwADAAOQA3AAAAAQAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAcIAAAEsAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAABAAAAABAAAAAAAAbnVsbAAAAAIAAAAGYm91bmRzT2JqYwAAAAEAAAAAAABSY3QxAAAABAAAAABUb3AgbG9uZwAAAAAAAAAATGVmdGxvbmcAAAAAAAAAAEJ0b21sb25nAAAEsAAAAABSZ2h0bG9uZwAABwgAAAAGc2xpY2VzVmxMcwAAAAFPYmpjAAAAAQAAAAAABXNsaWNlAAAAEgAAAAdzbGljZUlEbG9uZwAAAAAAAAAHZ3JvdXBJRGxvbmcAAAAAAAAABm9yaWdpbmVudW0AAAAMRVNsaWNlT3JpZ2luAAAADWF1dG9HZW5lcmF0ZWQAAAAAVHlwZWVudW0AAAAKRVNsaWNlVHlwZQAAAABJbWcgAAAABmJvdW5kc09iamMAAAABAAAAAAAAUmN0MQAAAAQAAAAAVG9wIGxvbmcAAAAAAAAAAExlZnRsb25nAAAAAAAAAABCdG9tbG9uZwAABLAAAAAAUmdodGxvbmcAAAcIAAAAA3VybFRFWFQAAAABAAAAAAAAbnVsbFRFWFQAAAABAAAAAAAATXNnZVRFWFQAAAABAAAAAAAGYWx0VGFnVEVYVAAAAAEAAAAAAA5jZWxsVGV4dElzSFRNTGJvb2wBAAAACGNlbGxUZXh0VEVYVAAAAAEAAAAAAAlob3J6QWxpZ25lbnVtAAAAD0VTbGljZUhvcnpBbGlnbgAAAAdkZWZhdWx0AAAACXZlcnRBbGlnbmVudW0AAAAPRVNsaWNlVmVydEFsaWduAAAAB2RlZmF1bHQAAAALYmdDb2xvclR5cGVlbnVtAAAAEUVTbGljZUJHQ29sb3JUeXBlAAAAAE5vbmUAAAAJdG9wT3V0c2V0bG9uZwAAAAAAAAAKbGVmdE91dHNldGxvbmcAAAAAAAAADGJvdHRvbU91dHNldGxvbmcAAAAAAAAAC3JpZ2h0T3V0c2V0bG9uZwAAAAAAOEJJTQQoAAAAAAAMAAAAAT/wAAAAAAAAOEJJTQQUAAAAAAAEAAAAAzhCSU0EDAAAAAAMNAAAAAEAAACgAAAAawAAAeAAAMigAAAMGAAYAAH/2P/gABBKRklGAAECAABIAEgAAP/tAAxBZG9iZV9DTQAB/+4ADkFkb2JlAGSAAAAAAf/bAIQADAgICAkIDAkJDBELCgsRFQ8MDA8VGBMTFRMTGBEMDAwMDAwRDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAENCwsNDg0QDg4QFA4ODhQUDg4ODhQRDAwMDAwREQwMDAwMDBEMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM/8AAEQgAawCgAwEiAAIRAQMRAf/dAAQACv/EAT8AAAEFAQEBAQEBAAAAAAAAAAMAAQIEBQYHCAkKCwEAAQUBAQEBAQEAAAAAAAAAAQACAwQFBgcICQoLEAABBAEDAgQCBQcGCAUDDDMBAAIRAwQhEjEFQVFhEyJxgTIGFJGhsUIjJBVSwWIzNHKC0UMHJZJT8OHxY3M1FqKygyZEk1RkRcKjdDYX0lXiZfKzhMPTdePzRieUpIW0lcTU5PSltcXV5fVWZnaGlqa2xtbm9jdHV2d3h5ent8fX5/cRAAICAQIEBAMEBQYHBwYFNQEAAhEDITESBEFRYXEiEwUygZEUobFCI8FS0fAzJGLhcoKSQ1MVY3M08SUGFqKygwcmNcLSRJNUoxdkRVU2dGXi8rOEw9N14/NGlKSFtJXE1OT0pbXF1eX1VmZ2hpamtsbW5vYnN0dXZ3eHl6e3x//aAAwDAQACEQMRAD8AquqhVrq1pPAhU7QFmh2bcu1kFCboVcuaFVcIKeoSTMcnc5CDkznptJtTnIZKYu1SBRpbKTF6rWmOVYedFUyHknVSQa2U6IXlQmFEv1TblPTV4tW1VbCsNydIKzd8JeqUDFeMjdtvnugerqgGxQ3ocK8ZadBl8d0UZPmswWKXrFNONmjzNOmMnzT/AGkeKy/X80hefFD21/3oP//QjYdFStdqrFrwAqF1mqzQHWtHa5VHnVEsfKATKlARxMgVB7lMV2uHsaX94Z7jp/JHuVZ1g18tCkAgzZbtU4KrG3VW2+kMR1jYfJLbtz9uxshrHvY1u73bm+9j07htZKYAJWLZCr20Eo+/bNbgfUbY2oNJG47hO524V7P7X00apjbW7m6j8nxTuEx3YDMS2cW2lw7IJBHK6CzC3dlRvwD4J4kGIxczcmlHsxXt4CCa3jsnLaLEpiU+13gokFFCtyW5MmSVa5cU7SVBOEqVxF//0cq7I81RtukoL73OOiGS89iqUYukZMzZKYuCEd/gU4ZY7tAT6WcTF9sGQYI4IMFTtz3ZFYdnMGSNR67CBksj/SO/w7f/AAw3/rqcYTnchFq6c7cDGvYpClspNE4j4FlbhdS6IsaCIMTssY73V2N/7b/0diNjY736NP6M6F0TP/F/9+etRvR2WgzuY5wguYdpI/lN+g/+2xFdWOnvqbmf0W07BmEBoY/8yvJawBlbH/mXI323WHx2eezn20V14l1O17SSHBoIdJndS5vvdv8A8I163+mYzjh4tj9d/ss8nOl1Yd/a/Rq11onB6eQHGt9z21NiQdfc/YR/wbVz+P1Nzm3VWPFTMhvpv2t4nR19f0dljWp2sosekJd7en+wyOEG3pwPZbzcetgDGCGNEMHkPopnY4Kjtlp5a3pk9lWd0nyXXOxGnsonCB7I8RRwh453SfJBf0ryXaOwGnshO6Y09keMo4Q8Q/pR8EJ3TD4Lt39JaeyC7pPkjxlb7YeLPTnDsVB2C8dl2Z6SPBCs6THZHjR7Yf/Sot6IJ4R29GaBwuiFDApek0DhVG4S84ejt8Eh0hvgui9JnEJeg3wSU4NfSwOyOzpzR2Wt6QCmK2pKc5mGG9kR+HRdS+i5gsqsG2yt3BCvlgULTVTU6+97aaWfTtsIYwf1nvhqSHmc36tdQdgnp2LkVZGDuBpZmbhbjxP8zkVNd69Wxzq/StYsXr/1d+yW1Mw2us21htwiAS1rP0v8j1mu/wC3F1dfXm5zzV0PFf1Egw7LfNGIw/y77B6l23/RVV+/8xZ3VOlZ4vdXj5Qyuq2VDKtJIq2ikP8AdXIe30P5yv07W/8AcX/R2WKSJN6scwCNHY6Tf9q6Th5J1dZSzd/WA9N3/SYg9S63gdPvbhnfldQsMVYGON1pJ+iLPzKP7f6T/glxeJ9ZOuV4Dem4NzGsDNrbBXtur13WelcP33Oe71NvqsXR/Ujp1GPTe80TnHabc71G3NsZaDY1lDme6j2j9Zrf+m/0v+jTTEC7+xIldAfa62Fk3WvsxsypuPnUtZZZSx29hrs/m7arf8I1r2vot/0d9f8AxathgVPrTHYgo6zWC49PJGU1vL8O0huU3+V9nd6eXX/xdi0dg5a4OaYLXDUEH3Nc3+S5qaV4RhgUvTaU+0hSDoSUwNDSoHHarAcEzklNU47fBQditKtT4p4lJT//0+hgJbVIMEaa+aZwc3gSPJVG3ag0JjHCcCRPipirv3CSkevgmHKLMaQgZF+yK6QH5VgPpMd9EAaG++Po49Tvpf6b+Yp/SJUq0WXnDHfXjVM+0Z14LqMYHb7QdrsnJs/7T4df593+E/mqN9ir/sHDvtbkdUJ6nkN+h60/Z6/5ONhT6ez/AMMevb/pFYwMGnDreTYb8vIIfmZb/p2vGjePbVj1fQxsWv8ARY9f/biJZk11aOlHyR5pZ9oaNGt0a0aADwa0e1qw+vdD6da63qxFo6iTTVTay1zdri5mNX6bGfyHLTbn4pcQXgfHRA6jlY7rsCreAwX/AGiwzoGY7HWSf+uuqSFqNPLdP+reTgsbkXH0qrd1gsb7bhWzcWsY5277N6zG/mt+0f6T0P5tdF9W8lt2I51bG11OMtawOID2zXe2y9/87b7abPUs/Sv9RYfWsnqFdgbbWTgExU5jix4qdufXXfUT6TH/ANT+c/wnvVj6mkZFtlu1zPRlrGtcfTDXNAefT+j6trtvv2/4FGWuq2Ohp6txrLSHgPY4Fr2u+iWkbXtd/Jc1Z3RQ/Gos6ZYS5uAQ3Fsd9J+LZufhl4/Nsq2W4zv+JV8tLpgzHKq21+n1DGt7Xssxnnzb+t4//nvKZ/1xNXtuAltUm1uiSlBHwSQsGJ9qeHJjokphsUtmiXu7pbiAip//1OiFgdv9P6VYEzLQ4f8AB7f5KLdc/d/MtqBaDubBGvG/lv8AWQyWlw3gEjUGPypxfY0iCJ0+8fRVVtLMix/qSfTM/R1BII/OO539X2qWrne0hzZgmTtn+ttTPusskPIdrIMahQ9gbo5wnuDB/BJSJ7sqyyxlYZUWk7Hul5IB+k1tcMd/b2f9cU8ap1THbmufkXHe4vje8jT80D2VfQqra3ZX+4p16u3NLQIjXk/FyKGuLNzYLjySNWpKQOLSGixvpySAXMeBPhulJ2OHlzGltmyN0NJAkTs3aIro3ETP8oc/enGNVBhw8SO8nukpouwKLHbnsYYP6MloB3eDp3LKzelOGY+9pdXXS1pdtDR7nne5uyzb7fZ+auhbhMklrg0nk9/hu/dVerD9XKyH2vFmx4c0dgIHDfznNSU8p1kdSyaWU5OLc30yXU5TGku1/f8AQD97P5CtfU9zqRkG4Pdl2uAfY5j9orZ/N1vt27PdPqPXUDHucdzHj4Exp/VU3YlwBebNPIoo8WvdkWU/QY297gS4VyII/rJsjZdW0kmp7C26pzmkhtjPcwPj6bHfzb9v5iP6dkb9+4nWCJ/6SYOc87nAETHEJtJtjTktsa1pLa7iATjvcN7THur2aOftj6SI4mOzddQ4wfxTD0xZGxgMydwkjTbo72+5M6mt7A17Wua3s73N0RUyGpiQIGuvCG5736s1HYgaH+0iiqpzS9m1lvZ5g/8AR0UnNYZDoO7n3CZ/rJIQl7+Nh050TkiJLYHiYTaSXG2Xd2vDTGm3cwtYEzw2sy7Y4E6vdM7vlLdqSn//1ehEpGsEyU/3J+yqtpjpCZrROoUhypd0lLFkjThLaQIk+aIOEuySmDXOAgd0zW7dYn4qQUjwkpEA8ukIjGH87XzUm/JOUkMRXtnbpPKZ1ROu7RTTdu6SkbmuAhp08EP0zEOJIHCKkkpAapESfvU2NLGx+VSKZJSOytr3e4Sp11FjdjANqXdGZwiprOoEwU7WOaIB07BGd9JM7hJT/9k4QklNBCEAAAAAAFUAAAABAQAAAA8AQQBkAG8AYgBlACAAUABoAG8AdABvAHMAaABvAHAAAAATAEEAZABvAGIAZQAgAFAAaABvAHQAbwBzAGgAbwBwACAAQwBTADMAAAABADhCSU0EBgAAAAAABwAIAAEAAQEA/+ESlmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8APD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4gPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iQWRvYmUgWE1QIENvcmUgNC4xLWMwMzYgNDYuMjc2NzIwLCBNb24gRmViIDE5IDIwMDcgMjI6NDA6MDggICAgICAgICI+IDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+IDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiIHhtbG5zOnRpZmY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vdGlmZi8xLjAvIiB4bWxuczp4YXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iIHhtbG5zOnBob3Rvc2hvcD0iaHR0cDovL25zLmFkb2JlLmNvbS9waG90b3Nob3AvMS4wLyIgeG1sbnM6eGFwTU09Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iIHRpZmY6T3JpZW50YXRpb249IjEiIHRpZmY6WUNiQ3JQb3NpdGlvbmluZz0iMiIgdGlmZjpYUmVzb2x1dGlvbj0iMTAxNDAwMC8xMDAwMCIgdGlmZjpZUmVzb2x1dGlvbj0iMTAxNDAwMC8xMDAwMCIgdGlmZjpSZXNvbHV0aW9uVW5pdD0iMiIgdGlmZjpNYWtlPSJDYW5vbiIgdGlmZjpNb2RlbD0iQ2Fub24gRU9TIDQwMEQgRElHSVRBTCIgdGlmZjpOYXRpdmVEaWdlc3Q9IjI1NiwyNTcsMjU4LDI1OSwyNjIsMjc0LDI3NywyODQsNTMwLDUzMSwyODIsMjgzLDI5NiwzMDEsMzE4LDMxOSw1MjksNTMyLDMwNiwyNzAsMjcxLDI3MiwzMDUsMzE1LDMzNDMyOzVBM0RCM0E3QkQ0RDI0QTg2QjNDNUVGNTJDMkY2QTMxIiB4YXA6TW9kaWZ5RGF0ZT0iMjAwOS0wMS0yN1QxNToyOTowOCswMzowMCIgeGFwOkNyZWF0ZURhdGU9IjIwMDktMDEtMjdUMTU6Mjk6MDgrMDM6MDAiIHhhcDpNZXRhZGF0YURhdGU9IjIwMDktMDEtMjdUMTU6Mjk6MDgrMDM6MDAiIHhhcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIENTMyBXaW5kb3dzIiBleGlmOkV4aWZWZXJzaW9uPSIwMjIxIiBleGlmOkZsYXNocGl4VmVyc2lvbj0iMDEwMCIgZXhpZjpDb2xvclNwYWNlPSIxIiBleGlmOlBpeGVsWERpbWVuc2lvbj0iMTgwMCIgZXhpZjpQaXhlbFlEaW1lbnNpb249IjEyMDAiIGV4aWY6RGF0ZVRpbWVPcmlnaW5hbD0iMjAwOS0wMS0xOVQxNTowODoyMCswMzowMCIgZXhpZjpEYXRlVGltZURpZ2l0aXplZD0iMjAwOS0wMS0xOVQxNTowODoyMCswMzowMCIgZXhpZjpFeHBvc3VyZVRpbWU9IjEvMTYwIiBleGlmOkZOdW1iZXI9IjExLzEiIGV4aWY6RXhwb3N1cmVQcm9ncmFtPSIxIiBleGlmOlNodXR0ZXJTcGVlZFZhbHVlPSI0Nzk4NTAvNjU1MzYiIGV4aWY6QXBlcnR1cmVWYWx1ZT0iNDUzNDM1LzY1NTM2IiBleGlmOkV4cG9zdXJlQmlhc1ZhbHVlPSIwLzMiIGV4aWY6TWV0ZXJpbmdNb2RlPSI1IiBleGlmOkZvY2FsTGVuZ3RoPSIxNTAvMSIgZXhpZjpGb2NhbFBsYW5lWFJlc29sdXRpb249IjM4ODgwMDAvODc3IiBleGlmOkZvY2FsUGxhbmVZUmVzb2x1dGlvbj0iMjU5MjAwMC81ODIiIGV4aWY6Rm9jYWxQbGFuZVJlc29sdXRpb25Vbml0PSIyIiBleGlmOkN1c3RvbVJlbmRlcmVkPSIwIiBleGlmOkV4cG9zdXJlTW9kZT0iMSIgZXhpZjpXaGl0ZUJhbGFuY2U9IjAiIGV4aWY6U2NlbmVDYXB0dXJlVHlwZT0iMCIgZXhpZjpOYXRpdmVEaWdlc3Q9IjM2ODY0LDQwOTYwLDQwOTYxLDM3MTIxLDM3MTIyLDQwOTYyLDQwOTYzLDM3NTEwLDQwOTY0LDM2ODY3LDM2ODY4LDMzNDM0LDMzNDM3LDM0ODUwLDM0ODUyLDM0ODU1LDM0ODU2LDM3Mzc3LDM3Mzc4LDM3Mzc5LDM3MzgwLDM3MzgxLDM3MzgyLDM3MzgzLDM3Mzg0LDM3Mzg1LDM3Mzg2LDM3Mzk2LDQxNDgzLDQxNDg0LDQxNDg2LDQxNDg3LDQxNDg4LDQxNDkyLDQxNDkzLDQxNDk1LDQxNzI4LDQxNzI5LDQxNzMwLDQxOTg1LDQxOTg2LDQxOTg3LDQxOTg4LDQxOTg5LDQxOTkwLDQxOTkxLDQxOTkyLDQxOTkzLDQxOTk0LDQxOTk1LDQxOTk2LDQyMDE2LDAsMiw0LDUsNiw3LDgsOSwxMCwxMSwxMiwxMywxNCwxNSwxNiwxNywxOCwyMCwyMiwyMywyNCwyNSwyNiwyNywyOCwzMDsyMzcwMDNCQkVCQjBFMkRGNjEzNDgxQjYyMUEyMDRBNCIgZGM6Zm9ybWF0PSJpbWFnZS9qcGVnIiBwaG90b3Nob3A6Q29sb3JNb2RlPSIzIiBwaG90b3Nob3A6SUNDUHJvZmlsZT0ic1JHQiBJRUM2MTk2Ni0yLjEiIHBob3Rvc2hvcDpIaXN0b3J5PSIiIHhhcE1NOkluc3RhbmNlSUQ9InV1aWQ6NTRFRTQ1MUY2REVDREQxMUFCMDVEMjZEMDJBNDFCRkYiIHhhcE1NOkRvY3VtZW50SUQ9InV1aWQ6NTNFRTQ1MUY2REVDREQxMUFCMDVEMjZEMDJBNDFCRkYiPiA8ZXhpZjpJU09TcGVlZFJhdGluZ3M+IDxyZGY6U2VxPiA8cmRmOmxpPjQwMDwvcmRmOmxpPiA8L3JkZjpTZXE+IDwvZXhpZjpJU09TcGVlZFJhdGluZ3M+IDxleGlmOkZsYXNoIGV4aWY6RmlyZWQ9IlRydWUiIGV4aWY6UmV0dXJuPSIwIiBleGlmOk1vZGU9IjEiIGV4aWY6RnVuY3Rpb249IkZhbHNlIiBleGlmOlJlZEV5ZU1vZGU9IlRydWUiLz4gPHhhcE1NOkRlcml2ZWRGcm9tIHJkZjpwYXJzZVR5cGU9IlJlc291cmNlIi8+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIDw/eHBhY2tldCBlbmQ9InciPz7/4gxYSUNDX1BST0ZJTEUAAQEAAAxITGlubwIQAABtbnRyUkdCIFhZWiAHzgACAAkABgAxAABhY3NwTVNGVAAAAABJRUMgc1JHQgAAAAAAAAAAAAAAAAAA9tYAAQAAAADTLUhQICAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABFjcHJ0AAABUAAAADNkZXNjAAABhAAAAGx3dHB0AAAB8AAAABRia3B0AAACBAAAABRyWFlaAAACGAAAABRnWFlaAAACLAAAABRiWFlaAAACQAAAABRkbW5kAAACVAAAAHBkbWRkAAACxAAAAIh2dWVkAAADTAAAAIZ2aWV3AAAD1AAAACRsdW1pAAAD+AAAABRtZWFzAAAEDAAAACR0ZWNoAAAEMAAAAAxyVFJDAAAEPAAACAxnVFJDAAAEPAAACAxiVFJDAAAEPAAACAx0ZXh0AAAAAENvcHlyaWdodCAoYykgMTk5OCBIZXdsZXR0LVBhY2thcmQgQ29tcGFueQAAZGVzYwAAAAAAAAASc1JHQiBJRUM2MTk2Ni0yLjEAAAAAAAAAAAAAABJzUkdCIElFQzYxOTY2LTIuMQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAWFlaIAAAAAAAAPNRAAEAAAABFsxYWVogAAAAAAAAAAAAAAAAAAAAAFhZWiAAAAAAAABvogAAOPUAAAOQWFlaIAAAAAAAAGKZAAC3hQAAGNpYWVogAAAAAAAAJKAAAA+EAAC2z2Rlc2MAAAAAAAAAFklFQyBodHRwOi8vd3d3LmllYy5jaAAAAAAAAAAAAAAAFklFQyBodHRwOi8vd3d3LmllYy5jaAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABkZXNjAAAAAAAAAC5JRUMgNjE5NjYtMi4xIERlZmF1bHQgUkdCIGNvbG91ciBzcGFjZSAtIHNSR0IAAAAAAAAAAAAAAC5JRUMgNjE5NjYtMi4xIERlZmF1bHQgUkdCIGNvbG91ciBzcGFjZSAtIHNSR0IAAAAAAAAAAAAAAAAAAAAAAAAAAAAAZGVzYwAAAAAAAAAsUmVmZXJlbmNlIFZpZXdpbmcgQ29uZGl0aW9uIGluIElFQzYxOTY2LTIuMQAAAAAAAAAAAAAALFJlZmVyZW5jZSBWaWV3aW5nIENvbmRpdGlvbiBpbiBJRUM2MTk2Ni0yLjEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHZpZXcAAAAAABOk/gAUXy4AEM8UAAPtzAAEEwsAA1yeAAAAAVhZWiAAAAAAAEwJVgBQAAAAVx/nbWVhcwAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAo8AAAACc2lnIAAAAABDUlQgY3VydgAAAAAAAAQAAAAABQAKAA8AFAAZAB4AIwAoAC0AMgA3ADsAQABFAEoATwBUAFkAXgBjAGgAbQByAHcAfACBAIYAiwCQAJUAmgCfAKQAqQCuALIAtwC8AMEAxgDLANAA1QDbAOAA5QDrAPAA9gD7AQEBBwENARMBGQEfASUBKwEyATgBPgFFAUwBUgFZAWABZwFuAXUBfAGDAYsBkgGaAaEBqQGxAbkBwQHJAdEB2QHhAekB8gH6AgMCDAIUAh0CJgIvAjgCQQJLAlQCXQJnAnECegKEAo4CmAKiAqwCtgLBAssC1QLgAusC9QMAAwsDFgMhAy0DOANDA08DWgNmA3IDfgOKA5YDogOuA7oDxwPTA+AD7AP5BAYEEwQgBC0EOwRIBFUEYwRxBH4EjASaBKgEtgTEBNME4QTwBP4FDQUcBSsFOgVJBVgFZwV3BYYFlgWmBbUFxQXVBeUF9gYGBhYGJwY3BkgGWQZqBnsGjAadBq8GwAbRBuMG9QcHBxkHKwc9B08HYQd0B4YHmQesB78H0gflB/gICwgfCDIIRghaCG4IggiWCKoIvgjSCOcI+wkQCSUJOglPCWQJeQmPCaQJugnPCeUJ+woRCicKPQpUCmoKgQqYCq4KxQrcCvMLCwsiCzkLUQtpC4ALmAuwC8gL4Qv5DBIMKgxDDFwMdQyODKcMwAzZDPMNDQ0mDUANWg10DY4NqQ3DDd4N+A4TDi4OSQ5kDn8Omw62DtIO7g8JDyUPQQ9eD3oPlg+zD88P7BAJECYQQxBhEH4QmxC5ENcQ9RETETERTxFtEYwRqhHJEegSBxImEkUSZBKEEqMSwxLjEwMTIxNDE2MTgxOkE8UT5RQGFCcUSRRqFIsUrRTOFPAVEhU0FVYVeBWbFb0V4BYDFiYWSRZsFo8WshbWFvoXHRdBF2UXiReuF9IX9xgbGEAYZRiKGK8Y1Rj6GSAZRRlrGZEZtxndGgQaKhpRGncanhrFGuwbFBs7G2MbihuyG9ocAhwqHFIcexyjHMwc9R0eHUcdcB2ZHcMd7B4WHkAeah6UHr4e6R8THz4faR+UH78f6iAVIEEgbCCYIMQg8CEcIUghdSGhIc4h+yInIlUigiKvIt0jCiM4I2YjlCPCI/AkHyRNJHwkqyTaJQklOCVoJZclxyX3JicmVyaHJrcm6CcYJ0kneierJ9woDSg/KHEooijUKQYpOClrKZ0p0CoCKjUqaCqbKs8rAis2K2krnSvRLAUsOSxuLKIs1y0MLUEtdi2rLeEuFi5MLoIuty7uLyQvWi+RL8cv/jA1MGwwpDDbMRIxSjGCMbox8jIqMmMymzLUMw0zRjN/M7gz8TQrNGU0njTYNRM1TTWHNcI1/TY3NnI2rjbpNyQ3YDecN9c4FDhQOIw4yDkFOUI5fzm8Ofk6Njp0OrI67zstO2s7qjvoPCc8ZTykPOM9Ij1hPaE94D4gPmA+oD7gPyE/YT+iP+JAI0BkQKZA50EpQWpBrEHuQjBCckK1QvdDOkN9Q8BEA0RHRIpEzkUSRVVFmkXeRiJGZ0arRvBHNUd7R8BIBUhLSJFI10kdSWNJqUnwSjdKfUrESwxLU0uaS+JMKkxyTLpNAk1KTZNN3E4lTm5Ot08AT0lPk0/dUCdQcVC7UQZRUFGbUeZSMVJ8UsdTE1NfU6pT9lRCVI9U21UoVXVVwlYPVlxWqVb3V0RXklfgWC9YfVjLWRpZaVm4WgdaVlqmWvVbRVuVW+VcNVyGXNZdJ114XcleGl5sXr1fD19hX7NgBWBXYKpg/GFPYaJh9WJJYpxi8GNDY5dj62RAZJRk6WU9ZZJl52Y9ZpJm6Gc9Z5Nn6Wg/aJZo7GlDaZpp8WpIap9q92tPa6dr/2xXbK9tCG1gbbluEm5rbsRvHm94b9FwK3CGcOBxOnGVcfByS3KmcwFzXXO4dBR0cHTMdSh1hXXhdj52m3b4d1Z3s3gReG54zHkqeYl553pGeqV7BHtje8J8IXyBfOF9QX2hfgF+Yn7CfyN/hH/lgEeAqIEKgWuBzYIwgpKC9INXg7qEHYSAhOOFR4Wrhg6GcobXhzuHn4gEiGmIzokziZmJ/opkisqLMIuWi/yMY4zKjTGNmI3/jmaOzo82j56QBpBukNaRP5GokhGSepLjk02TtpQglIqU9JVflcmWNJaflwqXdZfgmEyYuJkkmZCZ/JpomtWbQpuvnByciZz3nWSd0p5Anq6fHZ+Ln/qgaaDYoUehtqImopajBqN2o+akVqTHpTilqaYapoum/adup+CoUqjEqTepqaocqo+rAqt1q+msXKzQrUStuK4trqGvFq+LsACwdbDqsWCx1rJLssKzOLOutCW0nLUTtYq2AbZ5tvC3aLfguFm40blKucK6O7q1uy67p7whvJu9Fb2Pvgq+hL7/v3q/9cBwwOzBZ8Hjwl/C28NYw9TEUcTOxUvFyMZGxsPHQce/yD3IvMk6ybnKOMq3yzbLtsw1zLXNNc21zjbOts83z7jQOdC60TzRvtI/0sHTRNPG1EnUy9VO1dHWVdbY11zX4Nhk2OjZbNnx2nba+9uA3AXcit0Q3ZbeHN6i3ynfr+A24L3hROHM4lPi2+Nj4+vkc+T85YTmDeaW5x/nqegy6LzpRunQ6lvq5etw6/vshu0R7ZzuKO6070DvzPBY8OXxcvH/8ozzGfOn9DT0wvVQ9d72bfb794r4Gfio+Tj5x/pX+uf7d/wH/Jj9Kf26/kv+3P9t////7gAOQWRvYmUAZEAAAAAB/9sAhAABAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAgEBAQEBAQICAgICAgICAgICAgICAwMDAwMDAwMDAwMDAwMDAQEBAQEBAQIBAQIDAgICAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwP/wAARCASwBwgDAREAAhEBAxEB/90ABADh/8QAwAAAAgICAwEBAAAAAAAAAAAAAwQBAgAFBgcICQoBAAMBAQEBAQEBAAAAAAAAAAECAwAEBQYHCAkQAAECBQMDAQUHAgYCAQEBEQERAgAhEgMEMUEFUSIGYXGBMhMHkaGxwUIUCNFS8OFiIxUJ8XIzFoIkkkMXorLCUyUm0uJjNHM1GBEAAgIBAwMDAgQFBAIBAwIHAAERAiExEgNBUQRhIhNxBfCBkTKhscFCFNHhIwbxUhVicjMkFoKSsjSiQwf/2gAMAwEAAhEDEQA/APJBqLy1EaJklp3nIico/wA/6ptn+tPJoRSN/iH+nbYS9I6EpcIK0U6ki2SFqBVVBb2oOhE4Nq7dTY0BGyA4FoChZgatREq1jUifcC1U9S4ZqgmgCJMCSqf6RZKXCJWSrogRDS4Utm1QSdCTo0HWHc1pBKVMFkGlO46kEJpprC1s5C11JYCQ1AfVrioPRIqxegP5IUyBeSUSohN0H3JBrO7BGze5soGkqag4aEAajYesF4tkpRzX+ZiTn+n3NIRA0eyOilsSaF1AXgaaaShIQBB1J9pgTmRXaqwI3Arg1qtQpqEII0IOkaTa5Qu4KCHtFTd9B0mRpB+gU4cgKWkKHVHWkaLuTFNraGfIohGuvW7tYL0M1AQBrWbAJ06xRYUEHLcsRvsaBUAq6AFCSvQflFKtLUZp1Sa1E30OaQAQQe9hBQJoQ7+sOnXVDVbspNZlKKiokgAMwTuQfwikbnLA7tW2msuh7jVUU0O8jIkewwtWqtyBzqau+wiSGl6io/qImiRdeoatJQzWXO5WnspaZtaVVdlhtAu1Zg115jzcCOCbLoZShk4NHQ1921SZoXFTKYC6qIKbDNq4Nfdt2vhBKCaqhlqPbDpvqbexB9tha8tbQoVQZuC6O901ivG31Kq1ksIQc1wYTUg1CTBBCQ18/kSfLLyK3WNLFqd/9rQekItcDegk0EyboCUCF3p74tZuBHZVJIDnNMpNSWkpGJJlK3ayAeof2hoDgQZCYAmYyn+4LumoaAucgeXAFoOrT207SGhnDJp6MallhaMuymgNbNriSC5XSGiEz90Ss8Sjtq9ykWeA3QhQ9WgooJEwU26xNUb1wT5FFp7kseRU2SOCamQ3pIGkOq1RlyJKCgCH0X4B91XSEvL1BVb3Jj7jyWhrS5kwgJnOn9XX1gQXyHtTa4O/23HUvnQn9oHXcwGEKy2E7iS0jX9ROpP2bQH6GjIZoIAFtSk11q/0wjpLkP0GGuBDVLgJdoRFGxGx9hnGfYle/RD1p7QS0dW6/EAqlU6ekczmSFLTae5sLTWEdpFZJMgge3YgaqInd9DuTSpkdZSCvcXALU5ZhugK7COSycyJKQRlyo0BC31KBxIVV/AQa1erC3iB1rnaIASCSmqAS0ijrjcQvyQ8MZahb/uNBpPwtPYVnUTtHK5THry7jYNokWkkpoTJDpDJJL3Ctt6BGOdIFwRpmQQhaZzWJxMwaZ6Dtq60lVbb2DQ0oRopT1hBphZY2x1LiHlS4hCFpAWUoDhk7XnCHWaENAcQ1JyUE6yiO1pytDOzahss0t0eKVU+xAKj1jb29FP46gcLLGyS5qMJIaJNdp7QDCy6rK1IN7nIcOAAQBBr/pnqPZE46lqKK/Uv84l5KOcCKmn00JC9IzWCclgXElxerkXQqiSnt7IBsg3PL2IQR/pBmfed4ybQVey9RK4EUpOROnwia/1EOsoonKlClwdoI0kUDSFq1ENW222SlbZhsRebbSjaqnAo9SD6gj8IrayiEJZy4TBkFoIDyKis1Blrr1ha1VtRZMdbULJNHDbSawXTtkH1JrJBBO0vSSIIZUjU09wJKI1oCSUoukyh3iTwwyyjnORwmQNAAHKNl3EFZwVpmpQPths1E5kj4eidffD7HEhbSyL3vhLi5RJGmRdOS/1hXWyWhzWtDAEzc1wDA0K0LvvUOhg1mVBenIqrOAZKESHaHGlqEodgYu8rAjat7Ra44FqsUOqR3SjUFB+MCrs37kPjoL3tbZJAaELZp3azT74dG2t5F7zmsK0BHKShmJyKHrD1tCJ30FHVFwdbcKVkiAtJ1B3nDp2bT0QKpJYBuL6yWuaiiSktATY6TigLR1yCuEgOIPcSFSYVJpGSUC7KxMAHEgIHBU+EglqahC3rA3Q+wt1VdClTiXK1qFsgVLhKev4xmSF+0FwLZGSAg0uPX0jEr2acIwBiAkFQPQuIB6HQ+saTVsnqwDmkGZLQTIrLrMiC2ma1000UdQhDCSSGgudqXHUyjUcMk9BUF4ef9sOqUlxkR7PyEdVW0TcTkHcrbac6oOK/Ak0JTSFtZ2wyV2pBOeRabU0smau5VG6j+kPSy0EZD6HzBKFJaqdyggzk57zuclHPKPVSiVOGyaSPrrDJKyZK8wCVdFJmELUMtUP+UI008nM62bxoUDg182vf2SrKsYoTtAmqQ9NNovxpPLLUjuTUDtLkVU6DYxTQW2Eqis2VsB7/AE+FoVCPftGThyc9r4ioCgqUcW1Ks5S2aPxi3qShxJhDTUVQSApPcu9SxpWqEtaFOoD5jUpJc1wm0LKQSopvA6E7XlQwJqVyTLtSSBVvNIrVzhkrKRdxIAAQ1HX+466dIvRqDktjPQC9wQlrBNR6kal0oZpPUm+RIVuuDSAppI0TczMxp7Yei6nLyWl4AF7XFDNBtoiaf+Y69CCs2oQIilxDHObogMwvQrFYiqIcsqWhatwcW1IVBP8Aq6kJFaNRBwcluhhaCPmVPoYiyAKmY0+xYtV4INN4F3vY3YsJCgAoD0JMMs6AcRkRuPe8GtwMygYCSOpJlHVxw8Hn8tktQKoQSW0hxB6/Z1MdL0OS1k9dBa49CQCSdWkSB3UxtMnJezSbAvcXD4gG9SpUnUAfjDU1ODkunoJkh5IVgDQCK/1TWQG8Xq+5x2anQWuuFRdU52ugRp9m/wBsOnJHk5OkGvvWyTUXADVKVKnQqY6+KUcN7blgSc4qQEIaih2okgmekdSs0cnIKXWyIAc5EExNesWrZanLy1lSa1wLHL3ItOiBY6q2TPPatux1BPe13bo4LsgB1JCQwzt0szXuLnkuUFCNP0t3VYK1Oa2FIMkNJcQgNNKTKGYmdliql6kVt/u1FfmMFQaSXEkBUUeqiS+kPtbQd1Go0Fbx7iwfEQXFQAVT4iBGq2nDIcldywJuaA2Ya4uMt0mh03iyjMnHZWWSldAICFZKWqhGglvE7V3DU5O4MucSQnwupaBqDKbtvZBrVKC6u05YN76mvUq5pJnp0ijo+uS29bc5KK1zWgjvuNm3QqNQg9IjZQK7IikUUluhqlIhJaf1gO1oK8bhmNe1oILaqiQqoSNSP/MTe56nTvUENc1rCXCmskAIhcm3SFzUn9DC4bdsig/UomYyTeSqcrPUlp7WkgU6mSuX09YzTWo/Eotj6l3PYBIEFF0O8A63yJ9ZBucHSKgIsiSFOwMEV3SCW3qpJZuAJkho3K/hCXj8zo4LZGrb6HVAh1SBri5zRMayiF5nJ1NpVGnXC16gNJDB8RKFpkXNAgKrYm7a5DW3OQfCKtwdWqrV3WM00UXIllMfa9jGtqQOP6VIRdVJ/KIvdOR1zJ4G7d0U0EuKlULih3mIVHXx2THLF0KAjnCQRrtAToVguriYLTiR8XQFDSSSklnrL7Ihajb/ANArkqkMW3g3C8uPwgkgrugqWDFkpNvq2ja2XlqGubhSFlJFKAQjbZetlVGwZecXEuJLWtDQuie6DucE3eXJsce+f7TbBJAbqq6r7YRla8jjBtWMeLbb5Z2F4aXKEB6LEJWhZX6hmXQWkoXAdqj4Q7aYhWu2Aqys8jlm+GGZ2DWtSS9PdBiS9UmpNlZvOajpOM+yaFdj1jdYGdlXBurGYSE0IFLWuUNI6iFahwzfI7GzsXK3CprpNQkztldEgGq8jTEImXqEkQEM5IkYthhrF0J3B5AKFNTSZQbPEaEo2aMdNxpaTbrACEqQSvqPxMJu25BbkssIqHOc5g==", + "url": "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg", + "mime_type": "image/jpeg", + "file_size": 758559, + "width": 1800, + "height": 1200, + "format": "JPEG" + }, + "jpeg_1920": { + "head_b64": "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", + "url": "https://a.d-cd.net/NnguN8BamP5YNvPYMNoZ1sep_a0-1920.jpg", + "mime_type": "image/jpeg", + "file_size": 695363, + "width": 1800, + "height": 1200, + "format": "JPEG" + }, + "jpeg_400x300": { + "head_b64": "/9j/4AAQSkZJRgABAQEARwBHAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wgARCAEsAZADAREAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAf/xAAWAQEBAQAAAAAAAAAAAAAAAAAABgj/2gAMAwEAAhADEAAAAZzC6pAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH/8QAFBABAAAAAAAAAAAAAAAAAAAAoP/aAAgBAQABBQJtn//EABQRAQAAAAAAAAAAAAAAAAAAAKD/2gAIAQMBAT8BbZ//xAAUEQEAAAAAAAAAAAAAAAAAAACg/9oACAECAQE/AW2f/8QAFBABAAAAAAAAAAAAAAAAAAAAoP/aAAgBAQAGPwJtn//EABQQAQAAAAAAAAAAAAAAAAAAAKD/2gAIAQEAAT8hbZ//2gAMAwEAAgADAAAAEP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/AP8A/wD/xAAUEQEAAAAAAAAAAAAAAAAAAACg/9oACAEDAQE/EG2f/8QAFBEBAAAAAAAAAAAAAAAAAAAAoP/aAAgBAgEBPxBtn//EABQQAQAAAAAAAAAAAAAAAAAAAKD/2gAIAQEAAT8QbZ//2Q==", + "url": "https://samplelib.com/jpeg/sample-red-400x300.jpg", + "mime_type": "image/jpeg", + "file_size": 2668, + "width": 400, + "height": 300, + "format": "JPEG" + }, + "gif_animated": { + "head_b64": "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", + "url": "https://upload.wikimedia.org/wikipedia/commons/2/2c/Rotating_earth_(large).gif", + "mime_type": "image/gif", + "file_size": 1001718, + "width": 400, + "height": 400, + "duration": 4.5, + "fps": 11.111, + "bitrate_avg": 1739, + "error_desc": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", + "format": "GIF" + }, + "webp_static": { + "head_b64": "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", + "url": "https://static.wikia.nocookie.net/dogs/images/3/37/Corgi.jpg/revision/latest/scale-to-width-down/1200", + "mime_type": "image/webp", + "file_size": 171004, + "width": 1200, + "height": 802, + "format": "WEBP/VP8" + }, + "webp_from_jpg": { + "head_b64": "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", + "url": "https://i.oneme.ru/i?r=BTE2sh_eZW7g8kugOdIm2NotjU5wvPCGU6C84MIROa43V59d4UMSYKFe4uq1jLgMEwU", + "mime_type": "image/webp", + "file_size": 58086, + "width": 1280, + "height": 714, + "format": "WEBP/VP8X" + }, + "webp_animated_200": { + "head_b64": "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", + "url": "https://samplelib.com/webp/sample-animated-200x200.webp", + "mime_type": "image/webp", + "file_size": 8738, + "width": 200, + "height": 200, + "duration": 1.0, + "fps": 8.333, + "bitrate_avg": 71, + "format": "WEBP/VP8X" + }, + "webp_animated_1": { + "head_b64": "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", + "url": "https://colinbendell.github.io/webperf/animated-gif-decode/1.webp", + "mime_type": "image/webp", + "file_size": 9539758, + "width": 800, + "height": 450, + "duration": 8.7, + "fps": 30.043, + "bitrate_avg": 8584, + "error_desc": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", + "format": "WEBP/VP8X" + }, + "webp_animated_2": { + "head_b64": "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", + "url": "https://colinbendell.github.io/webperf/animated-gif-decode/2.webp", + "mime_type": "image/webp", + "file_size": 11513060, + "width": 800, + "height": 450, + "duration": 9.0, + "fps": 25.0, + "bitrate_avg": 10000, + "error_desc": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", + "format": "WEBP/VP8X" + }, + "webp_animated_4": { + "head_b64": "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", + "url": "https://colinbendell.github.io/webperf/animated-gif-decode/4.webp", + "mime_type": "image/webp", + "file_size": 943960, + "width": 1284, + "height": 518, + "format": "WEBP/VP8X" + }, + "avi_15s": { + "head_b64": "UklGRhx4DgBBVkkgTElTVMoiAABoZHJsYXZpaDgAAABAnAAAKKAAAAAAAAAQCQAAeAEAAAAAAAACAAAAAAAQAIACAADgAQAAAAAAAAAAAAAAAAAAAAAAAExJU1TgEAAAc3RybHN0cmg4AAAAdmlkc0ZNUDQAAAAAAAAAAAAAAAABAAAAGQAAAAAAAAB4AQAArTwAAP////8AAAAAAAAAAIAC4AFzdHJmKAAAACgAAACAAgAA4AEAAAEAGABGTVA0ABAOAAAAAAAAAAAAAAAAAAAAAABKVU5LGBAAAAQAAAAAAAAAMDBkYwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB2cHJwRAAAAAAAAAAAAAAAGQAAAIACAADgAQAAAwAEAIACAADgAQAAAQAAAOABAACAAgAA4AEAAIACAAAAAAAAAAAAAAAAAAAAAAAATElTVIoQAABzdHJsc3RyaDgAAABhdWRzAQAAAAAAAAAAAAAAAAAAACAAAADJBAAAAAAAAEACAACiAQAA/////wAAAAAAAAAAAAAAAHN0cmYeAAAAVQABAESsAACAPgAAgAQAAAwAAQACAAAAgAQBAHEFSlVOSxgQAAAEAAAAAAAAADAxd2IAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAASlVOSwQBAABvZG1sZG1saPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAExJU1QaAAAASU5GT0lTRlQOAAAATGF2ZjYwLjE2LjEwMABKVU5L+AMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATElTVJQVDgBtb3ZpMDF3YqEBAAD/+5DEAAAUqGckFbeACyC345s9QAA5GROLgzdV0DN5kjAclcHobB6FsccrGhjJiY6ZKUmTkZjoyYmHmFgoGA0UwwBCAjgOQJAIYPQXBQKNPnOaZpoeo2ePR4wKxWMjymvm79/cfvgAZmI/+AZ/+HgDvgAYe+OHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4ePAAAAAEYeHh7wAAAP3mPAIAQAQQQgMY8tIxxfhjAiB8MbXRowMgNDGtiuN94z0wdwwjpuGKGQUT0QEmHQbzErCyMKcLAODCe8HBXQ+BoqIHMhmR/A4U0DDoANCeRRbgZ9MAQNAxw0DGE0lOvwDRQGNGAY0kAsDAwgP/wbCh0QXDBYUJtDFP/4WEh+wYqC4YUiGrQyL//jHCCwgsOSKBFAjqFzC5v//yGi5RzSaHOHOKJFSKmRFv///IsUSKk6ZEWNll08o2To/////q16S0TFJIyRRMUki8owBwCDMEaBnTBaAFswIkFqMTnLJDUPyYI+gIahMkwAMDBkY0s6AAAAAAGwAQAAAbWJEwAAAQAAAAEgAMSNiADNFAQ8FEMAAAGyTGF2YzYwLjMxLjEwMgAAAbMAEAcAAAG2EGCxgpm38bbfxtt/G238bbfxh0bYLoHHG238bbfxtt+xW2MxJCCwPJvs59LycaX5ilFrdQSIyzpbe4izS1xjbPlXr9vZmQHLQ3RUJCQIw9CCX+HglJvp0/xEyDadkHLeommM7FLe8wwzBxzN/eZO0HBOLmB5mLtlemhEBYjNZAeEvmsanMgzQ0poOPQDgAaDCWXj3qoSlI+LgIy8Sswl2AWwNvLhzpACAClBgQR6JKlMIRaJQ87YOZeD9PA32Aw2ygQ9VIc/IIybbBwB8jbb+Ntv422/jCW2wYQJyNtv422/jbb+Ntv4wMO2wE4Bgjbb+Ntv422/jbb+MJbbBhAm422/jbb+Ntv2gqwX/9yUr8L5CSgPNch4W+bxudyjJDCmA48aHWbvr3Lyg4JgagDQYfD4R1KtIoHg/q6nIDLM1CIPoi2gxvy4cwyDqClBgOCUB9SnHygShLq93MBipXUIftYi3QYOfFYcwWsQODE4kgGNpOtplLKaZ3e8HLXEannFOCKpzCqdLZJikyOAdVaptN8sZxRRdSXKhcCWEID4lAGl2D0Rk+J2KjQNsaDGlbQ3YbgcaU53OPjbbOC6Ntv422/jbb+MOjbBdA5I22/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv2lu22/Me1tvZnsnd2g5Haiki9q0OhuCGB/R6BhhT5It2Ubt6Cp5bFGUbZ2rh7V1EFg4Px2wPs7q/6HTUR6A7CUYdeC2DNAyQRwZlKWJoIA+9vC8S/51hUhUYvBBV2DZjKhm/vvHWQMYhghh2mHhRUsoI8YDKM9NlkDoLwx23bd2223ttXttR21DXjZg3pS9kv+pboaYImQ1IGKN4C7AhApBCBoXAiCQxy3gfp9XTpf8kut/JedjWcawRMdGE1tg0gM7G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G6bBVBKYYTB6JIIWM9ZHyhr6gGG0kjTdgcIo1vCRRt50sK/vYiBIVpmEvixnOxBRFym8qAFyA0YHADRHBCL4PxGV1j6L/huwr8DGlbSL+A7cR+3NOtQabLlubLLLyyrWWI5YgruEkRxG8JfkGJ8gGGwcBT5v1GPqV4IuGQ4JUjZc3WGZzYCNuINyocoLC82WOBuHolNjygRYo2bDDTWhSAvAMmBhLEgGZY5nEgj+lqsSN3uqM4CtVbyCCwvF+KfVbYi0zG2ux8FONtv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/a3bbfmPa23sz2Tu7QcjtRSRe1aHbbb8x7W29meyd3aDkdqKSL2rQ6HMRxH+Jf0OJsoGGgcBX5v9GHoVYIumR4djvw88HLOh14HFemsgvRQVAqgw+Bh8Xgysv5FKoRlWwG6PeIkIfKurfT/gyage/ETBawg5iOI/xL+hxNlAw0DgK/N/ow9CrBF0yMRiwcm2/s/rbdU7qOg5BIgcAtwlCUXBCLmB0OmMVJFWc96C/dqP//0r//V9q7422zBONtv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbbIDRhi8JQ7HTQ7aLPrbALNiL7QxyiLgikmzNcygJv/Y3l9+Kc1ax4DKFxenHSdgvLmdVJ1Wz7DPrzc2BnKi3L3Ny3NllrmoOqVI2XN1hmc2AjbiDcqHKCwvNljlwlDsdNDtos+tsAs2IvtDHKIuCKSbM1wdUqRsubrDM5sBG3EG5UOUFhebLHA3iSJGj7ewrVaNtDgNtJTVEUUgLkDCQDCSmBm0wgMqFQH1WAxWmRYzkWBFSG/UCvl9VbeXvrmvjbXYZAmRtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfs7ttvzHtbb2Z7J3doOR2opIvatDrDcB8IYN3RAaBgLe3svCtvV4Wm8zlqjEa/fr8wWDg7H+D5hTV/1Zrwc0HDaIRgjeC2DJAZpKDMiTE3lPh8wo+JaWcBlmL3mZ1aq2VG4xi6Lf2KPGWEHMRxH+Jf0OJsoGGgcBX5v9GHoVYIumRiMWDk239n9bbqndR0HIJEDgFuEoSi4IRcwOh0xipIqznvQX7tR//+lf/6vtXfG22YJxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G22h7DKBKHY6aHbRZ9bYBZsRfaGOURcEUk2ZrmUBQ/7G8vvxTmrWPAY0uL046TsF5czqpOq2fYZ9ebmwM5UW5e5uW5sstc1B1SpGy5usMzmwEbcQblQ5QWF5sscuE4QxIwS2pOqGWu1jREX53Ngcby", + "url": "https://samplefile.com/samples/download/video/avi/avi_15s_sample_file_927KB.avi/", + "mime_type": "video/x-msvideo", + "file_size": 948260, + "width": 640, + "height": 480, + "duration": 15.0, + "fps": 25.0, + "sample_rate": 44100, + "bitrate_nominal": 328, + "bitrate_avg": 494, + "format": "AVI" + }, + "avi_bird": { + "head_b64": "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", + "url": "https://www.engr.colostate.edu/me/facil/dynamics/files/bird.avi", + "mime_type": "video/x-msvideo", + "file_size": 1496576, + "width": 256, + "height": 240, + "duration": 11.0, + "fps": 30.0, + "bitrate_nominal": 1051, + "bitrate_avg": 1063, + "format": "AVI" + }, + "avi_sample": { + "head_b64": "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", + "url": "https://github.com/projectivetech/media-samples/raw/refs/heads/master/sample.avi", + "mime_type": "video/msvideo", + "file_size": 375688, + "width": 320, + "height": 240, + "duration": 5.0, + "fps": 25.0, + "sample_rate": 44100, + "bitrate_avg": 587, + "format": "AVI" + }, + "ogv_30s": { + "head_b64": "T2dnUwACAAAAAAAAAADuj9WhAAAAAM/Yl9cBKoB0aGVvcmEDAgEAKAAeAAKAAAHgAAAAAAAZAAAAAQAAAQAAAQAPQkAA2E9nZ1MAAgAAAAAAAAAAvxNrLAAAAADodQJwAR4Bdm9yYmlzAAAAAAFErAAA/////wD0AQD/////uAFPZ2dTAAAAAAAAAAAAAO6P1aEBAAAA9nWkIg4/////////////////kIF0aGVvcmENAAAATGF2ZjYwLjE2LjEwMAEAAAAfAAAAZW5jb2Rlcj1MYXZjNjAuMzEuMTAyIGxpYnRoZW9yYYJ0aGVvcmG+zSj3uc1rGLWpSUoQc5zmMYxSlKQhCDGMYhCEIQhAAAAAAAAAAAAAEW2uU2eSyPxWEvx4OVts5ir1aKtUKBMpJFoQ/nk5m41mUwl4slUpk4kkghkIfDwdjgajQYC8VioUCQRiIQh8PBwMhgLBQIg4FRba5TZ5LI/FYS/Hg5W2zmKvVoq1QoEykkWhD+eTmbjWZTCXiyVSmTiSSCGQh8PB2OBqNBgLxWKhQJBGIhCHw8HAyGAsFAiDgUCw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8PDAwPEhQUFQ0NDhESFRUUDg4PEhQVFRUOEBETFBUVFRARFBUVFRUVEhMUFRUVFRUUFRUVFRUVFRUVFRUVFRUVEAwLEBQZGxwNDQ4SFRwcGw4NEBQZHBwcDhATFhsdHRwRExkcHB4eHRQYGxwdHh4dGxwdHR4eHh4dHR0dHh4eHRALChAYKDM9DAwOExo6PDcODRAYKDlFOA4RFh0zV1A+EhYlOkRtZ00YIzdAUWhxXDFATldneXhlSFxfYnBkZ2MTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTEhIVGRoaGhoSFBYaGhoaGhUWGRoaGhoaGRoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhoaGhESFh8kJCQkEhQYIiQkJCQWGCEkJCQkJB8iJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCQREhgvY2NjYxIVGkJjY2NjGBo4Y2NjY2MvQmNjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRISEhUXGBkbEhIVFxgZGxwSFRcYGRscHRUXGBkbHB0dFxgZGxwdHR0YGRscHR0dHhkbHB0dHR4eGxwdHR0eHh4REREUFxocIBERFBcaHCAiERQXGhwgIiUUFxocICIlJRcaHCAiJSUlGhwgIiUlJSkcICIlJSUpKiAiJSUlKSoqEBAQFBgcICgQEBQYHCAoMBAUGBwgKDBAFBgcICgwQEAYHCAoMEBAQBwgKDBAQEBgICgwQEBAYIAoMEBAQGCAgAfF5cdH1e3Ow/L66wGmYnfIUbwdUTe3LMRbqON8B+5RJEvcGxkvrVUjTMrsXYhAnIwe0dTJfOYbWrDYyqUrz7dw/JO4hpmV2LsQQvkUeGq1BsZLx+cu5iV0e0eScJ91VIQYrmqfdVSK7GgjOU0oPaPOu5IcDK1mNvnD+K8LwS87f8Jx2mHtHnUkTGAurWZlNQa74ZLSFH9oF6FPGxzLsjQO5Qe0edcpttd7BXBSqMCL4k/4tFrHIPuEQ7m1/uIWkbDMWVoDdOSuRQ9286kvVUlQjzOE6VrNguN4oRXYGkgcnih7t13/9kxvLYKQezwLTrO44sVmMPgMqORo1E0sm1/9SludkcWHwfJwTSybR4LeAz6ugWVgRaY8mV/9SluQmtHrzsBtRF/wPY+X0JuYTs+ltgrXAmlk10xQHmTu9VSIAk1+vcvU4ml2oNzrNhEtQ3CysNP8UeR35wqpKUBdGdZMSjX4WVi8nJpdpHnbhzEIdx7mwf6W1FKAiucMXrWUWVjyRf23chNtR9mIzDoT/6ZLYailAjhFlZuvPtSeZ+2oREubDoWmT3TguY+JHPdRVSLKxfKH3vgNqJ/9emeEYikGXDFNzaLjvTeGAL61mogOoeG3y6oU4rW55ydoj0lUTSR/mmRhPmF86uwIfzp3FtiufQCmppaHDlGE0r2iTzXIw3zBq5hvaTldjG4CPb9wdxAme0SyedVKczJ9AtYbgPOzYKJvZZImsN7ecrxWZg5dR6ZLj/j4qpWsIA+vYwE+Tca9ounMIsrXMB4Stiib2SPQtZv+FVIpfEbzv8ncZoLBXc3YBqTG1HsskTTotZOYTG+oVUjLk6zhP8bg4RhMUNtfZdO7FdpBuXzhJ5Fh8IKlJG7wtD9ik8rWOJxy6iQ3NwzBpQ219mlyv+FLicYs2iJGSE0u2txzed++D61ZWCiHD/cZdQVCqkO2gJpdpNaObhnDfAPrT89RxdWFZ5hO3MseBSIlANppdZNIV/Rwe5eLTDvkfWKzFnH+QJ7m9QWV1KdwnuIwTNtZdJMoXBf74OhRnh2t+OTGL+AVUnIkyYY+QG7g9itHXyF3OIygG2s2kud679ZWKqSFa9n3IHD6MeLv1lZ0XyduRhiDRtrNnKoyiFVLcBm0ba5Yy3fQkDh4XsFE34isVpOzpa9nR8iCpS4HoxG2rJpnRhf3YboVa1PcRouh5LIJv/uQcPNd095ickTaiGBnWLKVWRc0OnYTSyex/n2FofEPnDG8y3PztHrzOLK1xo6RAml2k9owKajOC0Wr4D5x+3nA0UEhK2m198wuBHF3zlWWVKWLN1CHzLClUfuoYBcx4b1llpeBKmbayaR58njtE9onD66lUcsg0Spm2snsb+8HaJRn4dYcLbCuBuYwziB8/5U1C1DOOz2gZjSZtrLJk6vrLF3hwY4Io9xuT/ruUFRSBkNtUzTOWhjh26irLEPx4jPZL3Fo3QrReoGTTM21xYTT9oFdhTUIvjqTkfkvt0bzgVUjq/hOYY8j60IaO/0AzRBtqkTS6R5ellZd5uKdzzhb8BFlDdAcrwkE0rbXTOPB+7Y0FlZO96qFL4Ykg21StJs8qIW7h16H5hGiv8V2Cflau7QVDepTAHa6Lgt6feiEvJDM21StJsmOH/hynURrKxvUpQ8BH0JF7BiyG2qZpnL/7AOU66gt+reLEXY8pVOCQvSsBtqZTNM8bk9ohRcwD18o/WVkbvrceVKRb9I59IEKysjBeTMmmbA21xu/6iHadLRxuIzkLpi8wZYmmbbWi32RVAUjruxWlJ//iFxE38FI9hNKOoCdhwf5fDe4xZ81lgREhK2m1j78vW1CqkuMu/AjBNK210kzRUX/B+69cMMUG5bYrIeZxVSEZISmkzbXOi9yxwIfPgdsov7R71xuJ7rFcACjG/9PzApqFq7wEgzNJm2suWESPuwrQvejj7cbnQxMkxpm21lUYJL0fKmogPPqywn7e3FvB/FCNxPJ85iVUkCE9/tLKx31G4CgNtWTTPFhMvlu8G4/TrgaZttTChljfNJGgOT2X6EqpETy2tYd9cCBI4lIXJ1/3uVUllZEJz4baqGF64yxaZ+zPLYwde8Uqn1oKANtUrSaTOPHkhvuQP3bBlEJ/LFe4pqQOHUI8T8q7AXx3fLVBgSCVpMba55YxN3rv8U1Dv51bAPSOLlZWebkL8vSMGI21lJmmeVxPRwFlZF1CpqCN8uLwymaZyjbXHCRytogPN3o/n74CNykfT+qqRv5AQlHcRxYrC5KvGmbbUwmZY/29BvF6C1/93x4WVglXDLFpmbapmF89HKTogRwqqSlGbu+oiAkcWFbklC6Zhf+NtTLFpn8oWz+HsNRVSgIxZWON+yVyJlE5tq/+GWLTMutYX9ekTySEQPLVNQQ3OfycwJBM0zNtZcse7CvcKI0V/zh16Dr9OSA21MpmmcrHC+6pTAPHPwoit3LHHqs7jhFNRD6W8+EBGoSEoaZttTCZljfduH/fFisn+dRBGAZYtMzbVMwvul/T/crK1NQh8gN0SRRa9cOux6clC0/mDLFpmbarmF8/e6CopeOLCNW6S/IUUg3jJIYiAcDoMcGeRbOvuTPjXR/tyo79LK3kqqkbxkkMRAOB0GODPItnX3Jnxro/25Ud+llbyVVSN4ySGIgHA6DHBnkWzr7kz410f7cqO/Syt5KqpFVJwn6gBEvBM0zNtZcpGOEPiysW8vvRd2R0f7gtjhqUvXL+gWVwHm4XJDBiMpmmZtrLfPwd/IugP5+fKVSysH1EXreFAcEhelGmbbUmZY4Xdo1vQWVnK19P4RuEnbf0gQnR+lDCZlivNM22t1ESmopPIgfT0duOfQrsjgG4tPxli0zJmF5trdL1JDUIUT1ZXSqQDeR4B8mX3TrRro/2McGeUvLtwo6jIEKMkCUXWsLyZROd9P/rFYNtXPBli0z398iVUlVKAjFlY437JXImUTm2r/4ZYtMy61hf16RPJIU9nZ1MAAAAAAAAAAAAAvxNrLAEAAADASjJED0D/////////////////6AN2b3JiaXMNAAAATGF2ZjYwLjE2LjEwMAEAAAAfAAAAZW5jb2Rlcj1MYXZjNjAuMzEuMTAyIGxpYnZvcmJpcwEFdm9yYmlzKUJDVgEACAAAADFMIMWA0JBVAAAQAABgJCkOk2ZJKaWUoSh5mJRISSmllMUwiZiUicUYY4wxxhhjjDHGGGOMIDRkFQAABACAKAmOo+ZJas45ZxgnjnKgOWlOOKcgB4pR4DkJwvUmY26mtKZrbs4pJQgNWQUAAAIAQEghhRRSSCGFFGKIIYYYYoghhxxyyCGnnHIKKqigggoyyCCDTDLppJNOOumoo4466ii00EILLbTSSkwx1VZjrr0GXXxzzjnnnHPOOeecc84JQkNWAQAgAAAEQgYZZBBCCCGFFFKIKaaYcgoyyIDQkFUAACAAgAAAAABHkRRJsRTLsRzN0SRP8ixREzXRM0VTVE1VVVVVdV1XdmXXdnXXdn1ZmIVbuH1ZuIVb2IVd94VhGIZhGIZhGIZh+H3f933f930gNGQVACABAKAjOZbjKaIiGqLiOaIDhIasAgBkAAAEACAJkiIpkqNJpmZqrmmbtmirtm3LsizLsgyEhqwCAAABAAQAAAAAAKBpmqZpmqZpmqZpmqZpmqZpmqZpmmZZlmVZlmVZlmVZlmVZlmVZlmVZlmVZlmVZlmVZlmVZlmVZlmVZQGjIKgBAAgBAx3Ecx3EkRVIkx3IsBwgNWQUAyAAACABAUizFcjRHczTHczzHczxHdETJlEzN9EwPCA1ZBQAAAgAIAAAAAABAMRzFcRzJ0SRPUi3TcjVXcz3Xc03XdV1XVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVYHQkFUAAAQAACGdZpZqgAgzkGEgNGQVAIAAAAAYoQhDDAgNWQUAAAQAAIih5CCa0JrzzTkOmuWgqRSb08GJVJsnuamYm3POOeecbM4Z45xzzinKmcWgmdCac85JDJqloJnQmnPOeRKbB62p0ppzzhnnnA7GGWGcc85p0poHqdlYm3POWdCa5qi5FJtzzomUmye1uVSbc84555xzzjnnnHPOqV6czsE54Zxzzonam2u5CV2cc875ZJzuzQnhnHPOOeecc84555xzzglCQ1YBAEAAAARh2BjGnYIgfY4GYhQhpiGTHnSPDpOgMcgppB6NjkZKqYNQUhknpXSC0JBVAAAgAACEEFJIIYUUUkghhRRSSCGGGGKIIaeccgoqqKSSiirKKLPMMssss8wyy6zDzjrrsMMQQwwxtNJKLDXVVmONteaec645SGultdZaK6WUUkoppSA0ZBUAAAIAQCBkkEEGGYUUUkghhphyyimnoIIKCA1ZBQAAAgAIAAAA8CTPER3RER3RER3RER3RER3P8RxREiVREiXRMi1TMz1VVFVXdm1Zl3Xbt4Vd2HXf133f141fF4ZlWZZlWZZlWZZlWZZlWZZlCUJDVgEAIAAAAEIIIYQUUkghhZRijDHHnINOQgmB0JBVAAAgAIAAAAAAR3EUx5EcyZEkS7IkTdIszfI0T/M00RNFUTRNUxVd0RV10xZlUzZd0zVl01Vl1XZl2bZlW7d9WbZ93/d93/d93/d93/d939d1IDRkFQAgAQCgIzmSIimSIjmO40iSBISGrAIAZAAABACgKI7iOI4jSZIkWZImeZZniZqpmZ7pqaIKhIasAgAAAQAEAAAAAACgaIqnmIqniIrniI4oiZZpiZqquaJsyq7ruq7ruq7ruq7ruq7ruq7ruq7ruq7ruq7ruq7ruq7ruq7rukBoyCoAQAIAQEdyJEdyJEVSJEVyJAcIDVkFAMgAAAgAwDEcQ1Ikx7IsTfM0T/M00RM90TM9VXRFFwgNWQUAAAIACAAAAAAAwJAMS7EczdEkUVIt1VI11VItVVQ9VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV1TRN0zSB0JCVAAAZAABkwfcghBAOo9RCMEFozEEGqeSgQUml1daD5hAzjDnvlYSSSUo9WM5BxJDzICHHFGNKaSstZdQYwUDn3HHlEARCQ1YEAFEAAIAxhjHEGHLOScmkRM4xKZ2UyDlHpZPSSSktlhgzKSW2EmPknKPSScqklBhLix2lEmOJrQAAgAAHAIAAC6HQkBUBQBQAAGIMUgophZRSzinmkFLKMeUcUko5ppxTzjkIHYTKMQadgxAppRxTzinnHITMQeWcg9BBKAAAIMABACDAQig0ZEUAECcA4JAcz5M0SxQlSxNFTxRl1xNNV5Y0zRQ1UVRVyxNV1VRV2xZNVbYlTRNFTfRUVRNFVRVV05ZNVbVtzzRt2VRd3RZVVbdl2/Z9V7aF3zNNWRZV1dZN1bV115Z9X7Z1XZg0zTQ1UVRVTRRV1XRV3TZV17Y1UXRdUVVlWVRVWVZl2RZWWdZ9SxRV1VNN2RVVVZZV2fVtVZZ933RdXVdl2fdVWfZ12xeG5fZ9o6iqtm7Krq+rsuz7tm7zbd83Sppmmpoouqomiqprqqpum6pr25YoqqqoqrLsmaorq7Is7Kor27omiqorqqosi6oqy6rs+r4qy7otqqqtq7Ls66Yr+77u+9iy7hunquq6Ktu+scqyr+u+r7R13fc905Rl01V93VRVX5d13yjbujCMqqrrqiz7xirLvrD7PrrxE0ZV1XVVdoVdlW1f2I2dsPu+scy6zbh9XzluX1eW31jyhbi2LQyzbzNuXzf6xq8MxzLkmaZti66q66bq6sKs68Zv+7oxjKrq66os81VX9nXd9wm77hvD6Kq6sMqy76u27Pu67hvLb/y4ts23fZ8x27pP+I18X1jKti20hZ9y67qxDL+RrvwIAAAYcAAACDChDBQasiIAiBMAYBByTjEFoVIMQgchpQ5CSRVjEDLnpFTMQQmltBZCSa1iDELlmITMOSmhhJZCKS11EFIKpbQWSmkttRZrSi3GDkJKoZSWQimtpZZiTK3FWDEGIXNMSsaclFBKS6GU1jLnpHQOUuogpFRSaq2U1GLFmJQMOiqdg5JKKjGVlFoLpbRWSoqxpBRbazHW1mKtoZTWQimtlZRiTC3V1mKstWIMQuaYlIw5KaGUlkIpqVWMSemgo5I5KKmkFFspKcXMOSkdhJQ6CCmVVGIrKbUWSmmtpBRbKKXFFlutKbVWQymtlZRiLCnF2GKrtcVWYwchpVBKa6GU1lJrNabWYgyltFZSirGkFFuLsdbWYq2hlNZCKrGVklpMsdXYWqw1tRZjarHWFmOtMdbaY629p5RiTC3V2FqsOdbWY6019w5CSqGU1kIpraXWakytxRpKaa2kElsoqcUWW62txVhDKa2VlFosKcXYYqu1xVhrai3GFlutKbVYY649x1ZjT63F2GKstbVWa6w151hjrwUAAAw4AAAEmFAGCg1ZCQBEAQAQhCjFnJQGIceco5QgxJiDlCrHIJTSWsUclFJa65yT0lKMnYNSUoqxpNRajLWWlFqLsdYCAAAKHAAAAmzQlFgcoNCQlQBAFAAAYgxCjEFokFHKMQiNQUoxBiFSijHnpERKMeaclMwx5ySklDHnHJSUQgilpNJSCKGUlFIrAACgwAEAIMAGTYnFAQoNWREARAEAAMYgxhBjCDoHIZMSOcigdBAaCCGVTkpGpZRWWsuklJZKaxGETkpIKaNSWiupZZJKa6WVAgDADhwAwA4shEJDVgIAeQAAiDFKMeaccwYhpRxzzjmDkFLMOeecYoox5yCEUCnGmHMQQsgccw5CCCFkzDkHIYQQOucghFBCCJ1zEEIIIZTOOQghlFBK5xyEEEIppQAAoAIHAIAAG0U2JxgJKjRkJQCQBwAAGKOUc1JSapRiDEIqrUUKMQahpNYqxpyTklKMFWPOSUktxg5CKSm1VmsHoZSUWqu1lJJSbLXmXEppLcZac06txVhrrj2n1mKsNefcCwDAXXAAADuwUWRzgpGgQkNWAgB5AAAMQkoxxhhjSCnGGGOMMaSUYowxxphijDHGGHNOMcYYY4w5xxhjzDHnnGOMMcacc84xxhhzzjnnGGOMOeecc4wx55xzzjnGmHPOOeecAACgAgcAgAAbRTYnGAkqNGQlAJAHAAAQIqWUUkpppJRSSimlNFJKKaWUUkoppZRSSimllFJKKaWUUkoppZRSSimllFJKKaWUUkoppZRSSimllFJKKaWUUkopJYQQQgghhBAKAHG8cAD0mbBRZHOCkaBCQ1YCAKkAAIAxCjkFnYRUGqWcg5BKSik1SjknIaWUUquck5JSa7HFWDknJaXWWqyxk5JSi7XWmnMnJbUWY6215pBSjLXm2nPQIaUWa80159xLa7HmnHMPPpjYYq299557UDHWXIPuQQihYqw55xyED74AAJMIBwDEBRtWRzgpGgssNGQVABADADAEAEEomgEAgAkOAAABVrArs7Rqo7ipk7zog8AndMRmZMilVMzkRNAjNdRiJdihFdzgBWChISsBADIAAARqzLXHWCPEmINUWi4VUgpKib1USikHoeWaKYWUcpZLx5hijFGsJXRIGQSthNAphYiilloroUPIScoxxtYpBgAAgCAAwECEzAQCBVBgIAMADhASpACAwgJDx3AREJBLyCgwKBwTzkmnDQBAECIzRCJiMUhMqAaKiukAYHGBIR8AMjQ20i4uoMsAF3Rx14EQghCEIBYHUEACDk644Yk3POEGJ+gUlToIAAAAAAAEAHgAAEg2gIiIaOY4Ojw+QEJERkhKTE5QAgAAAAAACAA+AACSFCAiIpo5jg6PD5AQkRGSEpMTlAAAAAAAAAAAACAgIAAAAAAAEAAAACAgT2dnUwAAQAAAAAAAAADuj9WhAgAAAEDYmANB/////////////////////////////////////////////////////////////////////////////////////xMisbQPvfc+997733vue77x9z//++9978PR94+3Vmn3j6B94+8eRYGn3j723ur3V77UVQBVAFUFfhl/ePvH3gPvH3j7x94+8feA+8fePvAfePvH3j7wH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fe194+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+8fePvH3j7x94+PSU+8fePvH3j7x9YzT7x9496Q3gfePvH3j7x99MNyn3j7x94lOKZT7x94+8feboDKfePpfeP//33j7x94+8fePvH3tfePvH3j8OuAABo0AUaK1r8PYNAGjQaAAAAAAAAAAAAAAAAAAAAAAABqgANBVAAVRQFVQBVUgAAAIgjBg5AToQPRgUIGywFXAF5fyPjH4DPmvCTCEPryLEEhCPDhUIG+L8B0iIbea9escgIkpHpwrEGuYAvuAp/RhV/wXx5wB/n7kDp0iAgAHgwKHICb6svCCB1Ek+E6B1aNBoeifEI9WhzywG/Ar98BsjywG/Mv9y8WLzFR+8w+BD8a+LB1SeSeri+QYh/hXd3w+dvq+b5vw1c/zUDJ8AgJH0vScvi0f8SADyT3WH90Z/IIh4Fft+0RCCo7RjjUYZXkjSpb2FBNhhxWXGszKtdAzgc++nPPwoodRZXhZXpD4EPwY/MeM8z2/79mn7MIc4HCH7P7XSen1sxg/VPgAervfA5jqzD5nn6XZHu93u93u93u93u93u9yAIB7vd7vd7vdJJIRkRlJJJJJJsyAGSSSTFixQK3FixYsWLFixYsWLFixOWLFixYsWLFixYsWKTFMWLFixYsWLFixYpJixYsWLFi8khuaEckkkkkk4hCJJJJJJEIwgskkkkklt77MWLF7PZ7PZ7PZ7PZ3oU32ez2W2226gpDbbbbbalOedtttttoUuOPHjx48ePHjx48eOY8ePHjx48ePHjx48ePY8M2PHjx48ePHjx48b63e48ePHjx+W0+hSG222221Ifd7vd7vdhbu93u3PHu93u93u93u93u929490eXmSSSSQRCz55MKs5JJJJM85C2xYpMWLFixYlmLFFxYsWLF6cKG870dVbxr6MWLFixYrcR4W7JPJJJNuyTZskkkkk27JNmySSSSQRCzknOSSTF7PZixc0ez2aNPs9ns9ns9lvs9mrVb/+i22220nTbp02222269VuvTbbcePHjx4SXHjZsePHjx48ePHjZseNk48ePHjx48ePH3uPHhcDHjx48ePHjx4xEQNWMydVvlttunTbq0=", + "tail_b64": "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", + "url": "https://samplefile.com/samples/download/video/ogv/ogv_30s_sample_file_2.4MB.ogv/", + "mime_type": "video/ogg", + "file_size": 2467166, + "width": 640, + "height": 480, + "duration": 30.0, + "fps": 25.0, + "sample_rate": 44100, + "bitrate_avg": 642, + "format": "OGV" + }, + "mp3_speech_10m": { + "head_b64": "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", + "url": "https://samplelib.com/mp3/sample-speech-10m.mp3", + "mime_type": "audio/mpeg", + "file_size": 9601402, + "duration": 600.0, + "sample_rate": 22050, + "bitrate_nominal": 128, + "bitrate_avg": 125, + "format": "MP3" + }, + "mp3_den_pobedy": { + "head_b64": "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", + "tail_b64": "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", + "url": "https://rus.hitmoz.org/get/music/20250302/Lev_Leshhenko_-_Den_Pobedy_79068553.mp3", + "mime_type": "audio/mpeg", + "file_size": 9387073, + "duration": 234.0, + "sample_rate": 44100, + "bitrate_nominal": 320, + "bitrate_avg": 313, + "format": "MP3" + }, + "aac_15s": { + "head_b64": "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", + "url": "https://samplefile.com/samples/download/audio/aac/aac_15s_sample_file_142KB.aac/", + "mime_type": "audio/aac", + "file_size": 144628, + "duration": 14.0, + "sample_rate": 44100, + "bitrate_nominal": 128, + "bitrate_avg": 79, + "format": "AAC" + }, + "aac_60s": { + "head_b64": "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", + "url": "https://samplefile.com/samples/download/audio/aac/aac_60s_sample_file_581KB.aac/", + "mime_type": "audio/aac", + "file_size": 594437, + "duration": 59.0, + "sample_rate": 44100, + "bitrate_nominal": 128, + "bitrate_avg": 79, + "format": "AAC" + }, + "wav": { + "head_b64": "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", + "url": "https://thetestdata.com/assets/audio/wav/thetestdata-sample-wav-1.wav", + "mime_type": "audio/x-wav", + "file_size": 39781518, + "duration": 113.0, + "sample_rate": 44100, + "bitrate_nominal": 2822, + "bitrate_avg": 2756, + "format": "WAV" + }, + "wma": { + "head_b64": "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", + "url": "https://thetestdata.com/assets/audio/wma/thetestdata-sample-wma-1.wma", + "mime_type": "audio/x-ms-wma", + "file_size": 1946142, + "duration": 113.0, + "sample_rate": 44100, + "bitrate_nominal": 128, + "bitrate_avg": 135, + "format": "WMA" + }, + "flac_24bit": { + "head_b64": "ZkxhQwAAACIQABAAACqFADzULuADcAANfQBkdOqozAmNUk+1LdbaAq2xAwAAEgAAAAAAAAAAAAAAAAAAAAAQAAQAACggAAAAcmVmZXJlbmNlIGxpYkZMQUMgMS4yLjEgMjAwNzA5MTcAAAAAgQAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=", + "url": "https://samples.ffmpeg.org/flac/24-bit_192kHz.flac", + "mime_type": "audio/flac", + "file_size": 2901817, + "duration": 4.6, + "sample_rate": 192000, + "bitrate_avg": 4924, + "format": "FLAC" + }, + "flac_large_metadata": { + "head_b64": "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", + "url": "https://samples.ffmpeg.org/flac/larger_than_64k_flac_metadata.flac", + "mime_type": "audio/flac", + "file_size": 12916014, + "duration": 169.0, + "sample_rate": 44100, + "bitrate_avg": 598, + "format": "FLAC" + }, + "webm_10s": { + "head_b64": "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", + "url": "https://samplelib.com/webm/sample-10s.webm", + "mime_type": "video/webm", + "file_size": 12437421, + "width": 1920, + "height": 1080, + "duration": 10.0, + "fps": 29.97, + "sample_rate": 48000, + "bitrate_avg": 9717, + "format": "WEBM" + }, + "webm_big_buck_bunny": { + "head_b64": "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", + "url": "https://upload.wikimedia.org/wikipedia/commons/transcoded/c/c0/Big_Buck_Bunny_4K.webm/Big_Buck_Bunny_4K.webm.1080p.vp9.webm", + "mime_type": "video/webm", + "file_size": 300466178, + "width": 1920, + "height": 1080, + "duration": 635.0, + "fps": 60.0, + "sample_rate": 48000, + "bitrate_avg": 3697, + "format": "WEBM" + }, + "mkv_h264": { + "head_b64": "GkXfo6NChoEBQveBAULygQRC84EIQoKIbWF0cm9za2FCh4EEQoWBAhhTgGcBAAAACeiExxFNm3TCv4SG/HdzTbuLU6uEFUmpZlOsgaFNu4tTq4QWVK5rU6yB8U27jFOrhBJUw2dTrIIO4027jlOrhBxTu2tTrIQJ6IRv7AEAAAAAAABRAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFUmpZsu/hI7OUxUq17GDD0JATYCNTGF2ZjYwLjE2LjEwMFdBjUxhdmY2MC4xNi4xMDBzpJDlXpnAKfMFFBUlZNUMVOECRImIQNJIwAAAAAAWVK5rTey/hIkWAPKuAQAAAAAAAKHXgQFzxYi9uTvW5XYFZJyBACK1nIN1bmSGj1ZfTVBFRzQvSVNPL0FWQ4OBASPjg4QBMS0A4Kawgg8AuoIIcJqBAlWwmFW6gQFVsYEBVbuBAVW5gQFVt4EBVbiBAlXugQDsAQAAAAAAAAIAAGOiswF6ADT/4QAeZ3oANLzZQDwAQ+wFqAgICgAAAwACAAADAMgeMGMsAQAGaOvgvLIs/vj4AK4BAAAAAAANM9eBAnPFiPOjj873RocNnIEAIrWcg3VuZIaIQV9WT1JCSVODgQLhkZ+BAbWIQOdwAAAAAABiZIEgVe6BAGOiTPMCHl0Bdm9yYmlzAAAAAAGAuwAAAAAAAIA4AQAAAAAAuAEDdm9yYmlzNAAAAFhpcGguT3JnIGxpYlZvcmJpcyBJIDIwMjAwNzA0IChSZWR1Y2luZyBFbnZpcm9ubWVudCkBAAAAFQAAAGVuY29kZXI9TGF2YzYwLjMxLjEwMgEFdm9yYmlzIkJDVgEAQAAAJHMYKkalcxaEEBpCUBnjHELOa+wZQkwRghwyTFvLJXOQIaSgQohbKIHQkFUAAEAAAIdBeBSEikEIIYQlPViSgyc9CCGEiDl4FIRpQQghhBBCCCGEEEIIIYRFOWiSgydBCB2E4zA4DIPlOPgchEU5WBCDJ0HoIIQPQriag6w5CCGEJDVIUIMGOegchMIsKIqCxDC4FoQENSiMguQwyNSDC0KImoNJNfgahGdBeBaEaUEIIYQkQUiQgwZByBiERkFYkoMGObgUhMtBqBqEKjkIH4QgNGQVAJAAAKCiKIqiKAoQGrIKAMgAABBAURTHcRzJkRzJsRwLCA1ZBQAAAQAIAACgSIqkSI7kSJIkWZIlWZIlWZLmiaosy7Isy7IsyzIQGrIKAEgAAFBRDEVxFAcIDVkFAGQAAAigOIqlWIqlaIrniI4IhIasAgCAAAAEAAAQNENTPEeURM9UVde2bdu2bdu2bdu2bdu2bVuWZRkIDVkFAEAAABDSaWapBogwAxkGQkNWAQAIAACAEYowxIDQkFUAAEAAAIAYSg6iCa0535zjoFkOmkqxOR2cSLV5kpuKuTnnnHPOyeacMc4555yinFkMmgmtOeecxKBZCpoJrTnnnCexedCaKq0555xxzulgnBHGOeecJq15kJqNtTnnnAWtaY6aS7E555xIuXlSm0u1Oeecc84555xzzjnnnOrF6RycE84555yovbmWm9DFOeecT8bp3pwQzjnnnHPOOeecc84555wgNGQVAAAEAEAQho1h3CkI0udoIEYRYhoy6UH36DAJGoOcQurR6GiklDoIJZVxUkonCA1ZBQAAAgBACCGFFFJIIYUUUkghhRRiiCGGGHLKKaeggkoqqaiijDLLLLPMMssss8w67KyzDjsMMcQQQyutxFJTbTXWWGvuOeeag7RWWmuttVJKKaWUUgpCQ1YBACAAAARCBhlkkFFIIYUUYogpp5xyCiqogNCQVQAAIACAAAAAAE/yHNERHdERHdERHdERHdHxHM8RJVESJVESLdMyNdNTRVV1ZdeWdVm3fVvYhV33fd33fd34dWFYlmVZlmVZlmVZlmVZlmVZliA0ZBUAAAIAACCEEEJIIYUUUkgpxhhzzDnoJJQQCA1ZBQAAAgAIAAAAcBRHcRzJkRxJsiRL0iTN0ixP8zRPEz1RFEXTNFXRFV1RN21RNmXTNV1TNl1VVm1Xlm1btnXbl2Xb933f933f933f933f931dB0JDVgEAEgAAOpIjKZIiKZLjOI4kSUBoyCoAQAYAQAAAiuIojuM4kiRJkiVpkmd5lqiZmumZniqqQGjIKgAAEABAAAAAAAAAiqZ4iql4iqh4juiIkmiZlqipmivKpuy6ruu6ruu6ruu6ruu6ruu6ruu6ruu6ruu6ruu6ruu6ruu6rguEhqwCACQAAHQkR3IkR1IkRVIkR3KA0JBVAIAMAIAAABzDMSRFcizL0jRP8zRPEz3REz3TU0VXdIHQkFUAACAAgAAAAAAAAAzJsBTL0RxNEiXVUi1VUy3VUkXVU1VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVU3TNE0TCA1ZCQAAAQDQWnPMrZeOQeisl8gopKDXTjnmpNfMKIKc5xAxY5jHUjFDDMaWQYSUBUJDVgQAUQAAgDHIMcQccs5J6iRFzjkqHaXGOUepo9RRSrGmWjtKpbZUa+Oco9RRyiilWkurHaVUa6qxAACAAAcAgAALodCQFQFAFAAAgQxSCimFlGLOKeeQUso55hxiijmnnGPOOSidlMo5J52TEimlnGPOKeeclM5J5pyT0kkoAAAgwAEAIMBCKDRkRQAQJwDgcBxNkzRNFCVNE0VPFF3XE0XVlTTNNDVRVFVNFE3VVFVZFk1VliVNM01NFFVTE0VVFVVTlk1VtWXPNG3ZVFXdFlXVtmVb9n1XlnXdM03ZFlXVtk1VtXVXlnVdtm3dlzTNNDVRVFVNFFXXVFXbNlXVtjVRdF1RVWVZVFVZdl1Z11VX1n1NFFXVU03ZFVVVllXZ1WVVlnVfdFXdVl3Z11VZ1n3b1oVf1n3CqKq6bsqurquyrPuyLvu67euUSdNMUxNFVdVEUVVNV7VtU3VtWxNF1xVV1ZZFU3VlVZZ9X3Vl2ddE0XVFVZVlUVVlWZVlXXdlV7dFVdVtVXZ933RdXZd1XVhmW/eF03V1XZVl31dlWfdlXcfWdd/3TNO2TdfVddNVdd/WdeWZbdv4RVXVdVWWhV+VZd/XheF5bt0XnlFVdd2UXV9XZVkXbl832r5uPK9tY9s+sq8jDEe+sCxd2za6vk2Ydd3oG0PhN4Y007Rt01V13XRdX5d13WjrulBUVV1XZdn3VVf2fVv3heH2fd8YVdf3VVkWhtWWnWH3faXuC5VVtoXf1nXnmG1dWH7j6Py+MnR1W2jrurHMvq48u3F0hj4CAAAGHAAAAkwoA4WGrAgA4gQAGIScQ0xBiBSDEEJIKYSQUsQYhMw5KRlzUkIpqYVSUosYg5A5JiVzTkoooaVQSkuhhNZCKbGFUlpsrdWaWos1hNJaKKW1UEqLqaUaW2s1RoxByJyTkjknpZTSWiiltcw5Kp2DlDoIKaWUWiwpxVg5JyWDjkoHIaWSSkwlpRhDKrGVlGIsKcXYWmy5xZhzKKXFkkpsJaVYW0w5thhzjhiDkDknJXNOSiiltVJSa5VzUjoIKWUOSiopxVhKSjFzTkoHIaUOQkolpRhTSrGFUmIrKdVYSmqxxZhzSzHWUFKLJaUYS0oxthhzbrHl1kFoLaQSYyglxhZjrq21GkMpsZWUYiwp1RZjrb3FmHMoJcaSSo0lpVhbjbnGGHNOseWaWqy5xdhrbbn1mnPQqbVaU0y5thhzjrkFWXPuvYPQWiilxVBKjK21WluMOYdSYisp1VhKirXFmHNrsfZQSowlpVhLSjW2GGuONfaaWqu1xZhrarHmmnPvMebYU2s1txhrTrHlWnPuvebWYwEAAAMOAAABJpSBQkNWAgBRAAAEIUoxBqFBiDHnpDQIMeaclIox5yCkUjHmHIRSMucglJJS5hyEUlIKpaSSUmuhlFJSaq0AAIACBwCAABs0JRYHKDRkJQCQCgBgcBzL8jxRNFXZdizJ80TRNFXVth3L8jxRNE1VtW3L80TRNFXVdXXd8jxRNFVVdV1d90RRNVXVdWVZ9z1RNFVVdV1Z9n3TVFXVdWVZtoVfNFVXdV1ZlmXfWF3VdWVZtnVbGFbVdV1Zlm1bN4Zb13Xd94VhOTq3buu67/vC8TvHAADwBAcAoAIbVkc4KRoLLDRkJQCQAQBAGIOQQUghgxBSSCGlEFJKCQAAGHAAAAgwoQwUGrISAIgCAAAIkVJKKY2UUkoppZFSSimllBJCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCAUA+E84APg/2KApsThAoSErAYBwAADAGKWYcgw6CSk1jDkGoZSUUmqtYYwxCKWk1FpLlXMQSkmptdhirJyDUFJKrcUaYwchpdZarLHWmjsIKaUWa6w52BxKaS3GWHPOvfeQUmsx1lpz772X1mKsNefcgxDCtBRjrrn24HvvKbZaa809+CCEULHVWnPwQQghhIsx99yD8D0IIVyMOecehPDBB2EAAHeDAwBEgo0zrCSdFY4GFxqyEgAICQAgEGKKMeecgxBCCJFSjDnnHIQQQiglUoox55yDDkIIJWSMOecchBBCKKWUjDHnnIMQQgmllJI55xyEEEIopZRSMueggxBCCaWUUkrnHIQQQgillFJK6aCDEEIJpZRSSikhhBBCCaWUUkopJYQQQgmllFJKKaWEEEoopZRSSimllBBCKaWUUkoppZQSQiillFJKKaWUkkIppZRSSimllFJSKKWUUkoppZRSSgmllFJKKaWUlFJJBQAAHDgAAAQYQScZVRZhowkXHoBCQ1YCAEAAABTEVlOJnUHMMWepIQgxqKlCSimGMUPKIKYpUwohhSFziiECocVWS8UAAAAQBAAICAkAMEBQMAMADA4QPgdBJ0BwtAEACEJkhkg0LASHB5UAETEVACQmKOQCQIXFRdrFBXQZ4IIu7joQQhCCEMTiAApIwMEJNzzxhifc4ASdolIHAQAAAABgAAAPAADHBRAR0RxGhsYGR4fHB0hIAAAAAAC4AMAHAMAhAkRENIeRobHB0eHxARISAAAAAAAAAAAABAQEAAAAAAACAAAABAQSVMNnSJ2/hKNEBtdzc0dnY8CAZ8iVRaOLTUFKT1JfQlJBTkREh4RxdCAgZ8icRaONTUlOT1JfVkVSU0lPTkSHiTUzNzk4NjgxNmfIn0WjkUNPTVBBVElCTEVfQlJBTkRTRIeIcXQgIHBhbmFnyEbqRaOjQ09NLlBBTkFTT05JQy5TRU1JLVBSTy5NRVRBREFUQS5YTUxEh0bAPD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiIHN0YW5kYWxvbmU9Im5vIiA/Pgo8Q2xpcE1haW4geG1sbnM6eHNpPSJodHRwOi8vd3d3LnczLm9yZy8yMDAxL1hNTFNjaGVtYS1pbnN0YW5jZSIgeG1sbnM9InVybjpzY2hlbWFzLVByb2Zlc3Npb25hbC1QbHVnLWluOlNlbWktUHJvOkNsaXBNZXRhZGF0YTp2MS4wIj4KICA8Q2xpcENvbnRlbnQ+CiAgICA8R2xvYmFsQ2xpcElEPjA2MEEyQjM0MDEwMTAxMDUwMTAxMEQyMTEzMDAwMDAwOUY1RjdBOUMwQTAwMDAwNDdEMkExNDkxMTQ5MzAwODI8L0dsb2JhbENsaXBJRD4KICAgIDxEdXJhdGlvbj45MzY8L0R1cmF0aW9uPgogICAgPEVkaXRVbml0PjEvNTA8L0VkaXRVbml0PgogICAgPEVzc2VuY2VMaXN0PgogICAgICA8VmlkZW8+CiAgICAgICAgPENvZGVjIEJpdFJhdGU9IjIwMCI+SDI2NF80MjJfTG9uZ0dPUDwvQ29kZWM+CiAgICAgICAgPEFjdGl2ZUxpbmU+MjE2MDwvQWN0aXZlTGluZT4KICAgICAgICA8QWN0aXZlUGl4ZWw+Mzg0MDwvQWN0aXZlUGl4ZWw+CiAgICAgICAgPEJpdERlcHRoPjEwPC9CaXREZXB0aD4KICAgICAgICA8RnJhbWVSYXRlPjUwcDwvRnJhbWVSYXRlPgogICAgICAgIDxUaW1lY29kZVR5cGU+Tm9uRHJvcDwvVGltZWNvZGVUeXBlPgogICAgICAgIDxTdGFydFRpbWVjb2RlPjA2OjQxOjQ2OjE0PC9TdGFydFRpbWVjb2RlPgogICAgICA8L1ZpZGVvPgogICAgICA8QXVkaW8+CiAgICAgICAgPENoYW5uZWw+NDwvQ2hhbm5lbD4KICAgICAgICA8U2FtcGxpbmdSYXRlPjQ4MDAwPC9TYW1wbGluZ1JhdGU+CiAgICAgICAgPEJpdHNQZXJTYW1wbGU+MjQ8L0JpdHNQZXJTYW1wbGU+CiAgICAgIDwvQXVkaW8+CiAgICA8L0Vzc2VuY2VMaXN0PgogICAgPENsaXBNZXRhZGF0YT4KICAgICAgPFJhdGluZz4wPC9SYXRpbmc+CiAgICAgIDxBY2Nlc3M+CiAgICAgICAgPENyZWF0aW9uRGF0ZT4yMDIzLTEyLTA0VDE4OjM4OjU0KzAxOjAwPC9DcmVhdGlvbkRhdGU+CiAgICAgICAgPExhc3RVcGRhdGVEYXRlPjIwMjMtMTItMDRUMTg6Mzg6NTQrMDE6MDA8L0xhc3RVcGRhdGVEYXRlPgogICAgICA8L0FjY2Vzcz4KICAgICAgPERldmljZT4KICAgICAgICA8TWFudWZhY3R1cmVyPlBhbmFzb25pYzwvTWFudWZhY3R1cmVyPgogICAgICAgIDxNb2RlbE5hbWU+REMtUzVNMjwvTW9kZWxOYW1lPgogICAgICA8L0RldmljZT4KICAgICAgPFNob290PgogICAgICAgIDxTdGFydERhdGU+MjAyMy0xMi0wNFQxODozODo1NCswMTowMDwvU3RhcnREYXRlPgogICAgICA8L1Nob290PgogICAgPC9DbGlwTWV0YWRhdGE+CiAgPC9DbGlwQ29udGVudD4KICA8VXNlckFyZWE+CiAgICA8QWNxdWlzaXRpb25NZXRhZGF0YSB4bWxucz0idXJuOnNjaGVtYXMtUHJvZmVzc2lvbmFsLVBsdWctaW46UDI6Q2FtZXJhTWV0YWRhdGE6djEuMiI+CiAgICAgIDxDYW1lcmFVbml0TWV0YWRhdGE+CiAgICAgICAgPElTT1NlbnNpdGl2aXR5PjI1MDA8L0lTT1NlbnNpdGl2aXR5PgogICAgICAgIDxHYW1tYT4KICAgICAgICAgIDxDYXB0dXJlR2FtbWE+U1RBTkRBUkQ8L0NhcHR1cmVHYW1tYT4KICAgICAgICA8L0dhbW1hPgogICAgICAgIDxHYW11dD4KICAgICAgICAgIDxDYXB0dXJlR2FtdXQ+QlQuNzA5PC9DYXB0dXJlR2FtdXQ+CiAgICAgICAgPC9HYW11dD4KICAgICAgPC9DYW1lcmFVbml0TWV0YWRhdGE+CiAgICA8L0FjcXVpc2l0aW9uTWV0YWRhdGE+CiAgPC9Vc2VyQXJlYT4KPC9DbGlwTWFpbj4KZ8iaRaOHRU5DT0RFUkSHjUxhdmY2MC4xNi4xMDBzc0CRY8CLY8WIvbk71uV2BWRnyJtFo4lWRU5ET1JfSUREh4xbMF1bMF1bMF1bMF1nyJlFo4hUSU1FQ09ERUSHizA2OjQxOjQ2OjI4Z8iiRaOHRU5DT0RFUkSHlUxhdmM2MC4zMS4xMDIgbGlieDI2NGfIoUWjiERVUkFUSU9ORIeTMDA6MDA6MTguNzIzMDAwMDAwAHNzQJNjwItjxYjzo4/O90aHDWfIm0WjiVZFTkRPUl9JRESHjFswXVswXVswXVswXWfImUWjiFRJTUVDT0RFRIeLMDY6NDE6NDY6MjhnyKRFo4dFTkNPREVSRIeXTGF2YzYwLjMxLjEwMiBsaWJ2b3JiaXNnyKFFo4hEVVJBVElPTkSHkzAwOjAwOjE4LjcyMzAwMDAwMAAfQ7Z1EFGgAL+EQRAeQOeBA6MpXeGBAACAAAACrwYF//+r3EXpvebZSLeWLNgg2SPu73gyNjQgLSBjb3JlIDE2NCByMzEwOCAzMWUxOWY5IC0gSC4yNjQvTVBFRy00IEFWQyBjb2RlYyAtIENvcHlsZWZ0IDIwMDMtMjAyMyAtIGh0dHA6Ly93d3cudmlkZW9sYW4ub3JnL3gyNjQuaHRtbCAtIG9wdGlvbnM6IGNhYmFjPTEgcmVmPTMgZGVibG9jaz0xOjA6MCBhbmFseXNlPTB4MzoweDExMyBtZT1oZXggc3VibWU9NyBwc3k9MSBwc3lfcmQ9MS4wMDowLjAwIG1peGVkX3JlZj0xIG1lX3JhbmdlPTE2IGNocm9tYV9tZT0xIHRyZWxsaXM9MSA4eDhkY3Q9MSBjcW09MCBkZWFkem9uZT0yMSwxMSBmYXN0X3Bza2lwPTEgY2hyb21hX3FwX29mZnNldD0tMiB0aHJlYWRzPTM2IGxvb2thaGVhZF90aHJlYWRzPTYgc2xpY2VkX3RocmVhZHM9MCBucj0wIGRlY2ltYXRlPTEgaW50ZXJsYWNlZD0wIGJsdXJheV9jb21wYXQ9MCBjb25zdHJhaW5lZF9pbnRyYT0wIGJmcmFtZXM9MyBiX3B5cmFtaWQ9MiBiX2FkYXB0PTEgYl9iaWFzPTAgZGlyZWN0PTEgd2VpZ2h0Yj0xIG9wZW5fZ29wPTAgd2VpZ2h0cD0yIGtleWludD0yNTAga2V5aW50X21pbj0yNSBzY2VuZWN1dD00MCBpbnRyYV9yZWZyZXNoPTAgcmNfbG9va2FoZWFkPTQwIHJjPWNyZiBtYnRyZWU9MSBjcmY9MTUuMCBxY29tcD0wLjYwIHFwbWluPTAgcXBtYXg9NjkgcXBzdGVwPTQgaXBfcmF0aW89MS40MCBhcT0xOjEuMDAAgAAJWyZliIQBf/b/Ak8UGuC6W1zmuvptSabScQYd+Xqb6/OtTrVnBpFQMrCd8vITHMxsJ+VnnXAHhpaTdzDl31UJPUkdZZjiLgCgaO6cttLIz64mbvgWsgcdy91AxjcN5LyVEkRK0sat7huOxM1m2r6cM9hk+FiFTT2rGcH1P6CdFz6pCTzEI8VevL3znOO/EYDx2hnCVVhY40/0SytUMpu83cRtasq2biHu6EeD9gG5pCSAX8vX9wlslu8KFzkuvFBavTTdXwYViUcUtV6eeUApxwzT/52qZMX9P321gndPNqvltfWPuQOgy9JSg8FMnRXLuUSjgKU3+CzAE6sGLdd73UOc6MWJ2doQBEPdAFAdMgPvG7KMa+xo+cK+Uf3sr4Rbgnp9ryjgdDsubWqGc1A1bWnCX5ympy0wsW3TP5EJEo4+bU9BSBNGbW4HcGFqSSquU/gN4hqsCtQTq1YN/Gw9KPCGaaF8sMliY8wvViV3w2IShTXlu/gD6zb2EgDQaYzy/7smOg+NhnL9Blk17DqO9EOkRBhxt/6WgyXPYwhGOpwU2DbFf1PMXyOSyW0eNwT5tXu+Zs53QZpAjftPccg4yfSbwuLzZH724Hr/KVSLduIkgHXlJgEoIniyeFKi128yIBZlU0nicl8auKkV9dcKqRk+kqaP3GVaq9PXlKfqFm6GpWukm8wJ24Y8m3jHHel+eP94AobOtnl0i2OY7KQgkwoPpchLn00HY/t1vlgbsDEuD5qR9pon+DieAcHSNQFcmm5WXHXbGVqhBXdJl4kT3Cte93bAnMX9GwWAehv3iYReLl+NTU9bvFWq4smqNLjQumnhH9a6yaEHPnaWJ+4mb5qp8IVTHjLjXFBPTSfmRdYQUGC35nH7v1A4OvGTHWXlDaZBx10OxgMsGkNXXYVOP7slOxfkllIXxc7CopBxhNIexozHYWBfkuJqwEdvqSnVoGv9tEdJVajcQ2Ftf57ew5eRTDTf+20cZq/SqMMkSIPrkkMWoIXKdaACMe3bkzJDMhLQpvkudN+YJbUfq1hItnVAoOipHf4BzNMREgXhz5ZgC+nIUsEKqryzufh8V6XaVsQtjbgByQfDhto642QJiMqOAKrnmCJMNoP2m/T4Pm0kUvPCvOx0jzl2oUlOIdQ45g7MLBiJ358BvO2sWdXHDIUt7n+EuQIL2ZRbIjv/V8/Mv1RN+n1paxgXL7h+tYUPIqdPWhuWxs0wsTnhSsrYaSgRb+bVHb9uWcm3XwE/mH+n/Zs5EyLf88LKDiGlicmcuBNfwruUY0+ZLz1tm8qaXCVa9AS7WQxsjJUSRzGvctlgtuuS1ou5zK0wZpNUt2H+lKh3ifs0A9M8MQTclyIhog0C9Jx/+/3Z39xi6kg6nOItmbmiqrzqsyTF03V1jTW/IK6zUV0OAoLhSdvjHAroeqhNZ98cDtblxtR6/haXxs7x1T83ZgjKt/HE5oMVHmwGci1WRUIkmS142BicFxpNnoX628gsWUCaSsOfACrQlswzW14ucQGJuhrZC405eiIHn+keNrYkbhlyQ+vC5lfHUA075NVxsRMIlPhLSFgdaR89cYRleTscAeVVz/jEv+cu6yIOjxuUNL9d4ph1ysWpWWpFdx5wWoLoCqHQOQqdVoHJaCRYnEmMYO7Jbm+RYferDAhSNeO4r44o57wZrRhuMIg2t8fGCPubwt2jipMj6Q4zZ3bbpL3N5dsGGw+9WUo1/jWIaSCqzG/IQ+J+fbEQ7man5EZpsvpcau1BvFqqEG195JOEfvufM09ojoc+yozZCXTyUMxsd6PXXc/zSziTVDtoUogmGTZhRCJ3tsNTF9l/yN4EPaYRgDitKRhWq5CVZGbYtx40Sbo=", + "url": "https://hitokageproduction.com/files/videoSamples/h264.mkv", + "mime_type": "video/x-matroska", + "file_size": 166233339, + "width": 3840, + "height": 2160, + "duration": 19.0, + "fps": 50.0, + "sample_rate": 48000, + "bitrate_avg": 68353, + "format": "MKV" + }, + "mkv_h265": { + "head_b64": "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", + "url": "https://hitokageproduction.com/files/videoSamples/h265.mkv", + "mime_type": "video/x-matroska", + "file_size": 80652384, + "width": 3840, + "height": 2160, + "duration": 19.0, + "fps": 50.0, + "sample_rate": 48000, + "bitrate_avg": 33163, + "format": "MKV" + }, + "avi_1": { + "head_b64": "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", + "url": "https://hitokageproduction.com/files/videoSamples/avi.avi", + "mime_type": "video/x-msvideo", + "file_size": 46984694, + "width": 3840, + "height": 2160, + "duration": 18.0, + "fps": 100.0, + "sample_rate": 48000, + "bitrate_avg": 20393, + "format": "AVI" + }, + "mp4_15s": { + "head_b64": "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", + "tail_b64": "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", + "url": "file://sample-15s.mp4", + "mime_type": "video/mp4", + "file_size": 11916526, + "width": 1920, + "height": 1080, + "duration": 16.0, + "bitrate_avg": 5948, + "format": "MP4" + }, + "ogg_audio": { + "head_b64": "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", + "tail_b64": "DQCACwRyeQl0kPkiRYVKk0AEAAAAAAAAAABA9dV7HUa93j+HDqA5/udKsRlGNKm6rkAA0IIiv9kfor+1O1kRhbk/txzfat1WjPHo7P4vdz9vf9//FEAVjLOrf6pvXX++BkggrgPQSrL3mQKsN2cAAKit/FIAQAjQAMzjMa+sdWAgPX33UT8FzNn++zhH3mwP4CoefDWfYYSdNbD+jkPiinm/VgsA+iCpR3IVAAQHQFMelqz0S3J/mCV41eMQKkhGvpKF+JI0d0bwEU+iYxUWgRUAALvkGtA5o6QySIQQAAAAAAAAAKANsOAGQF4ngPDsw74CQE7dGe0fd7uZbNka/917WkhNfHv3xMenlRkhMx4ZXVP5qJyB2vcvVXZ9P6/n8+//jcd3GQropIrBw+dfdRumOcyx38oBqJt3XwBKzgOAAUWhLgUAdwJAQxqBMYMmWcloEpGvAcjap1fyLgCwx83X5nOnDeAw3Odndqy/3l4rCVUwTaj8l4VUCgAUCEAGHpbs8ENS3xnBEo4jXJLMfCUj+JY0f0b0qif1RekgyRX4ofXugPXJ9+u9k9FLIIDP711B01SahCAAAAAAAAAAAABQwlnOzt2zoXtzDfuqCnlpjW8CsADyHjA/dbq3K754GBfXLmpQel50kn3iVi7zQ17wUA0wWeSYzn73CBhO8tUIqmohmGbI+hrgHwMABqDW5NwJNNDQOdjJON4+aIBkwvoeuxIA4Ii1qVZAOAQPx09q7PpsbuuxBRBKSHl/rlRTAgRwCv6V7MRbcr8ZwSsex6wEGQhLzsRL0rxZUv1WT+o5SLACAGBn4yUwwDx/N1CTSkkCAAAAAAAAAAAAywAWmgC4CMDR53wAaLfqXxZHR8Of15yccz/DK06Is+jBDwx3SPt5tGlIu5nr1x93KOz+HG/76/0M+3U+n/oBMAxzAY+epikonuL+c+w5+VofjhtEHLsWYFAAgAIYPxhog6E0XAkaSAA4qf/3998IAGWh/caqheBe4IPNW4nMGFsI2sG1Pyq/8lpVAD6WnPBLSt2CYqnHCSpJIMaSM3xJXbuAD8ckYAUAwM7oJdCBH//giBqjGQQAAAAAAAAAAAAwDkCfDAAyFLXMqwMAzMld7eDWtY9TMKxd7XI/ioh+ju4eONLXi7q9zofT7f/nnoFFPffP4q3JOD/6r6ev07xQNNlUuf//PtwYeldpJYF76GMKsq6TASzsLAAKQKMBMBRko/xOTQN0XFoOyx0/zcAALkcgKigAyEwMUP2H++37UPX/SgWkS7ZQaMxtAQAeluzgLaXdgmBxXFRJkbHkAp5S91fo+OVoiAJlrsAL++sL1mPe3Ly3M3oJDOBvL98uKk2lJAEAAAAAAAAAAAAAbKO5wLA12hH16fa7IKV3kgvAI0CW1Ib16UO8tbjptye4k3PZj3/vxz3P5r9/Ps3erjibhANvRfRXd970TNPj/J8poG55KCDSeHJhpwEAAOirJoA+gHf6rZ4AA9Cb+xsMLOD2P5qdAcLFseCufenoTfaZjIRbnAyrqIrnJgH+lZzwU8I4IikuJ3UrpkhYcgIvKfsjSrf4cJyOMC/AWwEAuIuAbLwMAAC1UGkCAAAAAAAAAAAAAMB23E19JJO+Qg9gc23kvt1d6vWqpseSW0IAAB+0C+XNJ84MiQjnTqv8waGlPPfpUGGpr8+fplNfV7oKoP2FqaUwhwGqe39+vTtM81+fGcwmCwCAdgAmu2I4DlQAAGCgAWhmQSV6XSTcGNxXu7f7l9O/b0Cjb9DQm9MeGuEDd/R/RlVdIPMagHY2n0PbK46MAQA+lhzhS3J/RTBc7uR9YcKSE/GS9D1i9PzmpB4JMTcVhzPnn2y8BBrg461PqAwqgwQAAAAAAAAAAIAshf6uHWjfv8KrhwMAPkCFN50AAFxsDn5b/7I5u/WXB/N4Axd/ezO5KG00z3fVi5LBi/1UO9vsP71rMlxdFEMx4r6Tuc6GLg71r2yb6fTPOYw54jAXuCgAsEAdUwComtZdDSyMOd7ntw/rt8e5DQB2LUdDVJAv+7rPUN3M13obR7xXKTiSn5PkV6VUAB6WnOhLYn0i4MoneRQJEZaciaek7xWlX3yEk7YHAgmucFRrGFXwQPuHJw4dvQQ68N1/eTOHAU1lJAAAAAAAAAAAAACA/P19OGX/ONFPkjHaTBi0X+oA5A4w6/5m//N83csFqb++5b4o5H0fzb71YipiPudsqWmV4h7qfB1tUUBl9/1kAeQ36Gygz0sATwsAXMCg4FmLEgOYenet+c37bABoNz/ixxiKauBeWtzO2P6rSy1IANAlCzAyt99XyIsAAP6VHNFLynwFw+IYSBKW3Im7pP2IJvpoJ+1pLMQLoAEAcB9RI6OVAwDAoFBpAQAAAAAAAAAAAAAgs1l9d60DFoD2oqWftiz3TrNjT3PuPACQL6GNCueLgXDyhebwvw/OvJUy1RdT0SKZ37p5yZos+iLhuSvfToAkuz9OAAgvV4H5wgEAIJxKCORvSflvygKwAJBE7CFnzsCe77O+MfbcAFbLJPQTDZTh5oZVlV3xF1olIUGN0FP3AR6WnPBTIHcUsLhlDowlT3iX9tys9JffHBVhJtL37QUevfP8wZ3RSwCY/7V8g5pUmgQAAAAAAAAAAEDONzPE4FHHX9oB5gbIJ8wxAAB982i78fsrW/9dcqNftv+1ybA4/5jD84/V7Puc8+H/X2cSIKE9fS7XOwlTSWcrriqAe9zhBQxjAcBiAC5vuGgn4CiXhpqv5KCsAabnlIOMDwUGWLHVct1GxSYF7OFyMtK2bVsAXaiqqndVBQA+ljzwXWq8guHlIGUsueKn5P1lrVt8OKlPJSFeAEcAABfoEHr0CuYDzPt/jEUt1BQAAAAAAAAAAAAAwOR06aQ0zbsdIgDcRMjtuM2TMnxUuuABYD8Kal9Tbuzq11u7h47DxC2eZj/tatzcvrxe3Dz2H388XJwDzPnumPx+3nwmC84p35oGWNkKFuDSBBge6GFgADAOA8u3AOBmFVBa2J2jgrH3Hk8Wp7IvUGd3Ow6cAOABAEzT1dnJI0sGGWAPmw0+lryyi1Q/gs6noyUsYCx5wpuk9opZg9+c1M1amIlk8S5olnY97xw6WgPOrOlCZQgAAAAAAAAAAAD1+/7p+EwbvHQHoO0BFH/8a/sSAEA+v13u95uH+n7Tz+e+/vJkc29Ab5T+9GUpc2no0f0HGv2jzZMj06mpSroicRJOfbv9tQ/8rm00GC4ALAxU3wAAQJI3AKfoc92VBRzjNh7f/r07qYClagc4AVVfcPBY5q46VR9CwINYAj6W3MqbVH8EyW+OEYwlV/iQvP+i9dGHk/o2rxYwEeOkA734w+Qu4SswgLe/XqJSqQwBAAAAAAAAAAAA6ly9V5Xd9kgDWA+gMmU+AAA3jt++fb/M87ZhPp+jv6qrAdNwFn834839/v11rDcIiu7iUs0f5z/HHIdxzu0CBvKa1ZUDGS4AAACAClCbRgOA1oteQz2o+12azG5dAC0DFUAtwK67qLv/pJSEZuDzLAA+llzkS7L/rJlejpMIGEuu9CFQdxSl7304et0RsQIAYEezlwEAoDKoVAgAAAAAAAAAAADQAUwWAHAFAEDzi9pfh1sDdtoW1gIc+17t4paV34X7TP189RNu0qD4Yp9v+/ZPJ/94HNMv08BUqd6vX7vO8Qb/PEMHtJkWeNMZUw8AkG0HoBrI65wCAGAB5JUGYKLZ/sptNmE6588ft38vc1pQwa69w2pgACwwNH58liBUfAA+lryYmyR5s2n4cJRhLLmyp0A5kJUueDlu4m0pV+CC59kFenPrx1/f2VgDmopIKg0BAAAAAAAAAAAAAChRv7nThiqTm1mj/97P8ccaAbarAPhPb5/vXmz13KUy7DOFDDzU/n5bL9tzyx+P/34fO5Nyw2Q39+mTy86iGnLX83gOvH49Hur1MIgWQGejrwKAoKsBAIBMGiormq4BCmBIem7NbAxQ1YjRLwCwLCy6E6ZCAwKIHTM+ljz0XSABxcbLMUFYcmcPgdiR7b76dPRULWIFAMDO2csAAFALlQoBAAAAAAAAAAAANoBrAOABAAA972s6Kq29PXffV5+rSqbfdOaIYeOXn9F7rY+v9/HMmV7s6e5rM4fbD4/nagAgYaAqruxEBWhXvecG9LmkOGu9o7VaYAAYaAfAHpbkTOnB0AA0LKNaRAUAKPjA7AswZNTj+GvcJwBwnqu4ADMF62mAfTOLBbwhAD6WvJmtZD5R8LtjWMxYcmFPgbyL2BcfjgfGFTMRq71h57icvrlAg6ixBnxaUpNKhQAAAAAAAAAAAFASKuDxXLuZct87GBfpqlcstxAAtn0AcsPyUAYAKEeP62j8c4jb+Dd/jCOtPwUDAwPNcE3uuN6xyl/Hq4Px/f31Q6DPAINmLAAQGoYdAGBIyF1VVzXJAzDU+6nMDRgAZi2t3k4KAMACtJMUnqAAPpZczUMgF4uh+HRcUFfKWHJnD4Gyk5hX9nDc4j8CJs4Zn/c5Vtf+vrUzegl00Oz/mKMyqFQIAAAAAAAAAAAA+2SX+0AOuYs0gPwB+OveBwCAJS4i//6zX57PXZvPzr8qLR+QKWPKWT98NvzvNvv9mabJIrun65YXqpNpoMnvB2Dy3kkUg5XrggGSMnABC+t1wWAZNCd8m9gTjDEf8/Ph0w0GgP0yAicNYGAZuFaDQM4oPpbcyptAbSgYfjmOLWIsubOHQN5RHNWH43QVsQIAYGe0BnxTUWkqFQIAAAAAAAAAAAAEsFguAugiAHYWWfzPAwCIOu/V3qisLRp9O8yOwij2sfSZicNfGY/Pnsdt8+juyeZGHrIXD4+P5/H8+5jbV/gNwwxQe+irlp7uaajD+doFNVN8YhfDJ8kAGJAAgNVU4AIMpbfGaXZQAJV13pT3HgyAHRZ9AQDgDXwdKQF4AD6W3Oqb5PaJJvlwXGrBWPKUN8nnF/e6+HT03BYxJ/ivh05GLwHAxz89gwGVSgEAAAAAAAAAAABApzR/Gs/AWfNc75kmgL8BSkhmAgCNcavtHn/r8N/6lKdyAaeWMU255MZ+r9++OJ/qbweF/jQAgJ/K5npdraIbivnWmYB7mYyoQj6MumBAAgAAusHP9z8aQEWOOAEADMsWgB8AYIACPpbc6ruk/rCNl+MESzCWPPVNUvvF7KeH46yFCFpjvQVrOf7XYmf0MgAAgIbFMIAAAAAAAAAAAAAQfxmUEMjeVncAwFsBSP98dMZZfDigtPV4M7DL9Ubh3L+PkS/HOr8+t7eBu7rIhu08F0sfo1RCjnbUFEA+dfQBIy4AAFU3AG1bP5zWbwEAwAwFACTU2wAAgAE0AgABPpY85F1y/8WWPBxnJcRY8skOyWcXz3Xxm+MIhJh7k8O5T+6MXgKAPzy/BYtpACAAAAAAAAAAAABkF8fFvsHz8Z26jS3AjAA+92wUAGBep3RxOD3/pvf+3gtXhrYOuTz9/ejtfpkWr28Pf7VhhYLqqhZAvlU3pwEK/agEMtfzfkjYAAAYAAAAC9SVDABwLiAbaAA+lry4q+R4xRB8OE5aiLHkTW8l9Ufca/TpOC1Jgsb4FOBR/9f2ktFLAPDx3x/ngApgAAAAAAAAAAAAAJDo31kKSJ/+PgArAeArhQPg9fz4en0k3Hdv696q3dUyFLjWeq+brOatDpKbIQvocd3znBKtPcCmw+FAfwp9LzaU3m8AYAEAXADwkQlcAAA+ljz1TVL7xNb0jss1iowlX+6QsB3xvBYfjhMkyUTsYsFYHn/wdmf0MgAAgAoAAAAAAAAAAAAAQOkm2IAP/qrcIwB4BwBAab2dfZo6/7XO+WpRfH1yJoP+3udT82uvc04Wl2CYSdLklOgFi+unAQ7pv2on5Phb9B5wyocAAMi6AKie9Z428QUAABgAOFwFPpa8ma2k+MQwvRytW4mx5NVcJD6f+F7ZwzFJMHFY/SPY1s2/vdmZKwUAf3t+C1BhMQAAAAAAAAAAAABgnjuGonLZXCoAKwFkjcdnAIC8buXwJIK0dW9M483VDzCGo/r0Qr+97/3xdr895o/F8qdGBkBU1vDW5zDA5DNRJwBLK3sOcEXNbgAAAAsKYPcfAIBZPpa8ua2keMXAw3GptgJjyU+9Smq7uNfszzEQETTW2LAu+8N/enOh8QQjVwoAPvv3DRZTAQAAAAAAAAAAAAAwxVOCh3P1Ia/bflke3vH8f8hygAIwI0DNNJkAvn+d/3/8/N7/ZhseHtY/vY7bX/9OX3U/OB85j/vt67mrOd3OPWcagD+bTdN5zwYAAABgUBmMBcBMKQA+lty6m0CaUDRJ7ziJJGPJbbkJxEnirL3V0Q9wYSI5njXIq+bN8/c7o5cBAIDFVAAAAAAAAAAAAAAAgkSv8wnej2/LAYB87AAA0Mte4efB/Jupv//1wbgs/jipA3J7vO09v7w9vm1xfH5eugco0syJalW7swAwIQDA5+GzhepQ6AMA5DwAUTin2f+LNABABUArKgoAPh/tAz6WvOmtQCoU2/BwTEKMJW9pKxA2FPcyfDoWKSsAAC7wREZrQLeaSgUAAAAAAAAAAAAAAAB4tSz3dOc0COMRv5QBAG8AoOvH7MwAAGi/mW61bTvSSfo9LEveOXfj3skFvll8nTy+3P9+u98/3/48T96YLLLoqVLPdHwAaFhLBijyfSbO4uijDLAAC1A1gAUADKDAAvx8WgE+lty6m0AsEbevh+NJmbHkLZ4C4UJxPxaX26kLQcMBjPB2fXMpKDoi6i8BwPoOWEwFAAAAAAAAAAAAAEBCs+tRLDPL8eM9PhouyO93VHqUALAsAHvn4wE8PM7928MZXx+rbj8347V5PYvl+Tc9AGff8P96DmCsArO/b/vOgH9WVQMAvNQ1GgAsYAGWAmBNwAA+lry6rUAKElNzuZ26UsaSz7L/+y/uq/fhdpbErAAAuHB0eGRjDfgQsJgKAAAAAAAAAAAAAAAAkpdebsnpfSpDX+fLHgJAawCE/Pwilx4AgH34+O2zLm1nSfJIQZa6b+LhwjL5GufezvUt+/Ni9ouBP725M+fqgSkjgHz22wa/Ps2RA0dnAwDAAAAwAAYAQIMLPpa81KtAPCzmGj0cfawixpK3tBUID4rna7E6ev6BifAMort++/Zk9BIA9OkBUAEAAAAAAAAAAAAAkKkvvf4WgeO8CRAsAGGJSQAATO7rCuvffXvd5n/0/PB/3wa+ok/rvl6aWMCB+6a1/feQAZgNJBN1snkHAMgMAeBz1ujuSZQyAKgLAIACANAKAD6WfMRTQv3FbXo4Tm0RY8lPv0jYH/G8kk+3zj4mYgIAADujNeBkAhgAAAAAAAAAAAAAAAAAXC0APgXAzw0eLgE4e/z5/Md2ptojuw6DiNnLNtxb6tFXN3V7fDH2ZxY9OZdS3Fu319VP55zz/fE2FNFU90A/45Nd6K0coJ2egAKyiDcHzM9VAHAvAFyuAT6WvKatQNhY3JoPtwczMmPJhz8EwoPi/Vhc7vIDGQmYAADAzmgN+KSExTAAAAAAAAAAAAAAAAAAtA8tQKgAQr6h+04B1L0+/8MX0ZofeV2Eklh76uzPS/98vSHfWbj2dy+mLMr06SxSf6Sd6/XwWr+CBmZIoHf3JVwJAEUT0ABZqt8XwAfDAkpbDABcAKDWiwU+lnzEQyBOFFPw4XZqZMaSn2n9+yPuj+BynEbCTISYN1ixr998u7OxBpwHUAEAAAAAAAAAAAAAoNa7zsMTJDojAN4AIFgfTk0AAMp+u/HT1OXNVL51a799XP3N1NZ0Xm/fzz5/Nv/Nnfo61p5koCqpad64c+ckFJAsox2T6ev9Ybzd4cf+CuAALACgFABcAz6WvJSLQDwstmhxO/lSxpJ3vxUIC4onFqfyAF6VFQAAd6UIsrEGnAFQAQAAAAAAAAAAAAAAAL5gambuvmc/HvouewUACBUAnA/P9QAAKC3nv0x3oqJa8sfFYc7JNipM5a3FtLM+ve1D4WyanicAgALeqowqwRQUFFw5GHPn8f56G8c8JhjgBwAAwADAAgA+lny0Q39QbFxuqyLGku+y//0T96NYHScgZiL6cUGkm2++6XR/CQDav3QABgAAAAAAAAAAAAAAhPL/hUwIbn3kAfgGQEm+CgAAKHc+WSeNM/+aN//4d59j4lTB//7QTX/Z9EeFFtxVA0ABtc5VOJnnx/lxGx6g6wAAoAEA4HIBT2dnUwAEsihDAAAAAADo2utbZAAAAHSeOOsShZCChX59gHx5eH59fHNdRSoBPpa89Kv+xeLWrI7fnTJjyXdcJfRfPK9kcTuJlInYAyj0f/92Z2MN6FYEMAAAAAAAAAAAAAAAQELwVy/P/N9PZwQIFQB0U8MRAAANPb1M6enU9+ubtv7qI1MHhjOnF79/5nV8H4/3rxoggTmqq7Z7HxIAWHWH/fh9sfc89KPjBVAMAADqBT6WvLVT/4PiXpLe7XTVGEt++UXCeMR76VyOnhY0VgAA7Gy8BABvv9lgMQwAAAAAAAAAAAAAAIAFoL0B4BsAmNGZAADQip+2KS/aCg9tpxY5RWnbfj14d90837nZeXq1rzsw2a+L/eXYr+f75dOvGwCg3iunspndBVWVBPNqOEZe98P+HKp/agDFwACgAQAwAD6WPMtNIHxZ3Ibq+M1NZSx5L1uBsEFxr0V29JLIBI0VIJbXf/jDjhovAUD/bwAYAAAAAAAAAAAAAAAgeN1TmXme00UBQgeAP4QCAFflJXmRunxYGziaHgDs+7jV64q372G8fdo7H+jJbri69P/6WgF7BU7qYdzOODgebQJYCwAAYAE+lryVUyBcJI6gd3tvpcpY8tOvEs5HfK+od5wmmQkAAJyMVgYAj98sAAYAAAAAAAAAAAAAAMAC4BKAbwCwjE4A7uYfb7/otzs94LYBfbd2Zf308Lz397bt6+q405nm/ckNPe2939nv8+sGALj60MlNrl0ACe6AGUjir/OROfbPawAFACwAPpa8tov+C8UtqY7vl8ZY8l62AmGD4r6K7Li8VSVoPGYDyvX8/U87G2tA9wIAAAAAAAAAAAAAAAD4yeD8ygUzPdMAQgWAJt5/AcAZ41+naXp0f94ibquzabBr7P12670bb+md32MaIAEOAwFDkQBArx4gc859u9u//sMACzAAPpbctpv+O4vblh1ftY6x5CsdErZPPNeid5y6eoLGvgqIGFe/vFky14DHAAAAAAAAAAAAAAAAIBSTH8T7Qdp4BwCfAsDHjRoA2Pvzz51xJ1+si+Od143Obm6ek3vSHUh3L9UASe2ZmVvJWyQAFa0hCVCTi+cAA3cOAEDVIAA+ltzON/0mi6Ph+D71jCUf4/lrv7gfSXb8jtKg8AAAO6MZAAAAAAAAAAAAAAAAAJC4BAAA8GkuIhE2XBxGYTgr2KVt1uKPvux3XdQuqtd6a8Drl1Mu6++2t8ez3y4eEgrAB66c5KUAyJpQANQ1ys6E6VIKACh1APLgSRe5GicFAD6WvE1b/RqKo4mO87+OseRHWf/PK86jik6lj1WFIg3r3KYbM1gMAAAAAAAAAAAAAADI3l85VB5PosSXllYAWLfe9aDP52NqR4+X9NJzKTXqj0+H8euvjE8PoxT1yAcDlAVAEf8MGGeMy+2yYUsKACDXAGD6c62/AQDAAgA+lryvW30DiqnhOB2ZseS77v/+ivuaOHr9V4UJ9ARR5PjpemdjBgAAAAAAAAAAAAAAACsTzNJQ9PjclwAGw/xX7VGPz/ZTOyKLsxng6xhecx/zrV18/zyVBSSgfufKh4OSAqB7+QaGs/AD7OsPAMCiTgH4Xew7CgAAPpa8rVt9FcV2cPwOy4wlv9Lyf2/xfPQ6x4lUgoYrkGWsf7ezMUHgBQAAAAAAAAAAAAAAAIT1R8qz9LGMBfApAFzNVjsAOE+vP0ZOm/Nke+expYNkATx9fxzrujzct9td83AAYOh96GQ6IxKArOhXTRhjuKuNdtUAPpZ87OfXfzGWqTreqdQYS37W9T8fcT+K6uhlWyUO9ObcyWgNuAAAAAAAAAAAAAAAAACtX973E5UydL/+anQXICgAPDj/rQAAAHDvfJ+75jtl/0njMnU6JZk3+WDpuee5qbZeL5+mgBymkyTXSWcBQE34AJA6fKsBptoJADAAPpa87Vt9HcVcjs7xVp0qY8k/pf/PFOfFcSpNjQoTaCSMwvj+emdjgkB3AAAAAAAAAAAAAAAAsMg/vhCodr3nFIAXAMpPbo4AINKuz57vuulydew2lYzH2/fx+Pfl8TmPr3+2ohmABlIv2aCuBIAkfu9vGOM+7+7wzfk9WAA+lnyuxzd+MfopOt7eLmYs+bGs3/0V89eCo48tIkgAdjvG1x93NiYEAAAAAAAAAAAAAAAAyNrfzH6eNIqxAABA87TuWpeDK5PGT0ovUJgfnrNv3994O2P93/1cDPQU0G/RU5zNDQDQzRsDX8cYe09mP61gASgGoK/yP+4OPpb8Wpf/vAVUx9MixpKbr+1TXbRfVMcsBEXw5Xpo040JAgMAAAAAAAAAAAAAAADA69aIpCv1HL9zykkAnwGQehQJADg4q7X+9Kdzr/7js6kDs59Z82TRzL8+/5H9DwA1stiZFEDeW7/Pg6HGsJiTYikFAD6W3PxoP7oAjkmCseQ/9/IjBHAAJgAYB/vPrRkMGhMCAAAAAAAAAAAAAAAAECTLDF6dMG8PAIbk/npTe56m9mkDYDm+r7xyXwwI4KN9nwPmHjYYQwEAlA4A6Z9GOj6W/O+sX5oADjCW/O+oX5oADgAzoJwQBkEMIgAAAAAAAAAAAAAAAAB4AHYSapufRi8Wmbl3pjUAAAD3AQAAAAAAAJZ4Az6W/O+sX7oADjCW/OdZvjQBHAAGAABYDAAAAAAAAAAAAAAAAAC86APAAg4=", + "url": "https://thetestdata.com/assets/audio/ogg/thetestdata-sample-ogg-4.ogg", + "mime_type": "audio/ogg", + "file_size": 1242519, + "duration": 100.0, + "sample_rate": 44100, + "bitrate_avg": 97, + "format": "OGG" + } +} \ No newline at end of file diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py new file mode 100644 index 00000000..cad7cf9e --- /dev/null +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -0,0 +1,310 @@ +""" +Скачивает образцы файлов и сохраняет их фрагменты + ожидаемые метаданные +в fixtures.json для последующего тестирования. + +Два режима работы: + +1. Ручной (USE_AUTOMATIC_EXPECTED = False): + - В SAMPLE_URLS нужно указать expected-поля вручную (format, width, ...) + - Скрипт скачивает head/tail, дописывает mime_type, file_size из HTTP-ответа + - Подходит для фиксации эталонных значений + - Если expected не указаны — фикстура пропускается + +2. Автоматический (USE_AUTOMATIC_EXPECTED = True): + - В SAMPLE_URLS достаточно указать только url + - Скрипт сам запускает FileInspector и получает все expected-поля + - Подходит для быстрого добавления новых фикстур + - Полученные значения нужно проверить вручную перед коммитом! + +Файл содержит примеры SAMPLE_URLS. Отрредактировать для добавления новых. + +Фикстуры не затираются полностью: +- Существующие в fixtures.json остаются нетронутыми +- Если имя совпадает — обновляется (перезаписывается) +- Новые добавляются +- Перед именованием проверить fixtures.json + +Запуск: + python prepare_fixtures.py # все из SAMPLE_URLS + python prepare_fixtures.py mp4_vp9 mkv_h264 # только указанные +""" + +import asyncio +import base64 +import contextlib +import json +import logging +import sys +from pathlib import Path +from typing import cast + +import aiohttp +from maxapi.utils.file_inspector import FileInspector + +logger = logging.getLogger(__name__) +FIXTURES_FILE = Path(__file__).parent / "fixtures.json" + +DEFAULT_HEADERS = { + "User-Agent": ( + "Mozilla/5.0 (Windows NT 10.0; Win64; x64) " + "AppleWebKit/537.36 (KHTML, like Gecko) " + "Chrome/124.0.0.0 Safari/537.36" + ), +} + +# Если True — expected берутся из FileInspector автоматически. +# Внимание: полученные значения нужно проверять вручную! +USE_AUTOMATIC_EXPECTED = True + + +SAMPLE_URLS = { + # Провеирть ключи. Совпадающие заменят данные, новые добавят. + + # Без expected — работает только в автоматическом режиме. + # Формат, размеры и т.д. будут получены из FileInspector. + "mp4_vp9": { + "url": "https://samplelib.com/mp4/sample-10s-vp9.mp4", + }, + + # С expected — работает в обоих режимах. + # В ручном режиме значения берутся отсюда. + # В автоматическом — перезаписываются тем, что вернул FileInspector. + "mkv_h264": { + "url": "https://example.com/h264.mkv", + "expected": { + "width": 3840, + "height": 2160, + "duration": 19.0, + "fps": 50.0, + "sample_rate": 48000, + "format": "MKV", + }, + "head_size": 8192, + }, + + # Минимальный объём + "png_transparency": { + "url": ( + "https://upload.wikimedia.org/wikipedia/commons/4/47/" + "PNG_transparency_demonstration_1.png" + ), + "expected": {"format": "PNG", "width": 800, "height": 600}, + "head_size": 2048, + }, + + # Локальный файл + "mp3_id3_tag": { + "file": "/path/to/local/mp3.mp3", + "expected": {"format": "MP3", "duration": 160, "sample_rate": 44100, bit}, + }, +} + + +async def download_head( + session: aiohttp.ClientSession, url: str, size: int +) -> tuple[bytes, str, int | None]: + """Скачивает начало файла. Возвращает (данные, content_type, file_size).""" + async with session.get(url, headers=DEFAULT_HEADERS) as resp: + content_type = resp.headers.get("Content-Type", "") + file_size = None + with contextlib.suppress(ValueError): + file_size = int(resp.headers.get("Content-Length", "0")) or None + + data = b"" + while len(data) < size: + chunk = await resp.content.read(size - len(data)) + if not chunk: + break + data += chunk + return data, content_type, file_size + + +def read_local_head(filepath: str, size: int) -> tuple[bytes, str, int | None]: + """Читает начало локального файла.""" + import mimetypes + + path = Path(filepath) + content_type, _ = mimetypes.guess_type(filepath) + file_size = path.stat().st_size + with open(filepath, "rb") as f: # noqa: PTH123 + head = f.read(size) + return head, content_type or "application/octet-stream", file_size + + +def read_local_tail(filepath: str, size: int) -> bytes: + """Читает конец локального файла.""" + with open(filepath, "rb") as f: # noqa: PTH123 + f.seek(max(0, Path(filepath).stat().st_size - size)) + return f.read() + + +async def download_tail( + session: aiohttp.ClientSession, + url: str, + size: int, + head: bytes, +) -> bytes: + """Скачивает конец файла через Range-запрос. + Если сервер не поддерживает Range, возвращает b"". + """ + headers = {**DEFAULT_HEADERS, "Range": f"bytes=-{size}"} + async with session.get(url, headers=headers) as resp: + if resp.status not in (200, 206): + return b"" + data = b"" + while len(data) < size: + chunk = await resp.content.read(size - len(data)) + if not chunk: + break + data += chunk + + # Проверяем, что сервер вернул хвост, а не весь файл + if data[: len(head)] == head[: len(data)]: + return b"" + return data + + +def _load_existing() -> dict: + """Загружает существующие фикстуры из JSON.""" + if not FIXTURES_FILE.exists(): + return {} + return json.loads(FIXTURES_FILE.read_text(encoding="utf-8")) + + +async def get_expected(name, cfg, session) -> tuple[bytes, bytes]: + if USE_AUTOMATIC_EXPECTED: + logger.info( + "Получаю expected для %s: %s", + name, + cfg.get("url") or cfg.get("file"), + ) + + inspector = FileInspector() + if "file" in cfg: + finfo = await inspector.inspect_file(cfg["file"]) + elif "url" in cfg: + finfo = await inspector.inspect_url(cfg["url"], session=session) + else: + raise ValueError( + "В параметрах не найдена ссылка на источник url или file" + ) + head = inspector.last_head + tail = inspector.last_tail + expected = finfo.model_dump() + cfg["expected"] = expected + cfg["head_size"] = len(head) + cfg["tail_size"] = len(tail) + # Очистка данных + for k in tuple(expected): + if not expected[k]: + del expected[k] + del expected["file_name"] + if ( + set(expected.keys()) == {"url", "mime_type", "file_size"} + or len(expected) <= 3 + ): + logger.warning( + "Не достаточно данных в expected для %s: %s", + name, + expected, + ) + cfg["expected"] = {} + return b"", b"" + return head, tail + return b"", b"" + + +async def generate_fixture(name, cfg, session, head, tail) -> dict | None: + logger.info("Скачиваю %s: %s", name, cfg.get("url") or cfg.get("file")) + try: + fixture = {} + content_type = file_size = None + if not cfg.get("expected"): + # Если не указаны ожидания, пропускаем + return None + if not USE_AUTOMATIC_EXPECTED: + if "url" in cfg: + # Была создана вначале функции, + # если есть хоть один url в списке + session = cast(aiohttp.ClientSession, session) + + head, content_type, file_size = await download_head( + session, cfg["url"], cfg["head_size"] + ) + tail = b"" + if cfg.get("tail_size"): + tail = await download_tail( + session, cfg["url"], cfg["tail_size"], head + ) + elif "file" in cfg: + head, content_type, file_size = read_local_head( + cfg["file"], cfg.get("head_size", 65536) + ) + if cfg.get("tail_size"): + tail = read_local_tail(cfg["file"], cfg["tail_size"]) + + fixture.update( + { + "mine_type": content_type, + "file_size": file_size, + } + ) + fixture.update( + { + "head_b64": base64.b64encode(head).decode(), + "tail_b64": base64.b64encode(tail).decode(), + **cfg["expected"], + # Сохраним url в фикстуре для проверки параметров + "url": cfg.get("url") + or f"file://{Path(cfg.get('file')).name}", + } + ) + # Очистим от пустых значений + for k in tuple(fixture): + if not fixture[k]: + del fixture[k] + logger.info(" OK: head=%s, tail=%s", len(head), len(tail)) + return fixture + + except Exception as exc: + logger.error(" FAIL: %s", exc) + + +async def main(): + names = sys.argv[1:] if len(sys.argv) > 1 else list(SAMPLE_URLS) + fixtures = _load_existing() + updated = False + need_session = any( + "url" in SAMPLE_URLS[name] for name in names if name in SAMPLE_URLS + ) + session = aiohttp.ClientSession() if need_session else None + try: + for name in names: + if name not in SAMPLE_URLS: + logger.warning("Пропущено: %s", name) + continue + + cfg = SAMPLE_URLS[name] + + head, tail = await get_expected(name, cfg, session) + fixture = await generate_fixture(name, cfg, session, head, tail) + if fixture: + fixtures[name] = fixture + updated = True + finally: + if session: + await session.close() + + if updated: + FIXTURES_FILE.write_text( + json.dumps(fixtures, indent=2, ensure_ascii=False), + encoding="utf-8", + ) + logger.info("Сохранено: %s", FIXTURES_FILE) + + +if __name__ == "__main__": + logging.basicConfig( + level=logging.INFO, format="%(levelname)-7s| %(message)s" + ) + asyncio.run(main()) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py new file mode 100644 index 00000000..439a8ab4 --- /dev/null +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -0,0 +1,555 @@ +""" +Тесты для FileInspector и RangeDownloader на фикстурах. +""" + +import base64 +import json +import logging +import mimetypes +from io import BytesIO +from pathlib import Path +from unittest.mock import AsyncMock, Mock, patch + +import aiohttp +import pytest +from maxapi.connection.base import NamedBytesIO +from maxapi.utils.file_inspector import ( + FileInspector, + RangeDownloader, +) +from yarl import URL + +log = logging.getLogger("maxapi.fileinfo") +log.setLevel(logging.DEBUG) + +# mimetypes не знает image/webp в стандартной библиотеке Python (до 3.11+). +mimetypes.add_type("image/webp", ".webp") + +FIXTURES_FILE = Path(__file__).parent / "fixtures.json" +FIXTURES_ID = list( + json.loads(FIXTURES_FILE.read_text(encoding="utf-8")).keys() +) + + +@pytest.fixture(scope="session") +def all_fixtures(): + """Загружает все фикстуры один раз на сессию.""" + fixtures = json.loads(FIXTURES_FILE.read_text(encoding="utf-8")) + loaded = {} + for name, f in fixtures.items(): + head = base64.b64decode(f["head_b64"]) + tail = base64.b64decode(f["tail_b64"]) if "tail_b64" in f else None + expected = { + k: v for k, v in f.items() if k not in ("head_b64", "tail_b64") + } + loaded[name] = (head, tail, expected) + return loaded + + +@pytest.fixture(scope="session") +def fixture_bytes_io(all_fixtures): + """Кеширует NamedBytesIO для всех фикстур.""" + cached = {} + for name, (head, tail, exp) in all_fixtures.items(): + cached[name] = _make_fixture_named_bytes_io(name, head, tail, exp) + return cached + + +def _make_fixture_named_bytes_io(name, head, tail, exp) -> NamedBytesIO: + """ + Собирает полный объём: head + нули (середина) + tail + чтобы эмулировать реальный файл для inspect_bytes() + """ + mime = exp.get("mime_type", "") + ext = mimetypes.guess_extension(mime) or ".bin" + file_name = f"{name}{ext}" + + file_size = exp.get("file_size") or (len(head) + len(tail)) + + full = bytearray(file_size) + full[: len(head)] = head + if tail: + full[-len(tail) :] = tail + + bio = NamedBytesIO(full) + bio.name = file_name + return bio + + +def _make_fixture_file(name, head, tail, exp, tmp_path) -> Path: + """ + Создаёт временный файл из фикстуры. + + Собирает полный файл: head + нули (середина) + tail, + чтобы эмулировать реальный файл для inspect_file(). + """ + bio = _make_fixture_named_bytes_io(name, head, tail, exp) + file_path = tmp_path / f"{bio.name}" + + file_path.write_bytes(bio.getbuffer()) + return file_path + + +# ============================================================================= +# Mock-хелперы +# ============================================================================= + + +class MockResponseFactory: + """Фабрика мок-ответов aiohttp для RangeDownloader.""" + + def __init__( + self, + head: bytes, + tail: bytes | None, + content_type: str = "application/octet-stream", + file_size: int | None = None, + file_name: str | None = None, + ): + self.head = head + self.tail = tail + self.content_type = content_type + self.file_name = file_name + self.file_size = file_size + self._head_pos = 0 + self._tail_used = False + + def make_head_response(self, url: str) -> AsyncMock: + self._head_pos = 0 # Сброс для нового ответа + resp = AsyncMock() + resp.ok = True + resp.status = 200 + resp.url = URL(url) + resp.closed = False + resp.headers = { + "Content-Type": self.content_type, + "Content-Length": str(self.file_size or len(self.head)), + } + if self.file_name: + resp.headers["Content-Disposition"] = ( + f"attachment; filename={self.file_name}" + ) + resp.history = () + resp.request_info = Mock() + + async def read_head(n: int = -1) -> bytes: + available = len(self.head) - self._head_pos + log.debug("read_head: n=%s, available=%s", n, available) + if available <= 0: + return b"" + to_read = min(n, available) if n > 0 else available + chunk = self.head[self._head_pos : self._head_pos + to_read] + self._head_pos += to_read + return chunk + + resp.content.read = read_head + resp.read = AsyncMock(return_value=self.head) + resp.release = Mock() + return resp + + def make_tail_response(self, url: str) -> AsyncMock: + resp = AsyncMock() + resp.ok = True + resp.status = 206 + resp.url = URL(url) + resp.headers = {"Content-Type": self.content_type} + resp.history = () + resp.request_info = Mock() + + async def read_tail(n: int = -1) -> bytes: + if self._tail_used: + return b"" + self._tail_used = True + return self.tail or b"self.tail empty" + + resp.content.read = read_tail + resp.read = AsyncMock(return_value=self.tail) + resp.release = Mock() + return resp + + +def _make_mock_session( + head: bytes, + tail: bytes | None, + content_type: str = "application/octet-stream", + file_size: int | None = None, + file_name: str | None = None, +) -> AsyncMock: + factory = MockResponseFactory( + head, tail, content_type, file_size, file_name + ) + session = AsyncMock() + session.get = AsyncMock( + side_effect=lambda url, headers=None: ( + factory.make_tail_response(url) + if headers and "Range" in headers + else factory.make_head_response(url) + ) + ) + return session + + +# ============================================================================= +# Тесты FileInspector (параметризованные) +# ============================================================================= + + +@pytest.mark.parametrize("name", FIXTURES_ID) +class TestFileInspectorURL: + """FileInspector на всех фикстурах.""" + + async def test_inspect_returns_fileinfo(self, name, all_fixtures): + """Все фикстуры возвращают FileInfo без исключений.""" + head, tail, exp = all_fixtures[name] + session = _make_mock_session( + head, + tail, + exp["mime_type"], + exp["file_size"], + ) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/test", session=session + ) + assert info.status in ("ok", "partial", "error") + + async def test_format_with_no_mime_type(self, name, all_fixtures): + """Если сервер не вернул content_type, то парсер должен определить + формат по содержанию файла""" + head, tail, exp = all_fixtures[name] + log.debug("Длина заголовка в фикстуре %s", len(head)) + session = _make_mock_session( + head, + tail, + "", # no mime_type + exp["file_size"], + ) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/test", session=session + ) + assert info.format == exp["format"] + + async def test_expected_fields_match(self, name, all_fixtures): + """Проверяем FileInfo на соответствие полям expected, если заданы.""" + head, tail, exp = all_fixtures[name] + session = _make_mock_session( + head, + tail, + exp["mime_type"], + exp["file_size"], + ) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/test", session=session + ) + log.debug("Размер фикстуры head=%s", len(head)) + if exp.get("format"): + assert info.format == exp["format"] + if exp.get("width"): + assert info.width == exp["width"] + assert info.height == exp["height"] + if exp.get("fps"): + assert info.fps == exp["fps"] + if exp.get("duration"): + assert info.duration == exp["duration"] + if exp.get("sample_rate"): + assert info.sample_rate == exp["sample_rate"] + + +class TestFileInspectorError: + """FileInspector возврат ошибок""" + + async def test_client_error_returns_error_status(self): + """aiohttp.ClientError → FileInfo(status='error').""" + session = AsyncMock() + session.get = AsyncMock(side_effect=aiohttp.ClientConnectionError()) + + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/test.jpg", + session=session, + max_retries=1, + ) + assert info.status == "error" + + async def test_generic_exception_returns_error_status(self): + """Любое исключение → FileInfo(status='error').""" + session = AsyncMock() + session.get = AsyncMock(side_effect=ValueError("unexpected")) + + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/test.jpg", + session=session, + max_retries=1, + ) + assert info.status == "error" + assert info.error_desc == "unexpected" + + async def test_retry_then_success(self): + """503 → retry → успех.""" + factory = MockResponseFactory( + head=b"data", tail=b"", content_type="image/jpeg", file_size=4 + ) + meta_resp = factory.make_head_response("url") # для _fetch_meta + bad = factory.make_head_response("url") + bad.status, bad.ok = 503, False + good = factory.make_head_response("url") + session = AsyncMock() + session.get = AsyncMock(side_effect=[meta_resp, bad, good]) + + info = await FileInspector().inspect_url( + "https://x.com/x.jpg", session=session, retry_backoff_factor=0 + ) + assert info.status == "error" + assert session.get.call_count == 3 + + async def test_retry_exhausted(self): + """503 × 2 → error.""" + factory = MockResponseFactory( + head=b"data", tail=b"", content_type="image/jpeg", file_size=4 + ) + bad = factory.make_head_response("url") + bad.status, bad.ok = 503, False + session = AsyncMock() + session.get = AsyncMock(side_effect=[bad, bad]) + + info = await FileInspector().inspect_url( + "https://x.com/x.jpg", + session=session, + max_retries=1, + retry_backoff_factor=0, + ) + assert info.status == "error" + assert session.get.call_count == 2 + + async def test_client_error_no_retry(self): + """404 → сразу error.""" + factory = MockResponseFactory( + head=b"data", tail=b"", content_type="image/jpeg", file_size=4 + ) + bad = factory.make_head_response("url") + bad.status, bad.ok = 404, False + session = AsyncMock() + session.get = AsyncMock(side_effect=[bad]) + + info = await FileInspector().inspect_url( + "https://x.com/x.jpg", + session=session, + max_retries=2, + retry_backoff_factor=0, + ) + assert info.status == "error" + assert session.get.call_count == 1 + + +@pytest.mark.parametrize("name", FIXTURES_ID) +class TestFileInspectorLocalFile: + """FileInspector.inspect_file() на всех фикстурах.""" + + async def test_inspect_file_returns_fileinfo( + self, name, all_fixtures, tmp_path + ): + """Все фикстуры возвращают FileInfo без исключений.""" + head, tail, exp = all_fixtures[name] + file_path = _make_fixture_file(name, head, tail, exp, tmp_path) + info = await FileInspector().inspect_file(str(file_path)) + assert info.status in ("ok", "partial", "error") + + async def test_expected_fields_match(self, name, all_fixtures, tmp_path): + """Формат из локального файла совпадает с expected.""" + head, tail, exp = all_fixtures[name] + file_path = _make_fixture_file(name, head, tail, exp, tmp_path) + inspector = FileInspector() + info = await inspector.inspect_file( + str(file_path), + full_read_limit=0, # Отключаем полное чтение файла + ) + if exp.get("format"): + assert info.format == exp["format"] + if exp.get("sample_rate"): + assert info.sample_rate == exp["sample_rate"] + if exp.get("duration"): + assert info.duration == exp["duration"] + if exp.get("fps"): + assert info.fps == exp["fps"] + if exp.get("width"): + assert info.width == exp["width"] + assert info.height == exp["height"] + + +class TestFileInspectorLocalErrors: + """Тесты FileInspector с локальными файлами.""" + + async def test_inspect_local_nonexistent(self): + """Несуществующий файл → error.""" + inspector = FileInspector() + info = await inspector.inspect_file("/nonexistent/file.jpg") + assert info.status == "error" + assert info.error_desc == "Файл не найден" + + async def test_inspect_local_permission_denied(self, tmp_path: Path): + """Ошибка чтения файла → error.""" + file_path = tmp_path / "exists.jpg" + file_path.write_bytes(b"fake data") + + # Мокаем открытие файла — выбрасывает ошибку + with patch("anyio.open_file") as mock_open: + mock_open.side_effect = OSError("read error") + + inspector = FileInspector() + info = await inspector.inspect_file(str(file_path)) + assert info.status == "error" + assert "read error" in info.error_desc + + +class TestFileInspectorBytes: + """Тесты FileInspector с локальными файлами.""" + + @pytest.mark.parametrize("name", FIXTURES_ID) + async def test_bytes_butesio_namedbytesio_match(self, name, all_fixtures): + """bytes, BytesIO, NamedBytesIO дают идентичные результаты""" + head, tail, exp = all_fixtures[name] + + nbio = _make_fixture_named_bytes_io(name, head, tail, exp) + byt = nbio.getbuffer() # bytes + bio = BytesIO(byt) + file_name = nbio.name or "" + + inspector = FileInspector() + info_nbio = await inspector.inspect_bytes(nbio) + info_bio = await inspector.inspect_bytes(bio, file_name=file_name) + info_byt = await inspector.inspect_bytes(byt, file_name=file_name) + + assert info_nbio == info_bio + assert info_bio == info_byt + + @pytest.mark.parametrize("name", FIXTURES_ID) + async def test_expected_fields_match(self, name, all_fixtures): + """Формат из байт совпадает с expected.""" + head, tail, exp = all_fixtures[name] + nbio = _make_fixture_named_bytes_io(name, head, tail, exp) + info = await FileInspector().inspect_bytes( + nbio, + full_read_limit=0, # Отключаем полное чтение данных + ) + if exp.get("format"): + assert info.format == exp["format"] + if exp.get("sample_rate"): + assert info.sample_rate == exp["sample_rate"] + if exp.get("duration"): + assert info.duration == exp["duration"] + if exp.get("fps"): + assert info.fps == exp["fps"] + if exp.get("width"): + assert info.width == exp["width"] + assert info.height == exp["height"] + + +# ============================================================================= +# Специфичные тесты, не зависящие от списка фикстур +# ============================================================================= + + +class TestEdgeCases: + """Краевые случаи.""" + + async def test_small_head_partial(self): + """Маленький head — partial.""" + head = b"RIFF\x00\x00\x00\x00WEBP" # обрезанный WebP + session = _make_mock_session(head, b"", "image/webp", 1000) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/test.webp", + session=session, + max_total=len(head), + ) + assert info.format == "WEBP" + assert info.status == "partial" + assert info.error_desc.startswith("Недостаточно данных") + + async def test_html_page_error(self): + """HTML-страница — error.""" + html = b"" + session = _make_mock_session(html, b"", "text/html", len(html)) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/page.html", session=session + ) + assert info.status == "error" + assert info.error_desc == "Файл не является медиа (HTML-страница)" + + async def test_wrong_random_data_page_error(self): + """Случайное содержание по ссылке — error.""" + data = b"8" + session = _make_mock_session(data, b"", "", len(data)) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/page.html", session=session + ) + assert info.status == "error" + assert info.error_desc.startswith("Недостаточно данных") + + async def test_empty_body_error(self): + """Пустой ответ — error.""" + session = _make_mock_session(b"", b"", "text/plain", 0) + inspector = FileInspector() + info = await inspector.inspect_url( + "https://example.com/empty", session=session + ) + assert info.status == "error" + assert info.error_desc.startswith("Недостаточно данных") + + +# ============================================================================= + + +class TestRangeDownloader: + # Тесты извлечения имени файла + def test_from_content_disposition(self): + headers = {"Content-Disposition": 'attachment; filename="photo.jpg"'} + name = RangeDownloader._extract_filename(headers, "https://x.com/123") + assert name == "photo.jpg" + + def test_from_url_when_no_disposition(self): + headers = {} + name = RangeDownloader._extract_filename( + headers, "https://x.com/path/photo.jpg" + ) + assert name == "photo.jpg" + + def test_url_with_query_params(self): + headers = {} + name = RangeDownloader._extract_filename( + headers, "https://x.com/photo.jpg?size=large" + ) + assert name == "photo.jpg" + + def test_filename_with_percent_encoding(self): + headers = {} + name = RangeDownloader._extract_filename( + headers, "https://x.com/%D1%84%D0%B0%D0%B9%D0%BB.jpg" + ) + assert name == "файл.jpg" + + def test_content_disposition_without_quotes(self): + headers = {"Content-Disposition": "attachment; filename=photo.jpg"} + name = RangeDownloader._extract_filename(headers, "https://x.com/123") + assert name == "photo.jpg" + + def test_url_without_filename(self): + headers = {} + name = RangeDownloader._extract_filename(headers, "https://x.com/") + assert name == "unknown" # или что возвращается по умолчанию + + async def test_creates_session_when_none(self): + """RangeDownloader создаёт сессию если не передана.""" + dl = RangeDownloader("https://example.com", session=None) + assert dl.session is None + + with patch("aiohttp.ClientSession") as mock_cls: + mock_cls.return_value = AsyncMock() + async with dl: + assert dl.session is not None + assert dl.session is None From 2b16f31300ab0aa47977d8240ccc673f884a180d Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 08:01:43 +0300 Subject: [PATCH 02/31] fix: ruff --- maxapi/utils/file_inspector.py | 15 ++++++--------- tests/test_utils/fileinfo/prepare_fixtures.py | 7 ++----- 2 files changed, 8 insertions(+), 14 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 84434892..da9de70d 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -1,5 +1,3 @@ -from __future__ import annotations - """ Инспекция медиафайлов без полной загрузки. @@ -8,6 +6,8 @@ битрейт. Высокоуровневая точка входа — :class:`FileInspector`. """ +from __future__ import annotations + import asyncio import logging import mimetypes @@ -1118,19 +1118,16 @@ def parse_media_dimensions( if not content_type or content_type == "application/octet-stream": # Если сервер не определил тип файла # Проверим все типы по содержанию - result = cls._parse_image_dimensions(head, content_type, file_size) + result = cls._parse_image_dimensions(head, "", file_size) if result: return result - result = cls._parse_video_dimensions( - head, tail, content_type, file_size - ) + result = cls._parse_video_dimensions(head, tail, "", file_size) if result: return result - result = cls._parse_audio_dimensions( - head, tail, content_type, file_size - ) + result = cls._parse_audio_dimensions(head, tail, "", file_size) if result: return result + return None if content_type.startswith("image/"): return cls._parse_image_dimensions(head, content_type, file_size) diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py index cad7cf9e..fbe4a95c 100644 --- a/tests/test_utils/fileinfo/prepare_fixtures.py +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -59,13 +59,12 @@ SAMPLE_URLS = { # Провеирть ключи. Совпадающие заменят данные, новые добавят. - + # -- # Без expected — работает только в автоматическом режиме. # Формат, размеры и т.д. будут получены из FileInspector. "mp4_vp9": { "url": "https://samplelib.com/mp4/sample-10s-vp9.mp4", }, - # С expected — работает в обоих режимах. # В ручном режиме значения берутся отсюда. # В автоматическом — перезаписываются тем, что вернул FileInspector. @@ -81,7 +80,6 @@ }, "head_size": 8192, }, - # Минимальный объём "png_transparency": { "url": ( @@ -91,11 +89,10 @@ "expected": {"format": "PNG", "width": 800, "height": 600}, "head_size": 2048, }, - # Локальный файл "mp3_id3_tag": { "file": "/path/to/local/mp3.mp3", - "expected": {"format": "MP3", "duration": 160, "sample_rate": 44100, bit}, + "expected": {"format": "MP3", "duration": 160, "sample_rate": 44100}, }, } From e2dbd7f4e3c93ae32ac227f7513be568db67f432 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 08:02:02 +0300 Subject: [PATCH 03/31] fix: NamedBytesIO --- maxapi/connection/base.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/maxapi/connection/base.py b/maxapi/connection/base.py index 24aee61d..fbe64a22 100644 --- a/maxapi/connection/base.py +++ b/maxapi/connection/base.py @@ -2,6 +2,7 @@ import asyncio import mimetypes +from io import BytesIO from pathlib import Path from typing import TYPE_CHECKING, Any @@ -39,6 +40,21 @@ def __init__(self, status: int) -> None: super().__init__(f"Server error {status}") +class NamedBytesIO(BytesIO): + """ + BytesIO с поддержкой атрибута .name для единообразия с файловыми объектами. + """ + + __slots__ = ("name",) + name: str | None + + def __init__( + self, buffer: bytes = b"", *, name: str | None = None + ) -> None: + super().__init__(buffer) + self.name = name # Соответствует протоколу typing.BinaryIO + + def _on_backoff(details: Details) -> None: """Логирование при retry. From 1c4c299f33c7d0bea36156b52458883106ac8070 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 08:33:53 +0300 Subject: [PATCH 04/31] =?UTF-8?q?add:=20tests=20for=20bot.get=5Ffile=5Finf?= =?UTF-8?q?o=20fix:=20JPEG=20parser=20fix:=20bot.get=5Ffile=5Finfo=20kwarg?= =?UTF-8?q?s=20=D0=B4=D0=BB=D1=8F=20=D0=BF=D0=B5=D1=80=D0=B5=D0=B4=D0=B0?= =?UTF-8?q?=D1=87=D0=B8=20=D0=B2=20RangeDownloader?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- maxapi/bot.py | 13 +++++++-- maxapi/utils/file_inspector.py | 4 +-- tests/test_utils/fileinfo/test_file_info.py | 29 +++++++++++++++++++++ 3 files changed, 42 insertions(+), 4 deletions(-) diff --git a/maxapi/bot.py b/maxapi/bot.py index f196fc6a..1611ed37 100644 --- a/maxapi/bot.py +++ b/maxapi/bot.py @@ -739,7 +739,9 @@ async def get_video(self, video_token: str) -> Video: return await GetVideo(bot=self, video_token=video_token).fetch() - async def get_file_info(self, url: str, *, timeout: int = 10) -> FileInfo: + async def get_file_info( + self, url: str, *, timeout: int = 10, **kwargs + ) -> FileInfo: """ Получает метаинформацию о файле по URL. @@ -751,12 +753,19 @@ async def get_file_info(self, url: str, *, timeout: int = 10) -> FileInfo: Args: url: URL файла. timeout: Таймаут HTTP-запроса в секундах. + kwargs: + - session: Общая aiohttp-сессия (создаётся при ``None``). + - max_total: Максимальный объём скачанных данных (байт). + - max_retries: Число повторных попыток при ``retry_on_statuses``. + - retry_on_statuses: HTTP-статусы, при которых повторять запрос. + - retry_backoff_factor: Множитель задержки между попытками + (1.0 → 1с, 2с, 4с). Returns: FileInfo: Метаинформация о файле. """ inspector = FileInspector() - return await inspector.inspect_url(url, timeout=timeout) + return await inspector.inspect_url(url, timeout=timeout, **kwargs) async def send_callback( self, diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index da9de70d..5b9ea110 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -1685,7 +1685,7 @@ def _gif_parse_info( def _jpeg_parse(data: bytes) -> dict | None: if len(data) < 2 or data[:2] != b"\xff\xd8": return None - + result = {"format": "JPEG"} pos = 2 while pos < len(data) - 1: if data[pos] != 0xFF: @@ -1709,7 +1709,7 @@ def _jpeg_parse(data: bytes) -> dict | None: break else: pos += 2 - return None + return result # ========================================================================= # [ ] Парсеры: видео diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 439a8ab4..711301bc 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -13,6 +13,7 @@ import aiohttp import pytest from maxapi.connection.base import NamedBytesIO +from maxapi.types.file_info import FileInfo from maxapi.utils.file_inspector import ( FileInspector, RangeDownloader, @@ -553,3 +554,31 @@ async def test_creates_session_when_none(self): async with dl: assert dl.session is not None assert dl.session is None + + +class TestBotGetFileInfo: + """Тесты bot.get_file_info().""" + + async def test_returns_fileinfo(self, bot): + """Возвращает FileInfo с полями.""" + head = b"\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01" # минимальный JPEG + session = _make_mock_session( + head=head, tail=b"", content_type="image/jpeg", file_size=len(head) + ) + + info = await bot.get_file_info( + "https://example.com/photo.jpg", timeout=5, session=session + ) + + assert isinstance(info, FileInfo) + assert info.mime_type == "image/jpeg" + assert info.format == "JPEG" + + @patch("maxapi.utils.file_inspector.FileInspector.inspect_url") + async def test_timeout_passed_to_inspector(self, mock_inspect, bot): + """timeout передаётся в RangeDownloader.""" + mock_inspect.return_value = FileInfo(url="test") + bot.session = AsyncMock() + + await bot.get_file_info("https://example.com/file.mp4", timeout=15) + assert mock_inspect.call_args.kwargs["timeout"] == 15 From 4e5371384cfd7d628762c1f25059809e06bd74fb Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 13:40:28 +0300 Subject: [PATCH 05/31] fix: copilot review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - file_size = exp.get("file_size") or (len(head) + len(tail)) упадёт с TypeError - В mock tail-чтения при tail is None возвращается b"self.tail empty" - Docstring теста - _m4a_parse_mvhd_duration(data: bytes) -> float - опечатки и орфография --- maxapi/types/file_info.py | 4 ++-- maxapi/utils/file_inspector.py | 8 ++++---- tests/test_utils/fileinfo/prepare_fixtures.py | 4 ++-- tests/test_utils/fileinfo/test_file_info.py | 20 ++++++++++++------- 4 files changed, 21 insertions(+), 15 deletions(-) diff --git a/maxapi/types/file_info.py b/maxapi/types/file_info.py index 0f2efe3b..0a498842 100644 --- a/maxapi/types/file_info.py +++ b/maxapi/types/file_info.py @@ -146,7 +146,7 @@ def __str__(self) -> str: ] _FIELD_LABELS = ( ("format", "Формат: {}"), - ("width", " Размеры: {}×"), + ("width", " Размеры: {}"), ("height", None), # добавляется к width ("duration", " Длительность: {} сек"), ("fps", " Частота кадров: {} к/с"), @@ -160,7 +160,7 @@ def __str__(self) -> str: if not value: continue if field == "height": - lines[-1] = lines[-1].rstrip(" пикс") + f"×{value} пикс" + lines[-1] += f"×{value} пикс" elif tmpl: lines.append(tmpl.format(value)) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 5b9ea110..c8311b5b 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -81,7 +81,7 @@ class FetchPlan(BaseModel): """ initial_head: int = 2048 - expand_chunk: int = 8196 + expand_chunk: int = 8192 max_head: int = 128_000 min_head: int = 2048 need_tail: int = 0 @@ -1604,7 +1604,7 @@ def _webp_parse( # noqa: C901 ) result["error_desc"] = ( "Длительность и частота кадров определены " - "экстраполирвоанием (приблизительно)" + "экстраполированием (приблизительно)" ) else: result["duration"] = scanned_duration_sec @@ -1669,7 +1669,7 @@ def _gif_parse_info( result["duration"] = scanned_duration_sec * ratio result["error_desc"] = ( "Длительность и частота кадров определены " - "экстраполирвоанием (приблизительно)" + "экстраполированием (приблизительно)" ) else: # Файл маленький или размер неизвестен — возвращаем как есть @@ -2576,7 +2576,7 @@ def _m4a_parse_audio_info(cls, data: bytes) -> dict | None: return out @staticmethod - def _m4a_parse_mvhd_duration(data: bytes) -> int | None: + def _m4a_parse_mvhd_duration(data: bytes) -> float | None: """Парсит длительность из mvhd атома.""" if len(data) < 20: return None diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py index fbe4a95c..38aaaa45 100644 --- a/tests/test_utils/fileinfo/prepare_fixtures.py +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -201,7 +201,7 @@ async def get_expected(name, cfg, session) -> tuple[bytes, bytes]: or len(expected) <= 3 ): logger.warning( - "Не достаточно данных в expected для %s: %s", + "Недостаточно данных в expected для %s: %s", name, expected, ) @@ -242,7 +242,7 @@ async def generate_fixture(name, cfg, session, head, tail) -> dict | None: fixture.update( { - "mine_type": content_type, + "mime_type": content_type, "file_size": file_size, } ) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 711301bc..1dc77eb1 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -65,7 +65,7 @@ def _make_fixture_named_bytes_io(name, head, tail, exp) -> NamedBytesIO: ext = mimetypes.guess_extension(mime) or ".bin" file_name = f"{name}{ext}" - file_size = exp.get("file_size") or (len(head) + len(tail)) + file_size = exp.get("file_size") or (len(head) + len(tail) if tail else 0) full = bytearray(file_size) full[: len(head)] = head @@ -161,7 +161,7 @@ async def read_tail(n: int = -1) -> bytes: if self._tail_used: return b"" self._tail_used = True - return self.tail or b"self.tail empty" + return self.tail or b"" resp.content.read = read_tail resp.read = AsyncMock(return_value=self.tail) @@ -289,21 +289,24 @@ async def test_generic_exception_returns_error_status(self): assert info.error_desc == "unexpected" async def test_retry_then_success(self): - """503 → retry → успех.""" + """Проверка retry/call_count""" + minimal_jpeg = b"\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01" factory = MockResponseFactory( - head=b"data", tail=b"", content_type="image/jpeg", file_size=4 + head=minimal_jpeg, tail=b"", content_type="image/jpeg", file_size=4 ) meta_resp = factory.make_head_response("url") # для _fetch_meta bad = factory.make_head_response("url") bad.status, bad.ok = 503, False good = factory.make_head_response("url") session = AsyncMock() + # 503 → retry → 200. session.get = AsyncMock(side_effect=[meta_resp, bad, good]) info = await FileInspector().inspect_url( "https://x.com/x.jpg", session=session, retry_backoff_factor=0 ) - assert info.status == "error" + assert info.status == "partial" + assert info.format == "JPEG" assert session.get.call_count == 3 async def test_retry_exhausted(self): @@ -561,9 +564,12 @@ class TestBotGetFileInfo: async def test_returns_fileinfo(self, bot): """Возвращает FileInfo с полями.""" - head = b"\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01" # минимальный JPEG + minimal_jpeg = b"\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01" session = _make_mock_session( - head=head, tail=b"", content_type="image/jpeg", file_size=len(head) + head=minimal_jpeg, + tail=b"", + content_type="image/jpeg", + file_size=len(minimal_jpeg), ) info = await bot.get_file_info( From ee46a4cce5c80933966035d6b5c888d046659cae Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 14:01:37 +0300 Subject: [PATCH 06/31] =?UTF-8?q?fix:=20=D0=A1=D0=B1=D0=BE=D1=80=D0=BA?= =?UTF-8?q?=D0=B0=20=D1=81=D1=82=D1=80=D0=BE=D0=BA=D0=B8=20FileInfo.=5F=5F?= =?UTF-8?q?str=5F=5F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- maxapi/types/file_info.py | 42 ++++++++++++++++++--------------------- 1 file changed, 19 insertions(+), 23 deletions(-) diff --git a/maxapi/types/file_info.py b/maxapi/types/file_info.py index 0a498842..1a67c47f 100644 --- a/maxapi/types/file_info.py +++ b/maxapi/types/file_info.py @@ -140,30 +140,26 @@ def __eq__(self, other: object) -> bool: def __str__(self) -> str: """Форматированная строка для вывода пользователю.""" - # fmt: off - lines = [ f"Имя файла: {self.file_name}", - f"Размер: {self.file_size_human}", + lines = [ + f"Имя файла: {self.file_name}", + f"Размер: {self.file_size_human}", ] - _FIELD_LABELS = ( - ("format", "Формат: {}"), - ("width", " Размеры: {}"), - ("height", None), # добавляется к width - ("duration", " Длительность: {} сек"), - ("fps", " Частота кадров: {} к/с"), - ("sample_rate", " Аудио: {} Гц"), - ("bitrate_nominal", " Битрейт (номинальный): {} кбит/с"), - ("bitrate_avg", " Битрейт (средний): {} кбит/с"), - ) - # fmt: on - for field, tmpl in _FIELD_LABELS: - value = getattr(self, field, None) - if not value: - continue - if field == "height": - lines[-1] += f"×{value} пикс" - elif tmpl: - lines.append(tmpl.format(value)) - + if self.format: + lines.append(f"Формат: {self.format}") + if self.width and self.height: + lines.append(f"Размеры: {self.width}×{self.height} пикс") + if self.duration: + lines.append(f"Длительность: {self.duration} сек") + if self.fps: + lines.append(f"Частота кадров: {self.fps} к/с") + if self.sample_rate: + lines.append(f"Аудио: {self.sample_rate} Гц") + if self.bitrate_nominal: + lines.append( + f"Битрейт (номинальный): {self.bitrate_nominal} кбит/с" + ) + if self.bitrate_avg: + lines.append(f"Битрейт (средний): {self.bitrate_avg} кбит/с") if self.error_desc: lines.append(f"⚠️ {self.error_desc}") From 6306369dc4819e763bcc82e7ef3a31e2d0cd7866 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 14:07:02 +0300 Subject: [PATCH 07/31] =?UTF-8?q?fix:=20=D0=A1=D0=B1=D0=BE=D1=80=D0=BA?= =?UTF-8?q?=D0=B0=20=D1=81=D1=82=D1=80=D0=BE=D0=BA=D0=B8=20FileInfo.str=20?= =?UTF-8?q?(2)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- maxapi/types/file_info.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/maxapi/types/file_info.py b/maxapi/types/file_info.py index 1a67c47f..eb6cc24b 100644 --- a/maxapi/types/file_info.py +++ b/maxapi/types/file_info.py @@ -140,10 +140,12 @@ def __eq__(self, other: object) -> bool: def __str__(self) -> str: """Форматированная строка для вывода пользователю.""" - lines = [ - f"Имя файла: {self.file_name}", - f"Размер: {self.file_size_human}", - ] + lines = [] + if self.file_name: + lines.append(f"Имя файла: {self.file_name}") + else: + lines.append("[Без имени]") + lines.append(f"Размер: {self.file_size_human}") if self.format: lines.append(f"Формат: {self.format}") if self.width and self.height: From fbb5f81811725025ad38e2c30fb0f8e41399eb6a Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 18 May 2026 20:18:18 +0300 Subject: [PATCH 08/31] =?UTF-8?q?fix:=20some=20copilot=20comments=20-=20mp?= =?UTF-8?q?4,=20m4a=20check=20-=20=D0=BE=D0=BF=D0=B5=D1=87=D0=B0=D1=82?= =?UTF-8?q?=D0=BA=D0=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- maxapi/utils/file_inspector.py | 8 +++++--- tests/test_utils/fileinfo/test_file_info.py | 2 +- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index c8311b5b..4eeb9d1f 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -919,7 +919,7 @@ async def inspect_file( async def inspect_bytes( self, - data: bytes | BytesIO, + data: bytes | BytesIO | NamedBytesIO, *, file_name: str = "", full_read_limit: int = 20_971_520, # 20 Мб @@ -1725,9 +1725,11 @@ def _mp4_parse_info( Ищет атом moov в head и tail. Для файлов с moov в конце (потоковая запись) нужен tail. """ - result = None + result = {} if cls._mp4_check(data): result = {"format": "MP4"} + elif cls._m4a_check(data): + result = {"format": "M4A"} # Ищем moov в head dims = cls._mp4_find_moov(data) @@ -1742,7 +1744,7 @@ def _mp4_parse_info( result.update(dims) return result - return result + return result or None @classmethod def _mp4_find_moov(cls, data: bytes) -> dict | None: diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 1dc77eb1..3d9dbddd 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -412,7 +412,7 @@ class TestFileInspectorBytes: """Тесты FileInspector с локальными файлами.""" @pytest.mark.parametrize("name", FIXTURES_ID) - async def test_bytes_butesio_namedbytesio_match(self, name, all_fixtures): + async def test_bytes_bytesio_namedbytesio_match(self, name, all_fixtures): """bytes, BytesIO, NamedBytesIO дают идентичные результаты""" head, tail, exp = all_fixtures[name] From 9d432cde095d27d515a7e50cfb657682c9a6e48d Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Tue, 19 May 2026 09:06:44 +0300 Subject: [PATCH 09/31] =?UTF-8?q?fix:=20=D0=9E=D0=BF=D1=82=D0=B8=D0=BC?= =?UTF-8?q?=D0=B8=D0=B7=D0=B8=D1=80=D0=BE=D0=B2=D0=B0=D0=BD=D1=8B=20=D1=81?= =?UTF-8?q?=D0=B5=D1=82=D0=B5=D0=B2=D1=8B=D0=B5=20=D0=B7=D0=B0=D0=BF=D1=80?= =?UTF-8?q?=D0=BE=D1=81=D1=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit RangeDownloader теперь не делает отдельный GET в _fetch_meta(), а сохраняет ответ для использования при чтении head --- maxapi/utils/file_inspector.py | 53 +++++++++++++++++----------------- 1 file changed, 26 insertions(+), 27 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 4eeb9d1f..ebe26a85 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -572,26 +572,23 @@ async def close(self): async def _fetch_meta(self): """Получает метаинформацию с retry.""" - response = await self._request_with_retry(self.original_url) - async with response: - final_url = str(response.url) - http_headers = response.headers - content_type = http_headers.get("Content-Type", "") - file_name = self._extract_filename(http_headers, self.original_url) + self._response = await self._request_with_retry(self.original_url) + final_url = str(self._response.url) + http_headers = self._response.headers + content_type = http_headers.get("Content-Type", "") + file_name = self._extract_filename(http_headers, self.original_url) - try: - file_size = ( - int(http_headers.get("Content-Length", "0")) or None - ) - except ValueError: - file_size = None - - self._meta = FileMeta( - url=final_url, - content_type=content_type, - file_name=file_name, - file_size=file_size, - ) + try: + file_size = int(http_headers.get("Content-Length", "0")) or None + except ValueError: + file_size = None + + self._meta = FileMeta( + url=final_url, + content_type=content_type, + file_name=file_name, + file_size=file_size, + ) # ======================================================================== # Private: Fetch with retry @@ -613,13 +610,12 @@ async def _fetch_chunk( if not self._meta: raise RuntimeError("Метаинформация не загружена.") - response = await self._request_with_retry( - self._meta.url, - allow_range=tail, - range_bytes=size if tail else None, - ) - if tail: + response = await self._request_with_retry( + self._meta.url, + allow_range=tail, + range_bytes=size if tail else None, + ) async with response: if response.status not in (200, 206): logger.debug( @@ -628,8 +624,11 @@ async def _fetch_chunk( return b"" return await self._read_response(response, size) else: - # Head: сохраняем соединение для докачки - self._response = response + response = self._response + if not response: + raise RuntimeError( + "Response отсутвует. Сначала нужно запросить _fetch_meta()" + ) data = await self._read_response(response, size) # Проверка: если tail повторяет начало head, From 0e395ace46d570a1324496b5a654b986e1bdf34a Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Tue, 19 May 2026 10:18:47 +0300 Subject: [PATCH 10/31] =?UTF-8?q?fix:=20=D0=9E=D0=BF=D1=80=D0=B0=D0=B2?= =?UTF-8?q?=D0=BA=D0=B0=20headers=20c=20auth?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Проверка на содержание Authorization или Cookie Параметр allow_external_auth для разрешения + docstrings --- maxapi/bot.py | 18 +++- maxapi/utils/file_inspector.py | 96 +++++++++++++++++-- tests/test_utils/fileinfo/prepare_fixtures.py | 2 +- 3 files changed, 101 insertions(+), 15 deletions(-) diff --git a/maxapi/bot.py b/maxapi/bot.py index 1611ed37..0b6a9ae6 100644 --- a/maxapi/bot.py +++ b/maxapi/bot.py @@ -753,13 +753,21 @@ async def get_file_info( Args: url: URL файла. timeout: Таймаут HTTP-запроса в секундах. + kwargs: - - session: Общая aiohttp-сессия (создаётся при ``None``). - - max_total: Максимальный объём скачанных данных (байт). - - max_retries: Число повторных попыток при ``retry_on_statuses``. - - retry_on_statuses: HTTP-статусы, при которых повторять запрос. - - retry_backoff_factor: Множитель задержки между попытками + - session : aiohttp-сессия (создаётся при ``None``). + Если вам нужно отправить авторизацию (``Authorization``, + ``Cookie``) на сторонний URL, укажите + ``allow_external_auth=True``. Без этого флага авторизация + отправляется только на доверенные домены + (``oneme.ru``, ``okcdn.ru``). + - max_total : Максимальный объём скачанных данных (байт). + - max_retries : Число повторных попыток при ``retry_on_statuses``. + - retry_on_statuses : HTTP-статусы, при которых повторять запрос. + - retry_backoff_factor : Множитель задержки между попытками (1.0 → 1с, 2с, 4с). + - allow_external_auth : Разрешить отправку авторизации на + сторонние домены (по умолчанию только oneme.ru/okcdn.ru). Returns: FileInfo: Метаинформация о файле. diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index ebe26a85..99d7b436 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -20,8 +20,15 @@ import aiohttp import anyio -from aiohttp import ClientConnectionError, ClientResponse, ClientTimeout +from aiohttp import ( + ClientConnectionError, + ClientResponse, + ClientTimeout, + RequestInfo, +) +from multidict import CIMultiDict from pydantic import BaseModel +from yarl import URL from ..connection.base import NamedBytesIO from ..types.file_info import FileInfo @@ -40,7 +47,7 @@ # 503 — Service Unavailable (сервер перегружен или на обслуживании) # 504 — Gateway Timeout (промежуточный прокси не дождался ответа) DEFAULT_RETRY_STATUSES: tuple[int, ...] = (429, 500, 502, 503, 504) - +_TRUSTED_DOMAINS = {"oneme.ru", "okcdn.ru"} _FORMAT_TO_MIME: dict[str, str] = { "JPEG": "image/jpeg", "PNG": "image/png", @@ -419,15 +426,35 @@ async def close(self): class RangeDownloader(RangeReader): """ - Самодостаточный загрузчик: определяет тип файла, планирует стратегию, - скачивает данные не разрывая соединение. + Загрузчик файлов по HTTP с докачкой и retry. Использование: - async with RangeDownloader(url, session=session) as downloader: - async for chunks in downloader: - if has_enough(chunks): - await downloader.success() + async with RangeDownloader(url, session=session) as dl: + async for chunks in dl: + dims = parse(chunks.head, chunks.tail, ...) + if dims.get("width"): + await dl.success() break + + Args: + url: URL файла. + session: aiohttp-сессия (создаётся при ``None``). + **Внимание:** Если передана сессия с авторизацией + (``Authorization``, ``Cookie``), эти заголовки будут + отправлены на указанный URL. Для публичных URL + не передавайте сессию — она будет создана автоматически + без чувствительных заголовков. + headers: Дополнительные HTTP-заголовки. + max_total: Максимальный объём скачанных данных (байт). + min_total: Минимальный гарантированный объём (байт). + timeout: Таймаут HTTP-запроса в секундах. + sock_connect: Таймаут установки TCP-соединения в секундах. + max_retries: Число повторных попыток при ``retry_on_statuses``. + retry_on_statuses: HTTP-статусы, при которых повторять запрос. + retry_backoff_factor: Множитель задержки между попытками + (1.0 → 1с, 2с, 4с). + allow_external_auth: Разрешить отправку авторизации на + сторонние домены (по умолчанию только oneme.ru или okcdn.ru). """ def __init__( @@ -442,6 +469,7 @@ def __init__( max_retries: int = 3, retry_on_statuses: tuple[int, ...] = DEFAULT_RETRY_STATUSES, retry_backoff_factor: float = 1.0, + allow_external_auth: bool = False, ): super().__init__( plan=FetchPlan(), @@ -457,6 +485,7 @@ def __init__( self.max_retries = max_retries self.retry_on_statuses = retry_on_statuses self.retry_backoff_factor = retry_backoff_factor + self._allow_external_auth = allow_external_auth # Сессия self._own_session = session is None @@ -485,6 +514,15 @@ def __init__( def final_url(self) -> str: return self._meta.url if self._meta else self.original_url + @property + def _is_trusted_url(self) -> bool: + """Проверяет, принадлежит ли URL доверенному домену.""" + host = urlparse(self.original_url).hostname or "" + return any( + host == domain or host.endswith("." + domain) + for domain in _TRUSTED_DOMAINS + ) + # ======================================================================== # Async Context Manager # ======================================================================== @@ -572,6 +610,39 @@ async def close(self): async def _fetch_meta(self): """Получает метаинформацию с retry.""" + if ( + ("Authorization" in self.headers or "Cookie" in self.headers) + and not self._is_trusted_url + and not self._allow_external_auth + ): + logger.warning( + "Сессия содержит авторизацию и будет " + "отправлена на сторонний URL: %s. " + "Передайте allow_external_auth=True чтобы разрешить.", + self.original_url, + ) + self._meta = FileMeta( + url=self.original_url, + content_type="", + file_name="", + file_size=None, + ) + self._fetched_meta = True + # исключение, поймается в inspect_url + # и вернёт FileInfo(status="error") + raise aiohttp.ClientResponseError( + status=0, + message="Сессия с авторизацией не может быть отправлена " + "на сторонний URL. Передайте allow_external_auth=True " + "чтобы разрешить.", + headers=CIMultiDict(self.headers), + request_info=RequestInfo( + url=URL(self.original_url), + method="GET", + headers={}, + ), + history=(), + ) self._response = await self._request_with_retry(self.original_url) final_url = str(self._response.url) http_headers = self._response.headers @@ -842,13 +913,20 @@ async def inspect_url( Args: url: URL файла. - session: Общая aiohttp-сессия (создаётся при ``None``). + session: aiohttp-сессия (создаётся при ``None``). + Если вам нужно отправить авторизацию (``Authorization``, + ``Cookie``) на сторонний URL, укажите + ``allow_external_auth=True``. Без этого флага авторизация + отправляется только на доверенные домены + (``oneme.ru``, ``okcdn.ru``). timeout: Таймаут HTTP-запроса в секундах. max_total: Максимальный объём скачанных данных (байт). max_retries: Число повторных попыток при ``retry_on_statuses``. retry_on_statuses: HTTP-статусы, при которых повторять запрос. retry_backoff_factor: Множитель задержки между попытками (1.0 → 1с, 2с, 4с). + allow_external_auth: Разрешить отправку авторизации на + сторонние домены (по умолчанию только oneme.ru/okcdn.ru). Returns: FileInfo: Результат инспекции (в т.ч. при сетевой ошибке). diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py index 38aaaa45..d08deeed 100644 --- a/tests/test_utils/fileinfo/prepare_fixtures.py +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -16,7 +16,7 @@ - Подходит для быстрого добавления новых фикстур - Полученные значения нужно проверить вручную перед коммитом! -Файл содержит примеры SAMPLE_URLS. Отрредактировать для добавления новых. +Файл содержит примеры SAMPLE_URLS. Отредактировать для добавления новых. Фикстуры не затираются полностью: - Существующие в fixtures.json остаются нетронутыми From ca3032893f53cae032b292ca399152c0c54c4486 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Tue, 19 May 2026 17:56:52 +0300 Subject: [PATCH 11/31] =?UTF-8?q?refactor:=20=D0=9E=D0=BF=D1=82=D0=B8?= =?UTF-8?q?=D0=BC=D0=B8=D0=B7=D0=B0=D1=86=D0=B8=D1=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Упрощение (уменьшение) кода: - mp4 m4a теперь общий парсер - удалён класс MediaChunks за ненадобностью fix: prepare_fixtures отключет полную загрузку файла в фикстуру. fix: передача параметра allow_external_auth + docstrings --- maxapi/utils/file_inspector.py | 172 +++++++++--------- tests/test_utils/fileinfo/prepare_fixtures.py | 4 +- 2 files changed, 85 insertions(+), 91 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 99d7b436..b6a15e82 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -26,7 +26,7 @@ ClientTimeout, RequestInfo, ) -from multidict import CIMultiDict +from multidict import CIMultiDict, CIMultiDictProxy from pydantic import BaseModel from yarl import URL @@ -36,7 +36,6 @@ if TYPE_CHECKING: from collections.abc import AsyncIterator, Callable - from multidict import CIMultiDictProxy logger = logging.getLogger("maxapi.fileinfo") @@ -205,17 +204,6 @@ def from_content_type( # noqa: C901 return cls() -class MediaChunks(BaseModel): - """Фрагменты файла, доступные парсеру.""" - - head: bytes = b"" - tail: bytes = b"" - file_size: int | None = None - is_complete: bool = False - fetched_head: int = 0 - fetched_tail: int = 0 - - class FileMeta(BaseModel): """HTTP-метаданные до чтения тела файла.""" @@ -248,25 +236,11 @@ def __init__( self.tail: bytes = b"" @abstractmethod - def __aiter__(self) -> AsyncIterator[MediaChunks]: ... + def __aiter__(self) -> AsyncIterator[None]: ... @abstractmethod async def close(self): ... - def _make_chunks(self) -> MediaChunks: - is_complete = ( - self.file_size is not None - and len(self.head) + len(self.tail) >= self.file_size - ) - return MediaChunks( - head=self.head, - tail=self.tail, - file_size=self.file_size, - is_complete=is_complete, - fetched_head=len(self.head), - fetched_tail=len(self.tail), - ) - # ============================================================================ # [ ] RangeFileReader @@ -308,24 +282,23 @@ def __init__( ) super().__init__(plan, content_type, file_name, file_size) - async def __aiter__(self) -> AsyncIterator[MediaChunks]: - # 1. Tail - if self.plan.need_tail > 0: - async with await anyio.open_file(self.path, "rb") as f: + async def __aiter__(self) -> AsyncIterator[None]: + async with await anyio.open_file(self.path, "rb") as f: + # 1. Tail + if self.plan.need_tail > 0: await f.seek( max(0, (self.file_size or 0) - self.plan.need_tail) ) self.tail = await f.read() - yield self._make_chunks() + await f.seek(0) - # 2. Head + expand - async with await anyio.open_file(self.path, "rb") as f: + # 2. Head + expand # Первый чанк self.head = await f.read(self.plan.initial_head) logger.debug( "head len=%s, tail len=%s", len(self.head), len(self.tail) ) - yield self._make_chunks() + yield # Докачка while ( @@ -336,7 +309,7 @@ async def __aiter__(self) -> AsyncIterator[MediaChunks]: if not chunk: break self.head += chunk - yield self._make_chunks() + yield async def close(self): pass # anyio.open_file закрывается через async with @@ -387,12 +360,11 @@ def __init__( super().__init__(plan, content_type, self._file_name, self._file_size) self._raw = raw - async def __aiter__(self) -> AsyncIterator[MediaChunks]: + async def __aiter__(self) -> AsyncIterator[None]: # 1. Tail if self.plan.need_tail > 0: tail_start = max(0, self._file_size - self.plan.need_tail) self.tail = bytes(self._raw[tail_start:]) - yield self._make_chunks() # 2. Head + expand (синхронно, без await) pos = 0 @@ -409,8 +381,7 @@ async def __aiter__(self) -> AsyncIterator[MediaChunks]: logger.debug( "head len=%s, tail len=%s", len(self.head), len(self.tail) ) - - yield self._make_chunks() + yield if end >= self._file_size: break @@ -542,7 +513,7 @@ async def __aexit__(self, *args): # Итерация по чанкам # ======================================================================== - async def __aiter__(self) -> AsyncIterator[MediaChunks]: + async def __aiter__(self) -> AsyncIterator[None]: if self._closed: return @@ -565,24 +536,26 @@ async def __aiter__(self) -> AsyncIterator[MediaChunks]: self.plan.need_tail, self.plan.max_head, ) + else: + self._meta = cast(FileMeta, self._meta) - # 1. Tail + # 1. Head + if self.plan.initial_head > 0: + self.head = await self._fetch_chunk( + self.plan.initial_head, + tail=False, + ) + + # 2. Tail if self.plan.need_tail > 0: self.tail = await self._fetch_chunk( self.plan.need_tail, tail=True, ) - yield self._make_chunks() - # 2. Head - if self.plan.initial_head > 0: - self.head = await self._fetch_chunk( - self.plan.initial_head, - tail=False, - ) - yield self._make_chunks() + yield - # 3. Докачка + # 3. Докачка head self._expand_count = 0 # Сброс перед докачкой while ( self.plan.expand_chunk > 0 and len(self.head) < self.plan.max_head @@ -591,7 +564,7 @@ async def __aiter__(self) -> AsyncIterator[MediaChunks]: if not chunk: break self.head += chunk - yield self._make_chunks() + yield # ======================================================================== # Управление @@ -639,7 +612,7 @@ async def _fetch_meta(self): request_info=RequestInfo( url=URL(self.original_url), method="GET", - headers={}, + headers=CIMultiDictProxy(CIMultiDict(self.headers)), ), history=(), ) @@ -693,7 +666,15 @@ async def _fetch_chunk( "Range не поддерживается: %s", response.status ) return b"" - return await self._read_response(response, size) + data = await self._read_response(response, size) + # Проверка: если tail повторяет начало head, + # то Range не поддерживается + if ( + self.head + and data[: len(self.head)] == self.head[: len(data)] + ): + logger.debug("Range не поддерживается: tail == head") + return b"" else: response = self._response if not response: @@ -702,21 +683,11 @@ async def _fetch_chunk( ) data = await self._read_response(response, size) - # Проверка: если tail повторяет начало head, - # то Range не поддерживается - if ( - self.tail - and len(data) >= len(self.tail) - and data[: len(self.tail)] == self.tail - ): - logger.debug("Range не поддерживается: tail == head") - self.tail = b"" - - logger.debug( - "Скачан %s: %s байт", "tail" if tail else "head", len(data) - ) + logger.debug( + "Скачан %s: %s байт", "tail" if tail else "head", len(data) + ) - return data + return data async def _read_response( self, response: ClientResponse, size: int @@ -907,6 +878,7 @@ async def inspect_url( max_retries: int = 3, retry_on_statuses: tuple[int, ...] = DEFAULT_RETRY_STATUSES, retry_backoff_factor: float = 1.0, + allow_external_auth: bool = False, ) -> FileInfo: """ Инспектирует удалённый файл по URL. @@ -941,8 +913,10 @@ async def inspect_url( max_retries=max_retries, retry_on_statuses=retry_on_statuses, retry_backoff_factor=retry_backoff_factor, + allow_external_auth=allow_external_auth, ) as reader: return await self._inspect(reader, url=url) + except aiohttp.ClientError as e: logger.error("Сетевая ошибка: %s", e) self.last_file_info = self._build_file_info( @@ -970,6 +944,9 @@ async def inspect_file( Args: path: Путь к файлу. full_read_limit: Файлы меньше этого размера читаются целиком. + Установите в ноль, чтоюы отключить полное чтение. + В таком случае будет использован план загрузки как для + inspect_url Returns: FileInfo: Результат инспекции. @@ -1033,9 +1010,9 @@ async def _inspect( """Общая логика для любого источника (RangeReader).""" self._last_reader = reader dims = {} - async for chunks in reader: + async for _ in reader: # Проверка: не HTML - if self._looks_like_html(chunks.head, reader.content_type): + if self._looks_like_html(reader.head, reader.content_type): self.last_file_info = self._build_file_info( url=url, mime_type=reader.content_type, @@ -1048,8 +1025,8 @@ async def _inspect( # Парсим dims = ( self.parse_media_dimensions( - chunks.head, - chunks.tail, + reader.head, + reader.tail, reader.content_type, reader.file_size, ) @@ -1073,8 +1050,8 @@ async def _inspect( if self._is_complete(dims, content_type): logger.debug( "Использовано финально head_len=%s, tail_len=%s", - len(chunks.head), - len(chunks.tail), + len(reader.head), + len(reader.tail), ) self.last_file_info = self._build_file_info( @@ -1192,6 +1169,10 @@ def parse_media_dimensions( if len(head) < 2: return None + if not tail and len(head) == file_size: + # файл целиком + tail = head + if not content_type or content_type == "application/octet-stream": # Если сервер не определил тип файла # Проверим все типы по содержанию @@ -1291,7 +1272,7 @@ def _parse_video_dimensions( # noqa: C901 # MP4 / MOV — сигнатура ftyp или moov if cls._mp4_check(head): - return cls._mp4_parse_info(head, tail) + return cls._mp4_m4a_parse_info(head, tail) # AVI — сигнатура RIFF AVI if cls._avi_check(head): @@ -1318,7 +1299,7 @@ def _parse_video_dimensions( # noqa: C901 # Fallback на content_type, если байты не распознаны # Это полезно, если файл обрезан или сигнатура нестандартна if content_type in ("video/mp4", "video/quicktime"): - return cls._mp4_parse_info(head) + return cls._mp4_m4a_parse_info(head) if content_type in ("video/x-msvideo", "video/msvideo"): return cls._avi_parse_info(head) if content_type == "video/webm": @@ -1379,7 +1360,7 @@ def _parse_audio_dimensions( # noqa: C901 # M4A (ftyp) if cls._m4a_check(head): - return cls._m4a_parse_audio_info(head) + return cls._mp4_m4a_parse_info(head) # WMA / ASF if cls._wma_check(head): @@ -1390,7 +1371,7 @@ def _parse_audio_dimensions( # noqa: C901 if content_type in ("audio/mpeg", "audio/mp3"): return cls._mp3_parse_info(head, tail, file_size) if content_type == "audio/mp4": - return cls._m4a_parse_audio_info(head) + return cls._mp4_m4a_parse_info(head) if content_type in ("audio/ogg", "application/ogg"): return cls._ogg_parse_info(head, tail, file_size) if content_type in ("audio/aac", "audio/x-aac"): @@ -1793,7 +1774,7 @@ def _jpeg_parse(data: bytes) -> dict | None: # ========================================================================= @classmethod - def _mp4_parse_info( + def _mp4_m4a_parse_info( cls, data: bytes, tail: bytes | None = None ) -> dict | None: """ @@ -1801,6 +1782,25 @@ def _mp4_parse_info( Ищет атом moov в head и tail. Для файлов с moov в конце (потоковая запись) нужен tail. + + Схема данных: + ftyp # тип файла (mp42, isom, M4A...) + moov # метаданные + ├── mvhd # длительность + ├── trak (видео) + │ ├── tkhd # width, height + │ └── mdia + │ └── minf + │ └── stbl + │ └── stsd # кодек (avc1, mp4v...) + └── trak (аудио) + ├── tkhd # width=0, height=0 + └── mdia + └── minf + └── stbl + └── stsd + └── mp4a # ← sample_rate здесь (+24 от "mp4a") + mdat # медиа-данные (видео/аудио) """ result = {} if cls._mp4_check(data): @@ -1864,7 +1864,7 @@ def _mp4_moov_parse(cls, data: bytes) -> dict | None: elif atom_type == b"trak": trak_data = data[pos + header_size : pos + size] dims = cls._mp4_parse_trak_for_dims(trak_data) - if dims and cls._mp4_valid_video_dims(dims): + if dims and cls._mp4_is_valid_video_dims(dims): result.update(dims) pos += size @@ -1943,7 +1943,7 @@ def _mp4_parse_tkhd(data: bytes) -> dict | None: return None @staticmethod - def _mp4_valid_video_dims(result: dict | None) -> bool: + def _mp4_is_valid_video_dims(result: dict | None) -> bool: """Проверяет, что размеры видео реалистичны.""" if not result: return False @@ -2646,14 +2646,6 @@ def _mp3_parse_xing_vbri(data: bytes, frame_pos: int) -> dict | None: return None - @classmethod - def _m4a_parse_audio_info(cls, data: bytes) -> dict | None: - result = cls._mp4_parse_info(data) or {} - out = {"format": "M4A"} - if result.get("duration") is not None: - out["duration"] = result["duration"] - return out - @staticmethod def _m4a_parse_mvhd_duration(data: bytes) -> float | None: """Парсит длительность из mvhd атома.""" diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py index d08deeed..15942c5f 100644 --- a/tests/test_utils/fileinfo/prepare_fixtures.py +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -178,7 +178,9 @@ async def get_expected(name, cfg, session) -> tuple[bytes, bytes]: inspector = FileInspector() if "file" in cfg: - finfo = await inspector.inspect_file(cfg["file"]) + finfo = await inspector.inspect_file( + cfg["file"], full_read_limit=0 + ) elif "url" in cfg: finfo = await inspector.inspect_url(cfg["url"], session=session) else: From 9b9923d7c967366b87a62c38d2c7a06323f5b276 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Tue, 19 May 2026 18:48:31 +0300 Subject: [PATCH 12/31] fix: mp4 m4a sample_rate detection fix: test_retry_then_success update fixtures.json --- maxapi/utils/file_inspector.py | 83 ++++++++++++++------- tests/test_utils/fileinfo/fixtures.json | 11 +++ tests/test_utils/fileinfo/test_file_info.py | 5 +- 3 files changed, 71 insertions(+), 28 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index b6a15e82..9bc674dc 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -1799,27 +1799,29 @@ def _mp4_m4a_parse_info( └── minf └── stbl └── stsd - └── mp4a # ← sample_rate здесь (+24 от "mp4a") + └── mp4a # ← sample_rate (+28 от "mp4a") mdat # медиа-данные (видео/аудио) """ - result = {} + result: dict = {} if cls._mp4_check(data): result = {"format": "MP4"} elif cls._m4a_check(data): result = {"format": "M4A"} + else: + return None - # Ищем moov в head - dims = cls._mp4_find_moov(data) - if dims and result: - result.update(dims) - return result - - # Ищем moov в tail - if tail: - dims = cls._mp4_find_moov(tail) - if dims and result: + for chunk in (data, tail or b""): + if not chunk: + continue + dims = cls._mp4_find_moov(chunk) + if dims: result.update(dims) - return result + + if not result.get("sample_rate"): + for chunk in (data, tail or b""): + if sr := cls._mp4_parse_sample_rate(chunk): + result["sample_rate"] = sr + break return result or None @@ -1864,8 +1866,12 @@ def _mp4_moov_parse(cls, data: bytes) -> dict | None: elif atom_type == b"trak": trak_data = data[pos + header_size : pos + size] dims = cls._mp4_parse_trak_for_dims(trak_data) - if dims and cls._mp4_is_valid_video_dims(dims): - result.update(dims) + if dims: + if cls._mp4_is_valid_video_dims(dims): + result.update(dims) + elif dims.get("sample_rate"): + # Всегда проверяем sample_rate, даже для аудио-trak + result["sample_rate"] = dims["sample_rate"] pos += size if size < header_size: @@ -1873,8 +1879,9 @@ def _mp4_moov_parse(cls, data: bytes) -> dict | None: return result or None @classmethod - def _mp4_parse_trak_for_dims(cls, data: bytes) -> dict | None: + def _mp4_parse_trak_for_dims(cls, data: bytes) -> dict | None: # noqa: C901 """Ищет tkhd внутри trak""" + result = {} pos = 0 while pos + 8 <= len(data): size = struct.unpack(">I", data[pos : pos + 4])[0] @@ -1882,23 +1889,34 @@ def _mp4_parse_trak_for_dims(cls, data: bytes) -> dict | None: if size == 0: break + header_size = 16 if size == 1 else 8 if size == 1: - if pos + 16 > len(data): - break size = struct.unpack(">Q", data[pos + 8 : pos + 16])[0] - header_size = 16 - else: - header_size = 8 if atom_type == b"tkhd": - return cls._mp4_parse_tkhd( + dims = cls._mp4_parse_tkhd( data[pos + header_size : pos + size] ) + if dims: + result.update(dims) + elif atom_type == b"mdia": + # Ищем sample_rate внутри mdia → minf → stbl → stsd → mp4a + sr = cls._mp4_parse_sample_rate( + data[pos + header_size : pos + size] + ) + if sr: + result["sample_rate"] = sr pos += size if size < header_size: break - return None + + if "sample_rate" not in result: + sr = cls._mp4_parse_sample_rate(data) + if sr: + result["sample_rate"] = sr + + return result or None @staticmethod def _mp4_parse_tkhd(data: bytes) -> dict | None: @@ -1942,13 +1960,28 @@ def _mp4_parse_tkhd(data: bytes) -> dict | None: } return None + @classmethod + def _mp4_parse_sample_rate(cls, data: bytes) -> int | None: + """Ищет sample_rate в mp4a (AudioSampleEntry, 16.16 fixed point).""" + pos = 0 + while pos < len(data): + mp4a_idx = data.find(b"mp4a", pos) + if mp4a_idx < 0 or mp4a_idx + 32 > len(data): + return None + raw = struct.unpack_from(">I", data, mp4a_idx + 28)[0] + sr = raw >> 16 + if 8000 <= sr <= 192000: + return sr + pos = mp4a_idx + 4 + return None + @staticmethod def _mp4_is_valid_video_dims(result: dict | None) -> bool: """Проверяет, что размеры видео реалистичны.""" if not result: return False - w = result.get("width") or 0 - h = result.get("height") or 0 + w = result.get("width", 0) + h = result.get("height", 0) return ( # Некоторые файлы имеют 0x40000000 (16384.0) как "не задано" not (w == 16_384 and h == 16_384) diff --git a/tests/test_utils/fileinfo/fixtures.json b/tests/test_utils/fileinfo/fixtures.json index ed61d54f..76ceb527 100644 --- a/tests/test_utils/fileinfo/fixtures.json +++ b/tests/test_utils/fileinfo/fixtures.json @@ -328,6 +328,7 @@ "width": 1920, "height": 1080, "duration": 16.0, + "sample_rate": 44100, "bitrate_avg": 5948, "format": "MP4" }, @@ -341,5 +342,15 @@ "sample_rate": 44100, "bitrate_avg": 97, "format": "OGG" + }, + "m4a": { + "head_b64": "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", + "url": "https://github.com/projectivetech/media-samples/raw/refs/heads/master/sample.m4a", + "mime_type": "audio/mp4", + "file_size": 34535, + "duration": 3.4, + "bitrate_avg": 79, + "sample_rate": 44100, + "format": "M4A" } } \ No newline at end of file diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 3d9dbddd..b94e435c 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -294,13 +294,12 @@ async def test_retry_then_success(self): factory = MockResponseFactory( head=minimal_jpeg, tail=b"", content_type="image/jpeg", file_size=4 ) - meta_resp = factory.make_head_response("url") # для _fetch_meta bad = factory.make_head_response("url") bad.status, bad.ok = 503, False good = factory.make_head_response("url") session = AsyncMock() - # 503 → retry → 200. - session.get = AsyncMock(side_effect=[meta_resp, bad, good]) + # 503 → retry → 200 (один GET: meta + head). + session.get = AsyncMock(side_effect=[bad, bad, good]) info = await FileInspector().inspect_url( "https://x.com/x.jpg", session=session, retry_backoff_factor=0 From 487056c287449d5659a52ee2ccbdcc37249d5095 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Tue, 19 May 2026 20:15:48 +0300 Subject: [PATCH 13/31] =?UTF-8?q?refactor:=20=D0=9E=D1=82=D0=BA=D0=B0?= =?UTF-8?q?=D0=B7=20=D0=BE=D1=82=20=D0=BF=D0=BB=D0=B0=D0=BD=D0=B8=D1=80?= =?UTF-8?q?=D0=BE=D0=B2=D0=B0=D0=BD=D0=B8=D1=8F=20=D0=B7=D0=B0=D0=B3=D1=80?= =?UTF-8?q?=D1=83=D0=B7=D0=BA=D0=B8.=20=D0=9F=D0=B0=D1=80=D1=81=D0=B5?= =?UTF-8?q?=D1=80=D1=8B=20=D1=80=D1=83=D0=BB=D1=8F=D1=82.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Удалить FetchPlan как класс. Заменить константами: INITIAL_HEAD=4096, MAX_HEAD=256000, EXPAND_CHUNK=4096, MAX_TAIL=65536. Переработать RangeDownloader.__aiter__: Сразу получаем минимальные GET 4096 байт + meta из заголовков (Content-Type, Length, filename). yield чанк Отправляем в парсер, он возвращает _need_head / _need_tail _need_head == -1 → докачка удвоением (используем существующий) _need_head > 0 → докачка до конкретного размера (используем существующий) _need_tail > 0 → Отдельный Range-запрос Без ключей — хватило Цикл пока парсер просит ещё. Адаптировать FileInspector._inspect под новую логику. Выигрыш: Меньше кода. Меньше Классов. Понятная логика для всех случаев. Надёжнее: план строится по реальным данным, а не по MIME-типу от сервера (который может быть application/octet-stream или врать). Точнее: парсер знает сколько ему нужно или просто просит ещё. Меньше запросов: meta + head в одном GET (вместо двух). Проще расширять: новый формат — только парсер, без правок плана. + Переименованы поля: FileInfo error_desc -> parse_note full_read_limit -> full_read_threshold --- examples/05_media_bot.py | 4 +- maxapi/types/file_info.py | 41 +- maxapi/utils/file_inspector.py | 1170 ++++++++--------- tests/test_utils/fileinfo/prepare_fixtures.py | 2 +- tests/test_utils/fileinfo/test_file_info.py | 26 +- 5 files changed, 580 insertions(+), 663 deletions(-) diff --git a/examples/05_media_bot.py b/examples/05_media_bot.py index 67514e12..c3614a82 100644 --- a/examples/05_media_bot.py +++ b/examples/05_media_bot.py @@ -230,8 +230,8 @@ async def cmd_info(event: MessageCreated) -> None: if info.status == "error": # info.status == "error" только если ничего не получилось определить, # Даже минимально: info.format is None - # info.error_desc содерит описание ошибки - await event.message.answer(f"⚠️ {info.error_desc}") + # info.parse_note содерит описание ошибки + await event.message.answer(f"⚠️ {info.parse_note}") return # Всё, что получилось узнать о файле — в строку через str(FileInfo) diff --git a/maxapi/types/file_info.py b/maxapi/types/file_info.py index eb6cc24b..44e581c3 100644 --- a/maxapi/types/file_info.py +++ b/maxapi/types/file_info.py @@ -19,8 +19,9 @@ class FileInfo(BaseModel): sample_rate: Частота дискретизации (аудио), Гц. bitrate_nominal: Номинальный битрейт из метаданных, кбит/с. bitrate_avg: Средний битрейт (размер / длительность), кбит/с. - error_desc: Описание ошибки или предупреждения. + parse_note: Описание ошибки или предупреждения от парсера. format: Определённый формат контейнера/кодека (по сигнатуре). + status: Результат инспекции (задаёт парсер или inspect). """ model_config = ConfigDict(frozen=True) @@ -36,7 +37,8 @@ class FileInfo(BaseModel): sample_rate: int | None = None bitrate_nominal: int | None = None bitrate_avg: int | None = None - error_desc: str = "" + status: Literal["ok", "partial", "error"] = "error" + parse_note: str = "" format: ( Literal[ "PNG", @@ -95,37 +97,6 @@ def file_size_human(self) -> str: return f"{self.file_size / 1_048_576:.1f} МБ" return f"{self.file_size / 1_073_741_824:.2f} ГБ" - @property - def status(self) -> Literal["ok", "partial", "error", "unknown"]: - """ - Степень полноты метаданных. - - Returns: - ``ok`` — ключевые поля для типа медиа заполнены; - ``partial`` — часть полей отсутствует; - ``error`` — ошибка получения данных; - ``unknown`` — тип медиа не распознан. - """ - if self.error_desc and not self.format: - return "error" - - if self.is_image: - if self.width and self.height: - return "ok" - return "partial" - - elif self.is_audio: - if self.duration and self.sample_rate: - return "ok" - return "partial" - - elif self.is_video: - if self.duration and self.width and self.fps: - return "ok" - return "partial" - else: - return "unknown" - def __eq__(self, other: object) -> bool: """Сравнение без учёта ``url`` и ``file_name``.""" if not isinstance(other, FileInfo): @@ -162,7 +133,7 @@ def __str__(self) -> str: ) if self.bitrate_avg: lines.append(f"Битрейт (средний): {self.bitrate_avg} кбит/с") - if self.error_desc: - lines.append(f"⚠️ {self.error_desc}") + if self.parse_note: + lines.append(f"⚠️ {self.parse_note}") return "\n".join(lines) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 9bc674dc..0b4adf85 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -69,141 +69,22 @@ "MKV": "video/x-matroska", } +# Лимиты частичного чтения (парсеры запрашивают докачку через _need_*). +INITIAL_HEAD = 4096 +MAX_HEAD = 256_000 +EXPAND_CHUNK = 4096 +MAX_TAIL = 65_536 + +# Ключи метаданных ответа парсера (не попадают в FileInfo). +NEED_HEAD = "_need_head" +NEED_TAIL = "_need_tail" +PARSE_STATUS = "_status" # "ok" | "partial" + # ============================================================================ # [ ] Структуры данных # ============================================================================ -class FetchPlan(BaseModel): - """ - План частичного чтения файла. - - Attributes: - initial_head: Размер первого блока с начала файла. - expand_chunk: Размер блока докачки (0 — без докачки). - max_head: Верхняя граница размера head. - min_head: Нижняя граница размера head. - need_tail: Размер блока с конца (0 — хвост не нужен). - """ - - initial_head: int = 2048 - expand_chunk: int = 8192 - max_head: int = 128_000 - min_head: int = 2048 - need_tail: int = 0 - - @classmethod - def from_content_type( # noqa: C901 - cls, - content_type: str, - file_size: int | None = None, - ) -> FetchPlan: - """ - Строит план по MIME-типу и размеру файла. - - Args: - content_type: MIME-тип из заголовков или guess. - file_size: Размер файла в байтах, если известен. - - Returns: - FetchPlan: План чтения для данного типа контента. - """ - # Маленькие файлы качаем целиком - if file_size and file_size <= 64_000: - return cls( - initial_head=file_size, - max_head=file_size, - min_head=file_size, - ) - - # AVI: начало с докачкой - if content_type in ("video/x-msvideo", "video/msvideo"): - return cls( - initial_head=8192, - expand_chunk=4096, - max_head=256000, - ) - - # MP3: может иметь большой ID3v2 и иногда нужен хвост - if content_type in ("audio/mpeg", "audio/mp3"): - return cls( - initial_head=8192, - expand_chunk=8192, - max_head=32_768, - need_tail=8192, - ) - - # Видео с moov/seekhead в конце. - # Без хвоста не возможно определить длительность - if content_type in ( - "audio/ogg", - "video/ogg", - "application/ogg", - "video/ogv", - ): - return cls( - initial_head=8192, - expand_chunk=8192, - need_tail=8192, - ) - - # Видео mp4, если потоковое то все параметры записаны вконце - if content_type == "video/mp4": - return cls( - initial_head=8192, - expand_chunk=8192, - max_head=65_536, - need_tail=24_576, - ) - - # WMA: большой начальный кусок - if content_type in ("audio/x-ms-wma", "audio/wma"): - return cls( - initial_head=8192, - expand_chunk=8192, - ) - - # JPEG - if content_type == "image/jpeg": - if file_size: - # За счёт EXIF+preview информация о размере может быть глубже - # Кривая: резкий рост до ~10 КБ на маленьких файлах, - # плавный до ~30 КБ на средних, пологий до 64 КБ на больших. - # 10 КБ → 2.9 КБ - # 500 КБ → 17.5 КБ - # 1024 КБ → 25 КБ - needed = min(512 + int(24 * (file_size**0.5)), 65536) - else: - needed = 8192 - - return cls( - initial_head=needed, - expand_chunk=needed // 2, - ) - - # GIF/WebP Для оценки длительности анимации нужно >= 3% файла - if content_type in ("image/gif", "image/webp"): - needed = ( - max(20_240, (file_size or 0) // 25) if file_size else 20_240 - ) - needed = min(needed, 256_000) - return cls( - initial_head=needed, - expand_chunk=needed, # Вероятно никогда не будет использовано - max_head=needed, # Чтобы не урезалось - ) - - # По умолчанию для видео - if content_type.startswith("video/"): - return cls( - initial_head=8192, - expand_chunk=8192, - ) - - # По умолчанию - return cls() - - class FileMeta(BaseModel): """HTTP-метаданные до чтения тела файла.""" @@ -223,17 +104,27 @@ class RangeReader(ABC): def __init__( self, - plan: FetchPlan, content_type: str, file_name: str, file_size: int | None, + *, + full_read_threshold: int = 20_971_520, ): - self.plan = plan self.content_type = content_type self.file_name = file_name self.file_size = file_size + self._full_read_threshold = full_read_threshold self.head: bytes = b"" self.tail: bytes = b"" + self.pending_needs: dict[str, int] = {} + self._expand_count = 0 + + def _initial_head_size(self) -> int: + if self.file_size and self.file_size <= MAX_HEAD: + return self.file_size + if self.file_size and self.file_size < self._full_read_threshold: + return self.file_size + return INITIAL_HEAD @abstractmethod def __aiter__(self) -> AsyncIterator[None]: ... @@ -241,6 +132,39 @@ def __aiter__(self) -> AsyncIterator[None]: ... @abstractmethod async def close(self): ... + @abstractmethod + async def _fetch_tail(self, size: int) -> bytes: ... + + @abstractmethod + async def _expand_head(self, *, target: int | None = None) -> bytes: ... + + async def _satisfy_pending_needs(self) -> bool: + """ + Докачивает head/tail по pending_needs. + Возвращает True если что-то скачано. + """ + need_head = self.pending_needs.get(NEED_HEAD, 0) + need_tail = self.pending_needs.get(NEED_TAIL, 0) + self.pending_needs = {} + progressed = False + + if need_tail > 0 and len(self.tail) < need_tail: + self.tail = await self._fetch_tail(min(need_tail, MAX_TAIL)) + progressed = True + + if need_head == -1 and len(self.head) < MAX_HEAD: + chunk = await self._expand_head() + if chunk: + self.head += chunk + progressed = True + elif need_head > len(self.head): + chunk = await self._expand_head(target=min(need_head, MAX_HEAD)) + if chunk: + self.head += chunk + progressed = True + + return progressed + # ============================================================================ # [ ] RangeFileReader @@ -254,7 +178,7 @@ def __init__( self, path: str, *, - full_read_limit: int = 20_971_520, # 20 Мб + full_read_threshold: int = 20_971_520, # 20 Мб ): self.path = path file_path = Path(path) @@ -263,53 +187,53 @@ def __init__( file_size = file_path.stat().st_size content_type, _ = mimetypes.guess_type(path) content_type = content_type or "application/octet-stream" - - if file_size < full_read_limit: - # Небольшие данные будем анализировать целиком - plan = FetchPlan( - initial_head=file_size, - expand_chunk=0, - max_head=file_size, - need_tail=0, - ) - else: - plan = FetchPlan.from_content_type(content_type, file_size) - logger.debug( - "FILE plan: initial=%s, expand=%s, tail=%s", - plan.initial_head, - plan.expand_chunk, - plan.need_tail, + super().__init__( + content_type, + file_name, + file_size, + full_read_threshold=full_read_threshold, ) - super().__init__(plan, content_type, file_name, file_size) + self._file: Any = None async def __aiter__(self) -> AsyncIterator[None]: async with await anyio.open_file(self.path, "rb") as f: - # 1. Tail - if self.plan.need_tail > 0: - await f.seek( - max(0, (self.file_size or 0) - self.plan.need_tail) - ) - self.tail = await f.read() - await f.seek(0) - - # 2. Head + expand - # Первый чанк - self.head = await f.read(self.plan.initial_head) + self._file = f + self.head = await f.read(self._initial_head_size()) logger.debug( "head len=%s, tail len=%s", len(self.head), len(self.tail) ) yield - - # Докачка - while ( - self.plan.expand_chunk > 0 - and len(self.head) < self.plan.max_head - ): - chunk = await f.read(self.plan.expand_chunk) - if not chunk: + while self.pending_needs: + if not await self._satisfy_pending_needs(): break - self.head += chunk + logger.debug( + "head len=%s, tail len=%s", len(self.head), len(self.tail) + ) yield + self._file = None + + async def _fetch_tail(self, size: int) -> bytes: + if not self._file: + return b"" + await self._file.seek(max(0, (self.file_size or 0) - size)) + return await self._file.read() + + async def _expand_head(self, *, target: int | None = None) -> bytes: + if not self._file: + return b"" + if target is not None: + need = min(target, MAX_HEAD) - len(self.head) + if need <= 0: + return b"" + return await self._file.read(need) + chunk_size = min( + EXPAND_CHUNK * (2**self._expand_count), + MAX_HEAD - len(self.head), + ) + self._expand_count += 1 + if chunk_size <= 0: + return b"" + return await self._file.read(chunk_size) async def close(self): pass # anyio.open_file закрывается через async with @@ -328,63 +252,69 @@ def __init__( data: bytes | BytesIO | NamedBytesIO, file_name: str = "", *, - full_read_limit=20_971_520, # 20 Мб + full_read_threshold=20_971_520, # 20 Мб ): if isinstance(data, (BytesIO, NamedBytesIO)): - self._file_name = file_name or getattr(data, "name", "") + file_name = file_name or getattr(data, "name", "") raw = data.getbuffer() # memoryview без копирования else: - self._file_name = file_name raw = memoryview(data) # bytes → memoryview - self._file_size = len(raw) - content_type, _ = mimetypes.guess_type(self._file_name) + file_size = len(raw) + content_type, _ = mimetypes.guess_type(file_name) content_type = content_type or "application/octet-stream" - - if self._file_size < full_read_limit: - # Небольшие данные будем анализировать целиком - plan = FetchPlan( - initial_head=self._file_size, - expand_chunk=0, - max_head=self._file_size, - need_tail=0, - ) - else: - plan = FetchPlan.from_content_type(content_type, self._file_size) - logger.debug( - "BYTES plan: initial=%s, expand=%s, tail=%s", - plan.initial_head, - plan.expand_chunk, - plan.need_tail, + super().__init__( + content_type, + file_name, + file_size, + full_read_threshold=full_read_threshold, ) - super().__init__(plan, content_type, self._file_name, self._file_size) self._raw = raw + self._head_pos = 0 async def __aiter__(self) -> AsyncIterator[None]: - # 1. Tail - if self.plan.need_tail > 0: - tail_start = max(0, self._file_size - self.plan.need_tail) - self.tail = bytes(self._raw[tail_start:]) - - # 2. Head + expand (синхронно, без await) - pos = 0 - while pos < self._file_size: - chunk_size = ( - self.plan.expand_chunk if pos > 0 else self.plan.initial_head - ) - if chunk_size <= 0: + file_size = self.file_size or 0 + end = min(self._initial_head_size(), file_size) + self.head = bytes(self._raw[:end]) + self._head_pos = end + logger.debug( + "head len=%s, tail len=%s", len(self.head), len(self.tail) + ) + yield + while self.pending_needs: + if not await self._satisfy_pending_needs(): break - - end = min(pos + chunk_size, self._file_size) - self.head = bytes(self._raw[:end]) - pos = end logger.debug( "head len=%s, tail len=%s", len(self.head), len(self.tail) ) yield - if end >= self._file_size: - break + async def _fetch_tail(self, size: int) -> bytes: + file_size = self.file_size or 0 + tail_start = max(0, file_size - size) + return bytes(self._raw[tail_start:]) + + async def _expand_head(self, *, target: int | None = None) -> bytes: + file_size = self.file_size or 0 + if target is not None: + end = min(target, MAX_HEAD, file_size) + if end <= self._head_pos: + return b"" + chunk = bytes(self._raw[self._head_pos : end]) + self._head_pos = end + return chunk + chunk_size = min( + EXPAND_CHUNK * (2**self._expand_count), + MAX_HEAD - len(self.head), + file_size - self._head_pos, + ) + self._expand_count += 1 + if chunk_size <= 0: + return b"" + end = self._head_pos + chunk_size + chunk = bytes(self._raw[self._head_pos : end]) + self._head_pos = end + return chunk async def close(self): pass @@ -443,7 +373,6 @@ def __init__( allow_external_auth: bool = False, ): super().__init__( - plan=FetchPlan(), content_type="", file_name="", file_size=None, @@ -479,7 +408,6 @@ def __init__( self._closed: bool = False self._fetched_meta: bool = False self._meta: FileMeta | None = None - self._expand_count = 0 @property def final_url(self) -> str: @@ -517,55 +445,48 @@ async def __aiter__(self) -> AsyncIterator[None]: if self._closed: return - # Получаем метаинформацию и строим план if not self._fetched_meta: - await self._fetch_meta() - self._meta = cast(FileMeta, self._meta) - self.content_type = self._meta.content_type - self.file_name = self._meta.file_name - self.file_size = self._meta.file_size - self.plan = FetchPlan.from_content_type( - self._meta.content_type, - self._meta.file_size, - ) + initial = INITIAL_HEAD + if self.file_size and self.file_size <= MAX_HEAD: + initial = self.file_size + await self._fetch_meta_and_head(initial) self._fetched_meta = True logger.debug( - "URL plan: initial_head=%s, expand=%s, tail=%s, max=%s", - self.plan.initial_head, - self.plan.expand_chunk, - self.plan.need_tail, - self.plan.max_head, - ) - else: - self._meta = cast(FileMeta, self._meta) - - # 1. Head - if self.plan.initial_head > 0: - self.head = await self._fetch_chunk( - self.plan.initial_head, - tail=False, - ) - - # 2. Tail - if self.plan.need_tail > 0: - self.tail = await self._fetch_chunk( - self.plan.need_tail, - tail=True, + "URL initial head=%s, file_size=%s", + len(self.head), + self.file_size, ) yield - # 3. Докачка head - self._expand_count = 0 # Сброс перед докачкой - while ( - self.plan.expand_chunk > 0 and len(self.head) < self.plan.max_head - ): - chunk = await self._expand_head() - if not chunk: + while self.pending_needs: + if not await self._satisfy_pending_needs(): break - self.head += chunk + logger.debug( + "head len=%s, tail len=%s", len(self.head), len(self.tail) + ) yield + async def _fetch_meta_and_head(self, size: int) -> None: + """Один GET: заголовки + первые size байт тела.""" + await self._fetch_meta() + self._meta = cast(FileMeta, self._meta) + self.content_type = self._meta.content_type + self.file_name = self._meta.file_name + self.file_size = self._meta.file_size + if size > 0: + self.head = await self._read_head_bytes(size) + + async def _read_head_bytes(self, size: int) -> bytes: + if not self._response: + raise RuntimeError( + "Response отсутствует. Сначала нужно вызвать _fetch_meta()" + ) + return await self._read_response(self._response, size) + + async def _fetch_tail(self, size: int) -> bytes: + return await self._fetch_chunk(size, tail=True) + # ======================================================================== # Управление # ======================================================================== @@ -701,8 +622,8 @@ async def _read_response( data += chunk return data - async def _expand_head(self) -> bytes: - """Докачивает дополнительный кусок к head с удвоением размера.""" + async def _expand_head(self, *, target: int | None = None) -> bytes: + """Докачивает head: удвоение или до target байт.""" if not self._response or getattr(self._response, "closed", False): return b"" @@ -710,9 +631,19 @@ async def _expand_head(self) -> bytes: if allowed <= 0: return b"" - # Удвоение от начального expand_chunk + if target is not None: + need = min(target, MAX_HEAD) - len(self.head) + if need <= 0: + return b"" + try: + return await self._read_response( + self._response, min(need, allowed) + ) + except Exception: + return b"" + chunk_size = min( - self.plan.expand_chunk * (2**self._expand_count), + EXPAND_CHUNK * (2**self._expand_count), allowed, ) self._expand_count += 1 @@ -921,14 +852,16 @@ async def inspect_url( logger.error("Сетевая ошибка: %s", e) self.last_file_info = self._build_file_info( url=url, - error_desc=f"Сетевая ошибка: {e}", + parse_note=f"Сетевая ошибка: {e}", + status="error", ) return self.last_file_info except Exception as e: logger.exception("Ошибка инспекции: %s", e) self.last_file_info = self._build_file_info( url=url, - error_desc=str(e), + parse_note=str(e), + status="error", ) return self.last_file_info @@ -936,14 +869,14 @@ async def inspect_file( self, path: str, *, - full_read_limit: int = 20_971_520, # 20 Мб + full_read_threshold: int = 20_971_520, # 20 Мб ) -> FileInfo: """ Инспектирует локальный файл. Args: path: Путь к файлу. - full_read_limit: Файлы меньше этого размера читаются целиком. + full_read_threshold: Файлы меньше этого размера читаются целиком. Установите в ноль, чтоюы отключить полное чтение. В таком случае будет использован план загрузки как для inspect_url @@ -956,18 +889,21 @@ async def inspect_file( if not await file_path.exists(): self.last_file_info = self._build_file_info( url=path, - error_desc="Файл не найден", + parse_note="Файл не найден", + status="error", ) return self.last_file_info reader = RangeFileReader( - str(await file_path.resolve()), full_read_limit=full_read_limit + str(await file_path.resolve()), + full_read_threshold=full_read_threshold, ) return await self._inspect(reader, url=path) except Exception as e: logger.exception("Ошибка инспекции файла: %s", e) self.last_file_info = self._build_file_info( url=path, - error_desc=str(e), + parse_note=str(e), + status="error", ) return self.last_file_info @@ -976,7 +912,7 @@ async def inspect_bytes( data: bytes | BytesIO | NamedBytesIO, *, file_name: str = "", - full_read_limit: int = 20_971_520, # 20 Мб + full_read_threshold: int = 20_971_520, # 20 Мб ) -> FileInfo: """ Инспектирует уже загруженные байты. @@ -984,7 +920,7 @@ async def inspect_bytes( Args: data: Содержимое файла. file_name: Имя файла (для guess MIME по расширению). - full_read_limit: Буферы меньше этого размера читаются целиком. + full_read_threshold: Буферы меньше этого размера читаются целиком. Returns: FileInfo: Результат инспекции. @@ -993,7 +929,7 @@ async def inspect_bytes( file_name = file_name or getattr(data, "name", "") reader = RangeBytesReader( - data, file_name, full_read_limit=full_read_limit + data, file_name, full_read_threshold=full_read_threshold ) return await self._inspect(reader, url="") @@ -1009,32 +945,54 @@ async def _inspect( ) -> FileInfo: """Общая логика для любого источника (RangeReader).""" self._last_reader = reader - dims = {} + dims: dict = {} + status: Literal["ok", "partial", "error"] = "partial" + content_type = reader.content_type + async for _ in reader: - # Проверка: не HTML + if reader.content_type: + content_type = reader.content_type + + if not reader.head: + self.last_file_info = self._build_file_info( + url=url, + mime_type=content_type, + file_name=reader.file_name, + file_size=reader.file_size, + status="error", + parse_note="Недостаточно данных для " + "определения параметров", + ) + return self.last_file_info + if self._looks_like_html(reader.head, reader.content_type): self.last_file_info = self._build_file_info( url=url, mime_type=reader.content_type, file_name=reader.file_name, file_size=reader.file_size, - error_desc="Файл не является медиа (HTML-страница)", + status="error", + parse_note="Файл не является медиа (HTML-страница)", ) return self.last_file_info - # Парсим - dims = ( - self.parse_media_dimensions( - reader.head, - reader.tail, - reader.content_type, - reader.file_size, - ) - or {} + raw = self.parse_media_dimensions( + reader.head, + reader.tail, + reader.file_size, ) + if raw is None: + self.last_file_info = self._build_file_info( + url=url, + mime_type=content_type, + file_name=reader.file_name, + file_size=reader.file_size, + status="partial", + ) + return self.last_file_info + + dims, needs, status = self._split_parse_result(raw) - # Уточняем content_type если octet-stream - content_type = reader.content_type fmt = dims.get("format") if ( fmt @@ -1046,31 +1004,31 @@ async def _inspect( ): content_type = _FORMAT_TO_MIME[fmt] - # Достаточно ли данных - if self._is_complete(dims, content_type): + if status == "ok": logger.debug( "Использовано финально head_len=%s, tail_len=%s", len(reader.head), len(reader.tail), ) - self.last_file_info = self._build_file_info( url=url, mime_type=content_type, file_name=reader.file_name, file_size=reader.file_size, dims=dims, + status="ok", ) return self.last_file_info - # Цикл кончился — не хватило данных + reader.pending_needs = needs + self.last_file_info = self._build_file_info( url=url, - mime_type=reader.content_type, + mime_type=content_type, file_name=reader.file_name, file_size=reader.file_size, dims=dims or None, - error_desc="Недостаточно данных для определения параметров", + status=status, ) return self.last_file_info @@ -1078,6 +1036,21 @@ async def _inspect( # Private: хелперы # ======================================================================== + @staticmethod + def _split_parse_result( + raw: dict, + ) -> tuple[dict, dict[str, int], Literal["ok", "partial"]]: + """Отделяет служебные ключи парсера от полей FileInfo.""" + dims = dict(raw) + needs: dict[str, int] = {} + for key in (NEED_HEAD, NEED_TAIL): + if key in dims: + needs[key] = int(dims.pop(key)) + status = dims.pop(PARSE_STATUS, "partial") + if status not in ("ok", "partial"): + status = "partial" + return dims, needs, status + @staticmethod def _looks_like_html(head: bytes, content_type: str) -> bool: """Определяет, похож ли ответ на HTML-страницу.""" @@ -1088,26 +1061,6 @@ def _looks_like_html(head: bytes, content_type: str) -> bool: head_lower = head[:512].lower().lstrip() return head_lower.startswith((b" bool: - """Проверяет, достаточно ли данных для этого типа контента.""" - if not dims: - return False - if content_type.startswith("image/"): - return bool(dims.get("width") and dims.get("height")) - if content_type.startswith("audio/"): - return bool(dims.get("duration") and dims.get("sample_rate")) - if content_type.startswith("video/"): - if dims.get("error_desc") == ( - "Для определения duration необходим " - "конец файла (tail) или файл целиком" - ): - return True - return bool( - dims.get("duration") and dims.get("width") and dims.get("fps") - ) - return bool(dims) - @staticmethod def _build_file_info( url: str = "", @@ -1115,13 +1068,13 @@ def _build_file_info( file_name: str = "", file_size: int | None = None, dims: dict | None = None, - error_desc: str = "", + parse_note: str = "", + status: Literal["ok", "partial", "error"] = "partial", ) -> FileInfo: """Собирает FileInfo из параметров.""" if dims is None: dims = {} - # Вычисляем производные поля bitrate_avg = None if file_size and dims.get("duration"): bitrate_avg = round(file_size / dims["duration"] * 8 / 1024) @@ -1143,7 +1096,8 @@ def _build_file_info( sample_rate=dims.get("sample_rate"), bitrate_nominal=dims.get("bitrate_nominal") or dims.get("bitrate"), bitrate_avg=bitrate_avg, - error_desc=error_desc or dims.get("error_desc", ""), + parse_note=parse_note or dims.get("parse_note", ""), + status=status, ) @classmethod @@ -1151,236 +1105,93 @@ def parse_media_dimensions( cls, head: bytes, tail: bytes | None, - content_type: str | None = None, file_size: int | None = None, ) -> dict | None: """ - Извлекает метаданные из фрагментов файла. - - Args: - head: Байты с начала файла. - tail: Байты с конца (если нужны для формата). - content_type: MIME-тип, опционально - file_size: Полный размер файла, если известен. + Извлекает метаданные из фрагментов файла (только по сигнатуре). Returns: - Словарь полей для :class:`FileInfo` или ``None``. + Словарь с полями FileInfo и служебными ключами + (``_need_head``, ``_need_tail``, ``_status``), или ``None``. """ if len(head) < 2: return None - if not tail and len(head) == file_size: - # файл целиком + if not tail and file_size and len(head) == file_size: tail = head - if not content_type or content_type == "application/octet-stream": - # Если сервер не определил тип файла - # Проверим все типы по содержанию - result = cls._parse_image_dimensions(head, "", file_size) - if result: - return result - result = cls._parse_video_dimensions(head, tail, "", file_size) - if result: - return result - result = cls._parse_audio_dimensions(head, tail, "", file_size) - if result: + for parser in ( + lambda: cls._parse_image_dimensions(head, file_size), + lambda: cls._parse_video_dimensions(head, tail, file_size), + lambda: cls._parse_audio_dimensions(head, tail, file_size), + ): + if result := parser(): return result - return None - - if content_type.startswith("image/"): - return cls._parse_image_dimensions(head, content_type, file_size) - - if content_type.startswith("video/"): - return cls._parse_video_dimensions( - head, tail, content_type, file_size - ) - - if content_type.startswith("audio/"): - return cls._parse_audio_dimensions( - head, tail, content_type, file_size - ) - return None @classmethod def _parse_image_dimensions( - cls, data: bytes, content_type: str, file_size: int | None + cls, + data: bytes, + file_size: int | None, ) -> dict | None: """ - Парсит метаданные изображений, определяя формат по сигнатурам, - с fallback на content_type. + Изображения: PNG, JPEG, GIF, WEBP (по сигнатуре). - Args: - data: начальные байты файла (head). - content_type: MIME-тип из заголовков HTTP. - file_size: размер файла в байтах (опционально). - - Returns: - dict с ключами format, width, height или None. + Может: format, width, height; GIF/аним. WEBP — duration, fps. """ - - # WEBP — сигнатура RIFF WEBP if cls._webp_check(data): return cls._webp_parse(data, file_size) - - # PNG — сигнатура 8 байт + IHDR if cls._png_check(data): return cls._png_parse(data) - - # JPEG — сигнатура \xFF\xD8 if cls._jpg_check(data): return cls._jpeg_parse(data) - - # GIF — сигнатура GIF87a / GIF89a if cls._gif_check(data): return cls._gif_parse_info(data, file_size) - - # Fallback на content_type, если байты не распознаны - # Это полезно, если файл обрезан или сигнатура нестандартна - if content_type == "image/webp": - return cls._webp_parse(data, file_size) - if content_type == "image/png": - return cls._png_parse(data) - if content_type == "image/jpeg": - return cls._jpeg_parse(data) - if content_type == "image/gif": - return cls._gif_parse_info(data, file_size) - return None @classmethod - def _parse_video_dimensions( # noqa: C901 + def _parse_video_dimensions( cls, head: bytes, tail: bytes | None, - content_type: str, file_size: int | None, ) -> dict | None: """ - Парсит метаданные видео, определяя формат по сигнатурам, - с fallback на content_type. - - Args: - head: начальные байты файла. - tail: конечные байты файла (опционально, для moov/seekhead). - content_type: MIME-тип из заголовков HTTP. - file_size: размер файла в байтах (опционально). - - Returns: - dict с ключами format, width, height, fps, duration или None. + Видео: MP4, AVI, WebM/MKV, OGV. MP4/OGV: moov/duration часто в tail. """ - - # MP4 / MOV — сигнатура ftyp или moov if cls._mp4_check(head): return cls._mp4_m4a_parse_info(head, tail) - - # AVI — сигнатура RIFF AVI if cls._avi_check(head): return cls._avi_parse_info(head) - - # MKV / WebM — сигнатура EBML if cls._webm_mkv_check(head): - result = cls._webm_mkv_parse_info(head) - # format определяется по содержанию, - # но если есть content_type, берём из него. - if content_type: - if not result: - result = {} - if content_type == "video/webm": - result["format"] = "WEBM" - elif content_type == "video/x-matroska": - result["format"] = "MKV" - return result - - # OGV / OGG — сигнатура OggS + return cls._webm_mkv_parse_info(head) if cls._ogg_ogv_check(head): return cls._ogg_parse_info(head, tail, file_size) - - # Fallback на content_type, если байты не распознаны - # Это полезно, если файл обрезан или сигнатура нестандартна - if content_type in ("video/mp4", "video/quicktime"): - return cls._mp4_m4a_parse_info(head) - if content_type in ("video/x-msvideo", "video/msvideo"): - return cls._avi_parse_info(head) - if content_type == "video/webm": - result = cls._webm_mkv_parse_info(head) - if result: - result["format"] = "WEBM" - return result - if content_type == "video/x-matroska": - result = cls._webm_mkv_parse_info(head) - if result: - result["format"] = "MKV" - return result - if content_type in ("video/ogg", "application/ogg", "video/ogv"): - return cls._ogg_parse_info(head, tail, file_size) - return None @classmethod - def _parse_audio_dimensions( # noqa: C901 + def _parse_audio_dimensions( cls, head: bytes, tail: bytes | None, - content_type: str, file_size: int | None, ) -> dict | None: - """ - Парсит метаданные аудио, определяя формат по сигнатурам, - с fallback на content_type. - - Args: - head: начальные байты файла. - tail: конечные байты файла (опционально, для Vorbis/Opus). - content_type: MIME-тип из заголовков HTTP. - file_size: размер файла в байтах (опционально). - - Returns: - dict с ключами format, duration, sample_rate, bitrate или None. - """ - + """Аудио: MP3, WAV, FLAC, OGG, AAC, M4A, WMA.""" if cls._mp3_check(head): return cls._mp3_parse_info(head, tail, file_size) - - # WAV (RIFF WAVE) if cls._wav_check(head): - return cls._wav_parse_info(head) - - # FLAC (fLaC) + return cls._wav_parse_info(head, file_size) if cls._flac_check(head): return cls._flac_parse_info(head) - - # OGG / OPUS / SPEEX (OggS) if cls._ogg_ogv_check(head): return cls._ogg_parse_info(head, tail, file_size) - - # AAC (ADTS) if cls._aac_check(head): return cls._aac_parse_info(head, file_size) - - # M4A (ftyp) - if cls._m4a_check(head): - return cls._mp4_m4a_parse_info(head) - - # WMA / ASF + if cls._m4a_check(head) or cls._mp4_check(head): + return cls._mp4_m4a_parse_info(head, tail) if cls._wma_check(head): return cls._wma_parse_info(head) - - # Fallback на content_type, если байты не распознаны - # Это полезно, если файл обрезан или сигнатура нестандартна - if content_type in ("audio/mpeg", "audio/mp3"): - return cls._mp3_parse_info(head, tail, file_size) - if content_type == "audio/mp4": - return cls._mp4_m4a_parse_info(head) - if content_type in ("audio/ogg", "application/ogg"): - return cls._ogg_parse_info(head, tail, file_size) - if content_type in ("audio/aac", "audio/x-aac"): - return cls._aac_parse_info(head, file_size) - if content_type in ("audio/wav", "audio/x-wav", "audio/wave"): - return cls._wav_parse_info(head) - if content_type in ("audio/x-ms-wma", "audio/wma"): - return cls._wma_parse_info(head) - return None # ========================================================================= @@ -1483,37 +1294,33 @@ def _webm_mkv_check(data: bytes) -> bool: @classmethod def _png_parse(cls, data: bytes) -> dict[str, Any] | None: + """PNG: format, width, height (IHDR в первых 24 байтах).""" if not cls._png_check(data): - return + return None w, h = struct.unpack(">II", data[16:24]) - return {"width": w, "height": h, "format": "PNG"} + return { + "width": w, + "height": h, + "format": "PNG", + PARSE_STATUS: "ok", + } - @staticmethod + @classmethod def _webp_parse( # noqa: C901 - data: bytes, file_size: int | None = None + cls, + data: bytes, + file_size: int | None = None, ) -> dict[str, Any] | None: """ - Парсит метаданные из WEBP-файла. - - Поддерживает форматы: - - WEBP/VP8X (расширенный, с анимацией/альфа-каналом) - - WEBP/VP8 (стандартный lossy) - - WEBP/VP8L (lossless) - - Args: - data: Начальные байты файла - (рекомендуется ≥ 32 КБ для анимированных) - file_size: Полный размер файла в байтах (опционально, - для апроксимации длительности) + WEBP: format, width, height; для анимации (VP8X/ANMF) — duration, fps. - Returns: - dict с ключами: format, width, height, duration, fps - None, если формат не распознан + Анимация: кадры ANMF в head; для точности нужен head ≈ file_size/25. """ # Базовая проверка заголовка RIFF WEBP if len(data) < 12 or data[0:4] != b"RIFF" or data[8:12] != b"WEBP": return None + head_len = len(data) result: dict[str, Any] = {"format": "WEBP"} # Переменные для накопления данных об анимации @@ -1641,122 +1448,153 @@ def _webp_parse( # noqa: C901 break pos = next_pos - # Если размеры не найдены, файл невалиден для наших целей if "width" not in result or "height" not in result: + result[NEED_HEAD] = -1 + result[PARSE_STATUS] = "partial" return result - # --- Расчет длительности и FPS --- + need_head = 0 + is_animated = frame_count > 0 or result.get("format", "").endswith( + "VP8X" + ) + if is_animated and file_size: + target = min(max(20_240, file_size // 25), MAX_HEAD) + if head_len < target and not (file_size and head_len >= file_size): + need_head = target + if frame_count > 0: result["frames"] = frame_count scanned_duration_sec = total_ms / 1000.0 - - # Экстраполяция, если файл обрезан (data < file_size) - if file_size and file_size > len(data) and len(data) > 0: - ratio = file_size / len(data) - # Предполагаем равномерное распределение данных - estimated_duration_sec = scanned_duration_sec * ratio - result["duration"] = estimated_duration_sec - if estimated_duration_sec > 0: - result["fps"] = round( - (frame_count * ratio) / estimated_duration_sec, 3 - ) - result["error_desc"] = ( - "Длительность и частота кадров определены " - "экстраполированием (приблизительно)" - ) - else: - result["duration"] = scanned_duration_sec + if not need_head: if scanned_duration_sec > 0: result["fps"] = round( frame_count / scanned_duration_sec, 3 ) + if file_size: + target = min(max(20_240, file_size // 25), MAX_HEAD) + if head_len >= target and file_size > target: + ratio = file_size / target + scanned_duration_sec *= ratio + result["parse_note"] = ( + "Длительность и частота кадров определены " + "экстраполированием (приблизительно)" + ) + elif file_size > len(data) and len(data) > 0: + ratio = file_size / len(data) + scanned_duration_sec *= ratio + result["parse_note"] = ( + "Длительность и частота кадров определены " + "экстраполированием (приблизительно)" + ) + result["duration"] = scanned_duration_sec + if need_head: + result[NEED_HEAD] = need_head + ok = ( + not need_head + and result.get("width") + and result.get("height") + and (not is_animated or result.get("duration") is not None) + ) + result[PARSE_STATUS] = "ok" if ok else "partial" return result - @staticmethod - def _gif_parse_info( - data: bytes, file_size: int | None = None - ) -> dict[str, Any]: + @classmethod + def _gif_parse_info( # noqa: C901 + cls, + data: bytes, + file_size: int | None = None, + ) -> dict[str, Any] | None: """ - Извлекает метаданные из GIF по заголовку. + GIF: format, width, height; duration, fps по GCE в head. - Аргументы: - data: Первые байты файла (рекомендуется ≥ 20 КБ). - file_size: Полный размер файла. - - Возвращает: - dict с 'format', 'width', 'height', 'bitrate' или None. + Анимация: для точной duration нужен head ≈ file_size/25. """ if len(data) < 10 or data[:6] not in (b"GIF87a", b"GIF89a"): - return {} + return None - # Логические размеры изображения + head_len = len(data) width, height = struct.unpack(" len(data): break delay_cs = struct.unpack(" 0: - # Длительность по просканированным кадрам - scanned_duration_sec = total_cs / 100.0 - - data_size = len(data) - # Если известен полный размер файла — экстраполируем - if file_size and file_size > data_size: - ratio = file_size / data_size - result["duration"] = scanned_duration_sec * ratio - result["error_desc"] = ( - "Длительность и частота кадров определены " - "экстраполированием (приблизительно)" - ) - else: - # Файл маленький или размер неизвестен — возвращаем как есть - result["duration"] = scanned_duration_sec + need_head = 0 + if file_size and frame_count > 0: + target = min(max(20_240, file_size // 25), MAX_HEAD) + if head_len < target: + need_head = target - # FPS считаем по просканированным кадрам + if frame_count > 0 and not need_head: + scanned_duration_sec = total_cs / 100.0 if scanned_duration_sec > 0: result["fps"] = round(frame_count / scanned_duration_sec, 3) + if file_size: + target = min(max(20_240, file_size // 25), MAX_HEAD) + if head_len >= target and file_size > target: + scanned_duration_sec *= file_size / target + result["parse_note"] = ( + "Длительность и частота кадров определены " + "экстраполированием (приблизительно)" + ) + result["duration"] = scanned_duration_sec + + if file_size and frame_count > 0: + target = min(max(20_240, file_size // 25), MAX_HEAD) + if head_len >= file_size and head_len < target: + need_head = 0 + if not result.get("duration"): + result["duration"] = ( + (total_cs / 100.0) if total_cs else None + ) + if need_head: + result[NEED_HEAD] = need_head + ok = not need_head and ( + frame_count == 0 or result.get("duration") is not None + ) + result[PARSE_STATUS] = "ok" if ok else "partial" return result - @staticmethod - def _jpeg_parse(data: bytes) -> dict | None: + @classmethod + def _jpeg_parse(cls, data: bytes) -> dict | None: + """ + JPEG: format, width, height (маркер SOF в head). + + EXIF может сдвинуть SOF глубже — тогда нужен больший head. + """ if len(data) < 2 or data[:2] != b"\xff\xd8": return None - result = {"format": "JPEG"} pos = 2 while pos < len(data) - 1: if data[pos] != 0xFF: pos += 1 continue marker = data[pos + 1] - if ( - marker in (0xC0, 0xC1, 0xC2) # SOF0, SOF1, SOF2 - and pos + 9 <= len(data) - ): + if marker in (0xC0, 0xC1, 0xC2) and pos + 9 <= len(data): h, w = struct.unpack(">HH", data[pos + 5 : pos + 9]) - return {"width": w, "height": h, "format": "JPEG"} - # Пропускаем сегменты + return { + "width": w, + "height": h, + "format": "JPEG", + PARSE_STATUS: "ok", + } if marker not in (1, *tuple(range(208, 218))): if pos + 4 <= len(data): segment_length = struct.unpack( @@ -1767,21 +1605,24 @@ def _jpeg_parse(data: bytes) -> dict | None: break else: pos += 2 - return result + if len(data) < MAX_HEAD: + return {"format": "JPEG", NEED_HEAD: -1, PARSE_STATUS: "partial"} + return {"format": "JPEG", PARSE_STATUS: "partial"} # ========================================================================= # [ ] Парсеры: видео # ========================================================================= @classmethod - def _mp4_m4a_parse_info( - cls, data: bytes, tail: bytes | None = None + def _mp4_m4a_parse_info( # noqa: C901 + cls, + data: bytes, + tail: bytes | None = None, ) -> dict | None: """ - Парсит размеры и длительность из MP4/MOV файла. + MP4/M4A: format, width, height, duration, sample_rate (moov, mp4a). - Ищет атом moov в head и tail. Для файлов с moov в конце - (потоковая запись) нужен tail. + moov может быть в tail (потоковая запись); mp4a/sample_rate — в moov. Схема данных: ftyp # тип файла (mp42, isom, M4A...) @@ -1803,13 +1644,14 @@ def _mp4_m4a_parse_info( mdat # медиа-данные (видео/аудио) """ result: dict = {} - if cls._mp4_check(data): - result = {"format": "MP4"} - elif cls._m4a_check(data): + if cls._m4a_check(data): result = {"format": "M4A"} + elif cls._mp4_check(data): + result = {"format": "MP4"} else: return None + head_len = len(data) for chunk in (data, tail or b""): if not chunk: continue @@ -1823,7 +1665,47 @@ def _mp4_m4a_parse_info( result["sample_rate"] = sr break - return result or None + if not result: + return None + + has_moov = any(cls._mp4_find_moov(c) for c in (data, tail or b"") if c) + is_audio = result.get("format") == "M4A" or ( + result.get("format") == "MP4" and not result.get("width") + ) + need_head = 0 + need_tail = 0 + if not has_moov: + need_tail = MAX_TAIL + if not result.get("duration") and not has_moov: + need_tail = max(need_tail, MAX_TAIL) + if not result.get("sample_rate") and not any( + cls._mp4_parse_sample_rate(c) for c in (data, tail or b"") if c + ): + need_tail = max(need_tail, MAX_TAIL) + if head_len < MAX_HEAD: + need_head = -1 + + if need_head: + result[NEED_HEAD] = need_head + if need_tail: + result[NEED_TAIL] = need_tail + if is_audio: + ok = bool( + result.get("duration") + and result.get("sample_rate") + and not need_head + and not need_tail + ) + else: + ok = bool( + result.get("duration") + and result.get("width") + and result.get("height") + and not need_head + and not need_tail + ) + result[PARSE_STATUS] = "ok" if ok else "partial" + return result @classmethod def _mp4_find_moov(cls, data: bytes) -> dict | None: @@ -1843,7 +1725,7 @@ def _mp4_find_moov(cls, data: bytes) -> dict | None: return cls._mp4_moov_parse(moov_data) @classmethod - def _mp4_moov_parse(cls, data: bytes) -> dict | None: + def _mp4_moov_parse(cls, data: bytes) -> dict | None: # noqa: C901 result: dict[str, int | float | str] = {} pos = 0 while pos + 8 <= len(data): @@ -1862,7 +1744,6 @@ def _mp4_moov_parse(cls, data: bytes) -> dict | None: duration = cls._m4a_parse_mvhd_duration(data[pos : pos + size]) if duration is not None: result["duration"] = duration - result["format"] = "MP4" elif atom_type == b"trak": trak_data = data[pos + header_size : pos + size] dims = cls._mp4_parse_trak_for_dims(trak_data) @@ -2063,10 +1944,16 @@ def _avi_parse_info(cls, data: bytes) -> dict | None: if result["total_frames"] and result["fps"]: result["duration"] = int(result["total_frames"] / result["fps"]) - # Удаляем служебные ключи result.pop("total_frames", None) result.pop("file_size", None) + head_len = len(data) + ok = bool( + result.get("width") and result.get("height") and result.get("fps") + ) + if not ok and head_len < MAX_HEAD: + result[NEED_HEAD] = -1 + result[PARSE_STATUS] = "ok" if ok else "partial" return result @classmethod @@ -2252,7 +2139,10 @@ def _parse_strf(data: bytes, start: int, end: int, result: dict): @classmethod def _webm_mkv_parse_info(cls, data: bytes) -> dict | None: # noqa: C901 - """Парсит размеры и длительность из MKV/WebM файла.""" + """ + WebM/MKV: format, width, height, duration, fps, + sample_rate (EBML в head). + """ if len(data) < 4: return None @@ -2302,7 +2192,14 @@ def _webm_mkv_parse_info(cls, data: bytes) -> dict | None: # noqa: C901 result["sample_rate"] = round(sample_rate) result["bitrate_nominal"] = bitrate_nominal - return result if len(result) > 1 else None + if len(result) <= 1: + return None + head_len = len(data) + ok = bool(result.get("width") and result.get("height")) + if not ok and head_len < MAX_HEAD: + result[NEED_HEAD] = -1 + result[PARSE_STATUS] = "ok" if ok else "partial" + return result @staticmethod def _webm_read_ebml_size_vint( @@ -2465,19 +2362,14 @@ def _mp3_parse_info( # noqa: C901 file_size: int | None = None, ) -> dict[str, Any] | None: """ - Извлекает метаданные из MP3 по заголовку (начало или конец файла). - - Args: - head: начальные байты файла (рекомендуется ≥32 КБ). - tail: последние байты файла (для поиска Xing/VBRI/ID3v1). - file_size: полный размер файла. - - Returns: - dict с 'format', 'duration', 'sample_rate', 'bitrate' или None. + MP3: format, sample_rate, bitrate из первого фрейма; + duration — Xing/VBRI в head/tail или оценка по размеру. """ if len(head) < 4: return None + head_len = len(head) + tail_len = len(tail or b"") result: dict[str, Any] = {"format": "MP3"} sample_rate: int | None = None bitrate: int | None = None @@ -2514,6 +2406,11 @@ def _mp3_parse_info( # noqa: C901 break if frame_pos is None: + if head_len < MAX_HEAD: + result[NEED_HEAD] = -1 + if not (file_size and head_len >= file_size): + result[NEED_TAIL] = MAX_TAIL + result[PARSE_STATUS] = "partial" return result # Парсим заголовок фрейма (big-endian) @@ -2578,12 +2475,33 @@ def _mp3_parse_info( # noqa: C901 if duration: result["duration"] = duration - return result if len(result) > 1 else None + if len(result) <= 1: + return None + + need_tail = 0 + need_head = 0 + if ( + not duration + and tail_len < 8192 + and not (file_size and head_len >= file_size) + ): + need_tail = MAX_TAIL + if not sample_rate and head_len < MAX_HEAD: + need_head = -1 + + if need_head: + result[NEED_HEAD] = need_head + if need_tail: + result[NEED_TAIL] = need_tail + ok = bool(sample_rate and duration and not need_head and not need_tail) + result[PARSE_STATUS] = "ok" if ok else "partial" + return result @staticmethod def _mp3_find_frame_header(data: bytes, start: int) -> int | None: """Поиск заголовка первого аудио-фрейма после пропуска тегов.""" - for i in range(max(0, start), min(len(data) - 4, start + 65536)): + search_end = min(len(data) - 4, start + MAX_HEAD) + for i in range(max(0, start), search_end): if data[i] == 0xFF and (data[i + 1] & 0xE0) == 0xE0: # Проверка валидности версии и слоя version = (data[i + 1] >> 3) & 0x03 @@ -2719,22 +2637,15 @@ def _ogg_parse_info( # noqa: C901 file_size: int | None = None, ) -> dict | None: """ - Извлекает метаданные из OGG/OGV файла. - - Args: - head: Первые байты файла (заголовок). - tail: Последние байты файла (хвост для поиска гранулы). - content_type: MIME-тип файла (audio/ogg или video/ogg). + OGG/OGV: format, sample_rate, width, height, fps из BOS в head. - Returns: - dict с ключами format, duration, sample_rate, width, height, fps - или None если формат не распознан. - Без tail возвращает format + размеры/fps/sample_rate без duration. + duration — из последней гранулы в tail (или весь файл в head). """ # 1. Базовая валидация сигнатуры Ogg if len(head) < 27 or head[:4] != b"OggS": return None + head_len = len(head) # 2. Парсим заголовки всех потоков (Vorbis/Theora) из BOS-страниц streams = cls._ogg_parse_all_streams(head) if not streams: @@ -2764,17 +2675,17 @@ def _ogg_parse_info( # noqa: C901 if fps_num and fps_den and fps_den > 0: result["fps"] = round(fps_num / fps_den, 3) - # Если нет tail — возвращаем что есть + need_tail = 0 if not tail or len(tail) < 27: - if file_size and len(head) >= file_size: - # Весь файл в head — ищем последнюю гранулу в head - tail = head[-8192:] # Берём конец head как tail + if file_size and head_len >= file_size: + tail = head[-8192:] else: - result["error_desc"] = ( - "Для определения duration необходим " - "конец файла (tail) или файл целиком" - ) - return result + need_tail = MAX_TAIL + + if need_tail and (not tail or len(tail) < 27): + result[NEED_TAIL] = need_tail + result[PARSE_STATUS] = "partial" + return result # 3. Проходим по каждому найденному потоку для извлечения метаданных # С tail — пытаемся получить длительность @@ -2837,7 +2748,27 @@ def _ogg_parse_info( # noqa: C901 ) break - return result if len(result) > 1 else None + if len(result) <= 1: + return None + + if not result.get("duration") and need_tail: + result[NEED_TAIL] = need_tail + result[PARSE_STATUS] = "partial" + return result + + is_audio = result.get("format") == "OGG" + if is_audio: + ok = bool(result.get("duration") and result.get("sample_rate")) + else: + ok = bool( + result.get("duration") + and result.get("width") + and result.get("fps") + ) + if need_tail: + result[NEED_TAIL] = need_tail + result[PARSE_STATUS] = "ok" if ok else "partial" + return result @staticmethod def _ogg_parse_all_streams(data: bytes) -> list: # noqa: C901 @@ -2957,7 +2888,7 @@ def _ogg_parse_all_streams(data: bytes) -> list: # noqa: C901 @staticmethod def _ogg_extract_last_granule( - tail: bytes, + tail: bytes | None, expected_serial: int | None = None, stream_type: Literal["vorbis", "theora"] | None = None, ) -> int | None: @@ -2972,6 +2903,8 @@ def _ogg_extract_last_granule( Возвращает: Значение гранулы или None, если не найдено. """ + if not tail or len(tail) < 27: + return None pos = tail.rfind(b"OggS") while pos >= 0: if pos + 27 <= len(tail): @@ -3180,6 +3113,8 @@ def _aac_parse_info( # noqa: C901 result["duration"] = duration if total_samples == 0: + result[NEED_HEAD] = -1 + result[PARSE_STATUS] = "partial" return result # 📈 Экстраполяция по полному размеру файла @@ -3195,6 +3130,8 @@ def _aac_parse_info( # noqa: C901 if estimated_duration > duration * 1.3: result["duration"] = estimated_duration + ok = bool(result.get("sample_rate") and result.get("duration")) + result[PARSE_STATUS] = "ok" if ok else "partial" return result @staticmethod @@ -3254,9 +3191,11 @@ def _wav_parse_info( # noqa: C901 if sample_rate and sample_rate > 0: result["sample_rate"] = sample_rate if not byte_rate or byte_rate == 0: + result[PARSE_STATUS] = ( + "ok" if result.get("sample_rate") else "partial" + ) return result - # Определяем размер аудиоданных audio_bytes = None if data_size and data_size != 0xFFFFFFFF: audio_bytes = data_size @@ -3268,6 +3207,8 @@ def _wav_parse_info( # noqa: C901 if audio_bytes and audio_bytes > 0: result["duration"] = audio_bytes / byte_rate + ok = bool(result.get("sample_rate") and result.get("duration")) + result[PARSE_STATUS] = "ok" if ok else "partial" return result @staticmethod @@ -3394,15 +3335,17 @@ def _wma_parse_info(data: bytes) -> dict[str, Any] | None: # noqa: C901 result["sample_rate"] = sample_rate result["bitrate"] = bitrate # Всегда добавляем, даже если None - return ( - result - if ( - "duration" in result - or "sample_rate" in result - or bitrate is not None - ) - else None - ) + if not ( + "duration" in result + or "sample_rate" in result + or bitrate is not None + ): + return None + ok = bool(result.get("duration") and result.get("sample_rate")) + if not result.get("duration"): + result[NEED_HEAD] = -1 + result[PARSE_STATUS] = "ok" if ok else "partial" + return result @staticmethod def _flac_parse_info(head: bytes) -> dict | None: @@ -3493,6 +3436,7 @@ def _flac_parse_info(head: bytes) -> dict | None: "channels": channels, "duration": duration, "format": "FLAC", + PARSE_STATUS: "ok", } # Переходим к следующему блоку diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py index 15942c5f..32ab9594 100644 --- a/tests/test_utils/fileinfo/prepare_fixtures.py +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -179,7 +179,7 @@ async def get_expected(name, cfg, session) -> tuple[bytes, bytes]: inspector = FileInspector() if "file" in cfg: finfo = await inspector.inspect_file( - cfg["file"], full_read_limit=0 + cfg["file"], full_read_threshold=0 ) elif "url" in cfg: finfo = await inspector.inspect_url(cfg["url"], session=session) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index b94e435c..354d245e 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -286,7 +286,7 @@ async def test_generic_exception_returns_error_status(self): max_retries=1, ) assert info.status == "error" - assert info.error_desc == "unexpected" + assert info.parse_note == "unexpected" async def test_retry_then_success(self): """Проверка retry/call_count""" @@ -367,7 +367,7 @@ async def test_expected_fields_match(self, name, all_fixtures, tmp_path): inspector = FileInspector() info = await inspector.inspect_file( str(file_path), - full_read_limit=0, # Отключаем полное чтение файла + full_read_threshold=0, # Отключаем полное чтение файла ) if exp.get("format"): assert info.format == exp["format"] @@ -390,7 +390,7 @@ async def test_inspect_local_nonexistent(self): inspector = FileInspector() info = await inspector.inspect_file("/nonexistent/file.jpg") assert info.status == "error" - assert info.error_desc == "Файл не найден" + assert info.parse_note == "Файл не найден" async def test_inspect_local_permission_denied(self, tmp_path: Path): """Ошибка чтения файла → error.""" @@ -404,7 +404,7 @@ async def test_inspect_local_permission_denied(self, tmp_path: Path): inspector = FileInspector() info = await inspector.inspect_file(str(file_path)) assert info.status == "error" - assert "read error" in info.error_desc + assert "read error" in info.parse_note class TestFileInspectorBytes: @@ -435,7 +435,7 @@ async def test_expected_fields_match(self, name, all_fixtures): nbio = _make_fixture_named_bytes_io(name, head, tail, exp) info = await FileInspector().inspect_bytes( nbio, - full_read_limit=0, # Отключаем полное чтение данных + full_read_threshold=0, # Отключаем полное чтение данных ) if exp.get("format"): assert info.format == exp["format"] @@ -470,7 +470,6 @@ async def test_small_head_partial(self): ) assert info.format == "WEBP" assert info.status == "partial" - assert info.error_desc.startswith("Недостаточно данных") async def test_html_page_error(self): """HTML-страница — error.""" @@ -481,18 +480,21 @@ async def test_html_page_error(self): "https://example.com/page.html", session=session ) assert info.status == "error" - assert info.error_desc == "Файл не является медиа (HTML-страница)" + assert info.parse_note == "Файл не является медиа (HTML-страница)" - async def test_wrong_random_data_page_error(self): - """Случайное содержание по ссылке — error.""" + async def test_wrong_random_data_partial(self): + """ + Случайное содержание по ссылке — partial (только заголовки сервера). + """ data = b"8" session = _make_mock_session(data, b"", "", len(data)) inspector = FileInspector() info = await inspector.inspect_url( "https://example.com/page.html", session=session ) - assert info.status == "error" - assert info.error_desc.startswith("Недостаточно данных") + assert info.status == "partial" + assert info.file_size == len(data) + assert info.format is None async def test_empty_body_error(self): """Пустой ответ — error.""" @@ -502,7 +504,7 @@ async def test_empty_body_error(self): "https://example.com/empty", session=session ) assert info.status == "error" - assert info.error_desc.startswith("Недостаточно данных") + assert info.parse_note.startswith("Недостаточно данных") # ============================================================================= From ee82d03c85c4d83c326460f0b80fe4188b9c6953 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 18:27:17 +0300 Subject: [PATCH 14/31] =?UTF-8?q?fix:=20=D0=9E=D0=BF=D0=B5=D1=87=D0=B0?= =?UTF-8?q?=D1=82=D0=BA=D0=B0=20=D0=B2=20=D0=BA=D0=BE=D0=BC=D0=BC=D0=B5?= =?UTF-8?q?=D0=BD=D1=82=D0=B0=D1=80=D0=B8=D0=B8:=20=C2=AB=D0=9F=D1=80?= =?UTF-8?q?=D0=BE=D0=B2=D0=B5=D0=B8=D1=80=D1=82=D1=8C=C2=BB=20=E2=86=92=20?= =?UTF-8?q?=C2=AB=D0=9F=D1=80=D0=BE=D0=B2=D0=B5=D1=80=D0=B8=D1=82=D1=8C?= =?UTF-8?q?=C2=BB.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --- tests/test_utils/fileinfo/prepare_fixtures.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_utils/fileinfo/prepare_fixtures.py b/tests/test_utils/fileinfo/prepare_fixtures.py index 32ab9594..26de3c2b 100644 --- a/tests/test_utils/fileinfo/prepare_fixtures.py +++ b/tests/test_utils/fileinfo/prepare_fixtures.py @@ -58,7 +58,7 @@ SAMPLE_URLS = { - # Провеирть ключи. Совпадающие заменят данные, новые добавят. + # Проверить ключи. Совпадающие заменят данные, новые добавят. # -- # Без expected — работает только в автоматическом режиме. # Формат, размеры и т.д. будут получены из FileInspector. From aec25b28b5973a06bf38481931663aedeac7023c Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 18:31:18 +0300 Subject: [PATCH 15/31] Fix typo in file_inspector.py documentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit fix: Опечатка в докстринге: «чтоюы» → «чтобы». --- maxapi/utils/file_inspector.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 0b4adf85..e7769eb4 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -877,7 +877,7 @@ async def inspect_file( Args: path: Путь к файлу. full_read_threshold: Файлы меньше этого размера читаются целиком. - Установите в ноль, чтоюы отключить полное чтение. + Установите в ноль, чтобы отключить полное чтение. В таком случае будет использован план загрузки как для inspect_url From 8e30263af85a7245aa6c3724e78ad56609be77aa Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 18:32:06 +0300 Subject: [PATCH 16/31] =?UTF-8?q?fix:=20=D0=9E=D0=BF=D0=B5=D1=87=D0=B0?= =?UTF-8?q?=D1=82=D0=BA=D0=B0=20=D0=B2=20=D1=81=D0=BE=D0=BE=D0=B1=D1=89?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B8=20=D0=B8=D1=81=D0=BA=D0=BB=D1=8E=D1=87?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D1=8F:=20=C2=AB=D0=BE=D1=82=D1=81=D1=83?= =?UTF-8?q?=D1=82=D0=B2=D1=83=D0=B5=D1=82=C2=BB=20=E2=86=92=20=C2=AB=D0=BE?= =?UTF-8?q?=D1=82=D1=81=D1=83=D1=82=D1=81=D1=82=D0=B2=D1=83=D0=B5=D1=82?= =?UTF-8?q?=C2=BB.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- maxapi/utils/file_inspector.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index e7769eb4..39996a9f 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -600,7 +600,7 @@ async def _fetch_chunk( response = self._response if not response: raise RuntimeError( - "Response отсутвует. Сначала нужно запросить _fetch_meta()" + "Response отсутствует. Сначала нужно запросить _fetch_meta()" ) data = await self._read_response(response, size) From 8eedc5d33d8c4f0180a5dd8e631660d54b1e1822 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 18:35:04 +0300 Subject: [PATCH 17/31] =?UTF-8?q?fix:=20=D0=9D=D0=B5=D0=B2=D0=B5=D1=80?= =?UTF-8?q?=D0=BD=D0=BE=D0=B5=20=D0=BF=D0=BE=D0=BB=D0=BE=D0=B6=D0=B5=D0=BD?= =?UTF-8?q?=D0=B8=D0=B5=20=D1=81=D0=BA=D0=BE=D0=B1=D0=BE=D0=BA=20=D0=B2=20?= =?UTF-8?q?=D1=80=D0=B0=D1=81=D1=87=D1=91=D1=82=D0=B5=20file=5Fsize?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tests/test_utils/fileinfo/test_file_info.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 354d245e..47e43772 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -65,7 +65,7 @@ def _make_fixture_named_bytes_io(name, head, tail, exp) -> NamedBytesIO: ext = mimetypes.guess_extension(mime) or ".bin" file_name = f"{name}{ext}" - file_size = exp.get("file_size") or (len(head) + len(tail) if tail else 0) + file_size = exp.get("file_size") or len(head) + (len(tail) if tail else 0) full = bytearray(file_size) full[: len(head)] = head From ebff36e7a5cd6c807d7488ee1ab4c3eced3ad761 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 19:29:11 +0300 Subject: [PATCH 18/31] =?UTF-8?q?fix:=20=D0=9F=D1=80=D0=BE=D0=B2=D0=B5?= =?UTF-8?q?=D1=80=D0=BA=D0=B0=20=D0=BD=D0=B0=20=D1=83=D1=82=D0=B5=D1=87?= =?UTF-8?q?=D0=BA=D1=83=20=D0=B0=D0=B2=D1=82=D0=BE=D1=80=D0=B8=D0=B7=D0=B0?= =?UTF-8?q?=D1=86=D0=B8=D0=B8=20=D1=81=D0=BC=D0=BE=D1=82=D1=80=D0=B5=D1=82?= =?UTF-8?q?=D1=8C=20=D1=82=D0=B0=D0=BA=20=D0=B6=D0=B5=20=D0=B2=20self.sess?= =?UTF-8?q?ion?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit + Проверка на куки конкретного URL --- maxapi/utils/file_inspector.py | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 39996a9f..d53a0685 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -422,6 +422,30 @@ def _is_trusted_url(self) -> bool: for domain in _TRUSTED_DOMAINS ) + @property + def _has_sensitive_auth(self) -> bool: + """Проверяет наличие чувствительных данных авторизации.""" + if not self.session: + # Проверяем только собственные headers + return any(h in self.headers for h in ("Authorization", "Cookie")) + + headers = self.session.headers + if headers and any(h in headers for h in ("Authorization", "Cookie")): + return True + + # Куки, которые будут отправлены на этот URL + cookie_jar = self.session.cookie_jar + if cookie_jar and self.original_url: + try: + target_url = URL(self.original_url) + cookies_to_send = cookie_jar.filter_cookies(target_url) + return bool(cookies_to_send) + except Exception: + # Если не смогли проверить точно — перестраховываемся + return len(cookie_jar) > 0 + + return False + # ======================================================================== # Async Context Manager # ======================================================================== @@ -505,7 +529,7 @@ async def close(self): async def _fetch_meta(self): """Получает метаинформацию с retry.""" if ( - ("Authorization" in self.headers or "Cookie" in self.headers) + self._has_sensitive_auth and not self._is_trusted_url and not self._allow_external_auth ): @@ -537,6 +561,7 @@ async def _fetch_meta(self): ), history=(), ) + self._response = await self._request_with_retry(self.original_url) final_url = str(self._response.url) http_headers = self._response.headers @@ -600,7 +625,8 @@ async def _fetch_chunk( response = self._response if not response: raise RuntimeError( - "Response отсутствует. Сначала нужно запросить _fetch_meta()" + "Response отсутствует. " + "Сначала нужно запросить _fetch_meta()" ) data = await self._read_response(response, size) From 53761c27b61408da4880c73ded4f3fbabb7f6426 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 19:45:09 +0300 Subject: [PATCH 19/31] =?UTF-8?q?fix:=20max=5Ftotal,=20=D0=BE=D0=B3=D1=80?= =?UTF-8?q?=D0=B0=D0=BD=D0=B8=D1=87=D0=B8=D0=B2=D0=B0=D0=BB=20=D1=82=D0=BE?= =?UTF-8?q?=D0=BB=D1=8C=D0=BA=D0=BE=20=D0=B2=D0=BD=D1=83=D1=82=D1=80=D0=B8?= =?UTF-8?q?=20=D0=BE=D0=B4=D0=BD=D0=BE=D0=B3=D0=BE=20=D0=B2=D1=8B=D0=B7?= =?UTF-8?q?=D0=BE=D0=B2=D0=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Теперь подсчитывает всё скачанное. --- maxapi/utils/file_inspector.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index d53a0685..09d799e7 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -379,7 +379,8 @@ def __init__( ) self.original_url = url self.max_total = max_total - + self._downloaded: int = 0 + # Параметры соединения self.timeout = ClientTimeout(total=timeout, sock_connect=sock_connect) self.max_retries = max_retries @@ -639,13 +640,17 @@ async def _fetch_chunk( async def _read_response( self, response: ClientResponse, size: int ) -> bytes: - actual = min(size, self.max_total) + remaining = self.max_total - self._downloaded + if remaining < 0: + return b"" + to_read = min(size, remaining) data = b"" - while len(data) < actual: - chunk = await response.content.read(actual - len(data)) + while len(data) < to_read: + chunk = await response.content.read(to_read - len(data)) if not chunk: break data += chunk + self._downloaded += len(data) return data async def _expand_head(self, *, target: int | None = None) -> bytes: From 59172b238b45316ffd9ca4de3b98019981a81e81 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:05:25 +0300 Subject: [PATCH 20/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 47e43772..442ef5c8 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -12,13 +12,15 @@ import aiohttp import pytest -from maxapi.connection.base import NamedBytesIO -from maxapi.types.file_info import FileInfo -from maxapi.utils.file_inspector import ( +from aiohttp import CIMultiDict, CookieJar +from yarl import URL + +from ..connection.base import NamedBytesIO +from ..types.file_info import FileInfo +from ..utils.file_inspector import ( FileInspector, RangeDownloader, ) -from yarl import URL log = logging.getLogger("maxapi.fileinfo") log.setLevel(logging.DEBUG) @@ -187,6 +189,8 @@ def _make_mock_session( else factory.make_head_response(url) ) ) + session.headers = CIMultiDict() + session.cookie_jar = CookieJar() return session From 6819c2c4df2e60b8f00741d1ccf374ce0631ac24 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:08:26 +0300 Subject: [PATCH 21/31] fix: ruff --- maxapi/utils/file_inspector.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 09d799e7..93d9ec5c 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -380,7 +380,7 @@ def __init__( self.original_url = url self.max_total = max_total self._downloaded: int = 0 - + # Параметры соединения self.timeout = ClientTimeout(total=timeout, sock_connect=sock_connect) self.max_retries = max_retries @@ -446,7 +446,7 @@ def _has_sensitive_auth(self) -> bool: return len(cookie_jar) > 0 return False - + # ======================================================================== # Async Context Manager # ======================================================================== @@ -642,7 +642,7 @@ async def _read_response( ) -> bytes: remaining = self.max_total - self._downloaded if remaining < 0: - return b"" + return b"" to_read = min(size, remaining) data = b"" while len(data) < to_read: From 7f580f83edc60cefd46426ea03541bc3ea967f03 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:11:28 +0300 Subject: [PATCH 22/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 442ef5c8..2b4ce7a4 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -12,7 +12,7 @@ import aiohttp import pytest -from aiohttp import CIMultiDict, CookieJar +from multidict import CIMultiDict, CookieJar from yarl import URL from ..connection.base import NamedBytesIO From cc07b140a7248ee31d67a56eef8fff712575c95b Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:12:59 +0300 Subject: [PATCH 23/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 2b4ce7a4..59fd174e 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -12,7 +12,8 @@ import aiohttp import pytest -from multidict import CIMultiDict, CookieJar +from aiohttp import CookieJar +from multidict import CIMultiDict from yarl import URL from ..connection.base import NamedBytesIO From dfa714f09e1402edb43b86164a9d1097081c5036 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:15:23 +0300 Subject: [PATCH 24/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 59fd174e..3ebe410d 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -13,15 +13,14 @@ import aiohttp import pytest from aiohttp import CookieJar -from multidict import CIMultiDict -from yarl import URL - -from ..connection.base import NamedBytesIO -from ..types.file_info import FileInfo +from maxapi.connection.base import NamedBytesIO +from maxapi.types.file_info import FileInfo from ..utils.file_inspector import ( FileInspector, RangeDownloader, ) +from multidict import CIMultiDict +from yarl import URL log = logging.getLogger("maxapi.fileinfo") log.setLevel(logging.DEBUG) From 28d35829354e7bf3599f66bc62bca602e2ed68dc Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:17:20 +0300 Subject: [PATCH 25/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 3ebe410d..3478e48f 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -15,7 +15,7 @@ from aiohttp import CookieJar from maxapi.connection.base import NamedBytesIO from maxapi.types.file_info import FileInfo -from ..utils.file_inspector import ( +from maxapi.utils.file_inspector import ( FileInspector, RangeDownloader, ) From 791a0ddd40f9cf506d2e163ee8348332bdfb2dfb Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Mon, 25 May 2026 20:23:21 +0300 Subject: [PATCH 26/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 3478e48f..0b531ffb 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -288,6 +288,7 @@ async def test_generic_exception_returns_error_status(self): "https://example.com/test.jpg", session=session, max_retries=1, + allow_external_auth=True, ) assert info.status == "error" assert info.parse_note == "unexpected" @@ -306,7 +307,10 @@ async def test_retry_then_success(self): session.get = AsyncMock(side_effect=[bad, bad, good]) info = await FileInspector().inspect_url( - "https://x.com/x.jpg", session=session, retry_backoff_factor=0 + "https://x.com/x.jpg", + session=session, + retry_backoff_factor=0, + allow_external_auth=True, ) assert info.status == "partial" assert info.format == "JPEG" @@ -327,6 +331,7 @@ async def test_retry_exhausted(self): session=session, max_retries=1, retry_backoff_factor=0, + allow_external_auth=True, ) assert info.status == "error" assert session.get.call_count == 2 @@ -346,6 +351,7 @@ async def test_client_error_no_retry(self): session=session, max_retries=2, retry_backoff_factor=0, + allow_external_auth=True, ) assert info.status == "error" assert session.get.call_count == 1 From e299fb2cac2d7411c8330b46363729f9a17b0311 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Sat, 13 Jun 2026 01:08:09 +0300 Subject: [PATCH 27/31] =?UTF-8?q?fix:=20=D0=9E=D0=BF=D1=82=D0=B8=D0=BC?= =?UTF-8?q?=D0=B8=D0=B7=D0=B0=D1=86=D0=B8=D1=8F=20=5Fwebp=5Fparse=20=D0=91?= =?UTF-8?q?=D0=BE=D0=BB=D0=B5=D0=B5=20=D1=82=D0=BE=D1=87=D0=BD=D0=BE=D0=B5?= =?UTF-8?q?=20=D0=BE=D0=BF=D1=80=D0=B5=D0=B4=D0=B5=D0=BB=D0=B5=D0=BD=D0=B8?= =?UTF-8?q?=D0=B5=20=D0=BD=D0=B0=D0=BB=D0=B8=D1=87=D0=B8=D1=8F=20=D0=B0?= =?UTF-8?q?=D0=BD=D0=B8=D0=BC=D0=B0=D1=86=D0=B8=D0=B8=20fix:=20fixtures.js?= =?UTF-8?q?on=20(error=5Fdesc=20->=20parse=5Fnote)=20add:=20=D1=84=D0=BE?= =?UTF-8?q?=D1=80=D0=BC=D0=B0=D1=82=20webp=5Floseless=20=D0=B2=20fixtures.?= =?UTF-8?q?json?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- maxapi/utils/file_inspector.py | 14 +++++++------ tests/test_utils/fileinfo/fixtures.json | 26 ++++++++++++++++++++++--- 2 files changed, 31 insertions(+), 9 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index 93d9ec5c..c4affb05 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -1374,6 +1374,9 @@ def _webp_parse( # noqa: C901 # 1. VP8X: Расширенный формат (флаги, размеры canvas) if chunk_type == b"VP8X" and len(data) >= payload_start + 10: + # Флаги VP8X: бит 4 = анимация + vp8x_flags = data[payload_start] + result["_vp8x_flags"] = vp8x_flags # Размеры в VP8X хранятся как (value - 1) width = ( int.from_bytes( @@ -1452,11 +1455,11 @@ def _webp_parse( # noqa: C901 # 4. VP8L: Lossless изображение elif ( - chunk_type == b"VP8L" and is_chunk_complete and chunk_size >= 5 + chunk_type == b"VP8L" and chunk_size >= 5 ): - if payload_start + 4 <= len(data): + if payload_start + 5 <= len(data): bits = struct.unpack( - "> 14) & 0x3FFF) + 1 @@ -1485,9 +1488,8 @@ def _webp_parse( # noqa: C901 return result need_head = 0 - is_animated = frame_count > 0 or result.get("format", "").endswith( - "VP8X" - ) + vp8x_flags = result.pop("_vp8x_flags", 0) + is_animated = frame_count > 0 or bool(vp8x_flags & 0x10) if is_animated and file_size: target = min(max(20_240, file_size // 25), MAX_HEAD) if head_len < target and not (file_size and head_len >= file_size): diff --git a/tests/test_utils/fileinfo/fixtures.json b/tests/test_utils/fileinfo/fixtures.json index 76ceb527..e468298e 100644 --- a/tests/test_utils/fileinfo/fixtures.json +++ b/tests/test_utils/fileinfo/fixtures.json @@ -45,7 +45,7 @@ "duration": 4.5, "fps": 11.111, "bitrate_avg": 1739, - "error_desc": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", + "parse_note": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", "format": "GIF" }, "webp_static": { @@ -88,7 +88,7 @@ "duration": 8.7, "fps": 30.043, "bitrate_avg": 8584, - "error_desc": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", + "parse_note": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", "format": "WEBP/VP8X" }, "webp_animated_2": { @@ -101,7 +101,7 @@ "duration": 9.0, "fps": 25.0, "bitrate_avg": 10000, - "error_desc": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", + "parse_note": "Длительность и частота кадров определены экстраполирвоанием (приблизительно)", "format": "WEBP/VP8X" }, "webp_animated_4": { @@ -113,6 +113,26 @@ "height": 518, "format": "WEBP/VP8X" }, + "webp_loseless": { + "head_b64": "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", + "url": "https://convertico.com/samples/download.php?format=webp&file=abstract-whale-webp.webp", + "file_size": 2067484, + "mime_type": "image/webp", + "width": 1535, + "height": 1024, + "status": "ok", + "format": "WEBP/VP8L" + }, + "webp_loseless2": { + "head_b64": "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", + "url": "https://habrastorage.org/r/w1560/getpro/habr//post_images/820/00f/beb/82000fbebf8c287ab9a37bbc6e96976b.webp", + "mime_type": "image/webp", + "file_size": 80128, + "width": 514, + "height": 513, + "status": "ok", + "format": "WEBP/VP8L" + }, "avi_15s": { "head_b64": "UklGRhx4DgBBVkkgTElTVMoiAABoZHJsYXZpaDgAAABAnAAAKKAAAAAAAAAQCQAAeAEAAAAAAAACAAAAAAAQAIACAADgAQAAAAAAAAAAAAAAAAAAAAAAAExJU1TgEAAAc3RybHN0cmg4AAAAdmlkc0ZNUDQAAAAAAAAAAAAAAAABAAAAGQAAAAAAAAB4AQAArTwAAP////8AAAAAAAAAAIAC4AFzdHJmKAAAACgAAACAAgAA4AEAAAEAGABGTVA0ABAOAAAAAAAAAAAAAAAAAAAAAABKVU5LGBAAAAQAAAAAAAAAMDBkYwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB2cHJwRAAAAAAAAAAAAAAAGQAAAIACAADgAQAAAwAEAIACAADgAQAAAQAAAOABAACAAgAA4AEAAIACAAAAAAAAAAAAAAAAAAAAAAAATElTVIoQAABzdHJsc3RyaDgAAABhdWRzAQAAAAAAAAAAAAAAAAAAACAAAADJBAAAAAAAAEACAACiAQAA/////wAAAAAAAAAAAAAAAHN0cmYeAAAAVQABAESsAACAPgAAgAQAAAwAAQACAAAAgAQBAHEFSlVOSxgQAAAEAAAAAAAAADAxd2IAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAASlVOSwQBAABvZG1sZG1saPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAExJU1QaAAAASU5GT0lTRlQOAAAATGF2ZjYwLjE2LjEwMABKVU5L+AMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATElTVJQVDgBtb3ZpMDF3YqEBAAD/+5DEAAAUqGckFbeACyC345s9QAA5GROLgzdV0DN5kjAclcHobB6FsccrGhjJiY6ZKUmTkZjoyYmHmFgoGA0UwwBCAjgOQJAIYPQXBQKNPnOaZpoeo2ePR4wKxWMjymvm79/cfvgAZmI/+AZ/+HgDvgAYe+OHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4eHgAAAAAGHh4ePAAAAAEYeHh7wAAAP3mPAIAQAQQQgMY8tIxxfhjAiB8MbXRowMgNDGtiuN94z0wdwwjpuGKGQUT0QEmHQbzErCyMKcLAODCe8HBXQ+BoqIHMhmR/A4U0DDoANCeRRbgZ9MAQNAxw0DGE0lOvwDRQGNGAY0kAsDAwgP/wbCh0QXDBYUJtDFP/4WEh+wYqC4YUiGrQyL//jHCCwgsOSKBFAjqFzC5v//yGi5RzSaHOHOKJFSKmRFv///IsUSKk6ZEWNll08o2To/////q16S0TFJIyRRMUki8owBwCDMEaBnTBaAFswIkFqMTnLJDUPyYI+gIahMkwAMDBkY0s6AAAAAAGwAQAAAbWJEwAAAQAAAAEgAMSNiADNFAQ8FEMAAAGyTGF2YzYwLjMxLjEwMgAAAbMAEAcAAAG2EGCxgpm38bbfxtt/G238bbfxh0bYLoHHG238bbfxtt+xW2MxJCCwPJvs59LycaX5ilFrdQSIyzpbe4izS1xjbPlXr9vZmQHLQ3RUJCQIw9CCX+HglJvp0/xEyDadkHLeommM7FLe8wwzBxzN/eZO0HBOLmB5mLtlemhEBYjNZAeEvmsanMgzQ0poOPQDgAaDCWXj3qoSlI+LgIy8Sswl2AWwNvLhzpACAClBgQR6JKlMIRaJQ87YOZeD9PA32Aw2ygQ9VIc/IIybbBwB8jbb+Ntv422/jCW2wYQJyNtv422/jbb+Ntv4wMO2wE4Bgjbb+Ntv422/jbb+MJbbBhAm422/jbb+Ntv2gqwX/9yUr8L5CSgPNch4W+bxudyjJDCmA48aHWbvr3Lyg4JgagDQYfD4R1KtIoHg/q6nIDLM1CIPoi2gxvy4cwyDqClBgOCUB9SnHygShLq93MBipXUIftYi3QYOfFYcwWsQODE4kgGNpOtplLKaZ3e8HLXEannFOCKpzCqdLZJikyOAdVaptN8sZxRRdSXKhcCWEID4lAGl2D0Rk+J2KjQNsaDGlbQ3YbgcaU53OPjbbOC6Ntv422/jbb+MOjbBdA5I22/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv2lu22/Me1tvZnsnd2g5Haiki9q0OhuCGB/R6BhhT5It2Ubt6Cp5bFGUbZ2rh7V1EFg4Px2wPs7q/6HTUR6A7CUYdeC2DNAyQRwZlKWJoIA+9vC8S/51hUhUYvBBV2DZjKhm/vvHWQMYhghh2mHhRUsoI8YDKM9NlkDoLwx23bd2223ttXttR21DXjZg3pS9kv+pboaYImQ1IGKN4C7AhApBCBoXAiCQxy3gfp9XTpf8kut/JedjWcawRMdGE1tg0gM7G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G6bBVBKYYTB6JIIWM9ZHyhr6gGG0kjTdgcIo1vCRRt50sK/vYiBIVpmEvixnOxBRFym8qAFyA0YHADRHBCL4PxGV1j6L/huwr8DGlbSL+A7cR+3NOtQabLlubLLLyyrWWI5YgruEkRxG8JfkGJ8gGGwcBT5v1GPqV4IuGQ4JUjZc3WGZzYCNuINyocoLC82WOBuHolNjygRYo2bDDTWhSAvAMmBhLEgGZY5nEgj+lqsSN3uqM4CtVbyCCwvF+KfVbYi0zG2ux8FONtv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/a3bbfmPa23sz2Tu7QcjtRSRe1aHbbb8x7W29meyd3aDkdqKSL2rQ6HMRxH+Jf0OJsoGGgcBX5v9GHoVYIumR4djvw88HLOh14HFemsgvRQVAqgw+Bh8Xgysv5FKoRlWwG6PeIkIfKurfT/gyage/ETBawg5iOI/xL+hxNlAw0DgK/N/ow9CrBF0yMRiwcm2/s/rbdU7qOg5BIgcAtwlCUXBCLmB0OmMVJFWc96C/dqP//0r//V9q7422zBONtv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbb+Ntv422/jbbIDRhi8JQ7HTQ7aLPrbALNiL7QxyiLgikmzNcygJv/Y3l9+Kc1ax4DKFxenHSdgvLmdVJ1Wz7DPrzc2BnKi3L3Ny3NllrmoOqVI2XN1hmc2AjbiDcqHKCwvNljlwlDsdNDtos+tsAs2IvtDHKIuCKSbM1wdUqRsubrDM5sBG3EG5UOUFhebLHA3iSJGj7ewrVaNtDgNtJTVEUUgLkDCQDCSmBm0wgMqFQH1WAxWmRYzkWBFSG/UCvl9VbeXvrmvjbXYZAmRtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfs7ttvzHtbb2Z7J3doOR2opIvatDrDcB8IYN3RAaBgLe3svCtvV4Wm8zlqjEa/fr8wWDg7H+D5hTV/1Zrwc0HDaIRgjeC2DJAZpKDMiTE3lPh8wo+JaWcBlmL3mZ1aq2VG4xi6Lf2KPGWEHMRxH+Jf0OJsoGGgcBX5v9GHoVYIumRiMWDk239n9bbqndR0HIJEDgFuEoSi4IRcwOh0xipIqznvQX7tR//+lf/6vtXfG22YJxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G238bbfxtt/G22h7DKBKHY6aHbRZ9bYBZsRfaGOURcEUk2ZrmUBQ/7G8vvxTmrWPAY0uL046TsF5czqpOq2fYZ9ebmwM5UW5e5uW5sstc1B1SpGy5usMzmwEbcQblQ5QWF5sscuE4QxIwS2pOqGWu1jREX53Ngcby", "url": "https://samplefile.com/samples/download/video/avi/avi_15s_sample_file_927KB.avi/", From 1af49e742c4093da4536607ea3ec2515b4ffda45 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Sat, 13 Jun 2026 01:09:29 +0300 Subject: [PATCH 28/31] add: tests to test_file_info.py --- tests/test_utils/fileinfo/test_file_info.py | 1495 +++++++++++++++++++ 1 file changed, 1495 insertions(+) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index 0b531ffb..aca39b18 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -6,6 +6,7 @@ import json import logging import mimetypes +import struct from io import BytesIO from pathlib import Path from unittest.mock import AsyncMock, Mock, patch @@ -523,11 +524,13 @@ async def test_empty_body_error(self): class TestRangeDownloader: # Тесты извлечения имени файла def test_from_content_disposition(self): + """Извлечение имени файла из Content-Disposition.""" headers = {"Content-Disposition": 'attachment; filename="photo.jpg"'} name = RangeDownloader._extract_filename(headers, "https://x.com/123") assert name == "photo.jpg" def test_from_url_when_no_disposition(self): + """Без Content-Disposition: имя из URL.""" headers = {} name = RangeDownloader._extract_filename( headers, "https://x.com/path/photo.jpg" @@ -535,6 +538,7 @@ def test_from_url_when_no_disposition(self): assert name == "photo.jpg" def test_url_with_query_params(self): + """URL с query-параметрами, имя из пути.""" headers = {} name = RangeDownloader._extract_filename( headers, "https://x.com/photo.jpg?size=large" @@ -542,6 +546,7 @@ def test_url_with_query_params(self): assert name == "photo.jpg" def test_filename_with_percent_encoding(self): + """Имя файла с percent-encoding декодируется.""" headers = {} name = RangeDownloader._extract_filename( headers, "https://x.com/%D1%84%D0%B0%D0%B9%D0%BB.jpg" @@ -549,11 +554,13 @@ def test_filename_with_percent_encoding(self): assert name == "файл.jpg" def test_content_disposition_without_quotes(self): + """Content-Disposition без кавычек.""" headers = {"Content-Disposition": "attachment; filename=photo.jpg"} name = RangeDownloader._extract_filename(headers, "https://x.com/123") assert name == "photo.jpg" def test_url_without_filename(self): + """URL без имени файла = неизвестно.""" headers = {} name = RangeDownloader._extract_filename(headers, "https://x.com/") assert name == "unknown" # или что возвращается по умолчанию @@ -599,3 +606,1491 @@ async def test_timeout_passed_to_inspector(self, mock_inspect, bot): await bot.get_file_info("https://example.com/file.mp4", timeout=15) assert mock_inspect.call_args.kwargs["timeout"] == 15 + + +class TestFileInspectorAdvanced: + """FileInspector — дополнительные кейсы (loading, errors, properties).""" + + async def test_last_head_and_tail_no_reader(self): + inspector = FileInspector() + assert inspector.last_head == b"" + assert inspector.last_tail == b"" + + async def test_inspect_no_head_data_returns_error(self): + session = _make_mock_session(b"", b"", "", 0) + info = await FileInspector().inspect_url( + "https://x.com/e", session=session + ) + assert info.status == "error" + assert "Недостаточно данных" in info.parse_note + + async def test_looks_like_html_short_head_is_false(self): + assert not FileInspector._looks_like_html(b"", "") + + async def test_looks_like_html_by_content_type(self): + assert FileInspector._looks_like_html(b"abc", "text/html") + + async def test_looks_like_html_by_doctype(self): + assert FileInspector._looks_like_html( + b"xyz", "text/plain" + ) + + async def test_looks_like_html_by_html_tag(self): + assert FileInspector._looks_like_html( + b"", "text/plain" + ) + + async def test_build_file_info_bitrate_avg(self): + info = FileInspector._build_file_info( + url="test", + file_size=1_000_000, + dims={"duration": 10, "format": "MP3"}, + status="ok", + ) + assert info.bitrate_avg == 781 + + async def test_build_file_info_duration_under_10_rounds_to_1dp(self): + info = FileInspector._build_file_info( + url="test", dims={"duration": 5.56}, status="ok" + ) + assert info.duration == 5.6 + + async def test_build_file_info_duration_over_10_rounds_to_int(self): + info = FileInspector._build_file_info( + url="test", dims={"duration": 12.55}, status="ok" + ) + assert info.duration == 13 + + async def test_split_parse_result_invalid_status_becomes_partial(self): + _, _, status = FileInspector._split_parse_result( + {"_status": "invalid"} + ) + assert status == "partial" + + async def test_parse_media_dimensions_tiny_head_returns_none(self): + assert FileInspector.parse_media_dimensions(b"x", None) is None + + async def test_parse_media_dimensions_full_file_in_head_uses_head_as_tail( + self, + ): + head = ( + b"\x89PNG\r\n\x1a\n\x00\x00\x00\x0d" + b"IHDR\x00\x00\x00\x01\x00\x00\x00\x01" + ) + result = FileInspector.parse_media_dimensions( + head, None, file_size=len(head) + ) + assert result is not None + assert result["format"] == "PNG" + + async def test_mime_overridden_from_format_when_octet_stream(self): + head = ( + b"\x89PNG\r\n\x1a\n\x00\x00\x00\x0d" + b"IHDR\x00\x00\x00\x01\x00\x00\x00\x01" + ) + session = _make_mock_session( + head, None, "application/octet-stream", len(head) + ) + info = await FileInspector().inspect_url( + "https://x.com/t", session=session + ) + assert info.mime_type == "image/png" + + async def test_generic_exception_in_inspect_bytes_caught(self): + inspector = FileInspector() + with ( + patch.object(inspector, "last_file_info", None), + patch.object( + inspector, "_inspect", side_effect=ValueError("custom") + ), + pytest.raises(ValueError, match="custom"), + ): + await inspector.inspect_bytes(b"data", file_name="test") + + async def test_initial_head_size_small_file(self): + from maxapi.utils.file_inspector import RangeDownloader + + rd = RangeDownloader("https://x.com/f") + rd.file_size = 100 + assert rd._initial_head_size() == 100 + + async def test_initial_head_size_below_threshold(self): + rd = RangeDownloader("https://x.com/f") + rd.file_size = 1_000_000 + assert rd._initial_head_size() == 1_000_000 + + async def test_initial_head_size_large_file(self): + rd = RangeDownloader("https://x.com/f") + rd.file_size = 100_000_000 + assert rd._initial_head_size() == 4096 + + async def test_initial_head_size_no_file_size(self): + rd = RangeDownloader("https://x.com/f") + rd.file_size = None + assert rd._initial_head_size() == 4096 + + +class TestRangeDownloaderAdvanced: + """RangeDownloader — прямые тесты методов.""" + + async def test_is_trusted_url_true(self): + assert RangeDownloader("https://oneme.ru/f.jpg")._is_trusted_url + assert RangeDownloader("https://sub.okcdn.ru/f.jpg")._is_trusted_url + + async def test_is_trusted_url_false(self): + assert not RangeDownloader("https://evil.com/f.jpg")._is_trusted_url + assert not RangeDownloader("https://oneme.com/f.jpg")._is_trusted_url + + async def test_final_url_without_meta_returns_original(self): + assert ( + RangeDownloader("https://x.com/f").final_url == "https://x.com/f" + ) + + async def test_closed_aiter_returns_immediately(self): + rd = RangeDownloader("https://x.com/f") + rd._closed = True + count = 0 + async for _ in rd: + count += 1 + assert count == 0 + + async def test_read_head_bytes_no_response_raises(self): + rd = RangeDownloader("https://x.com/f") + with pytest.raises(RuntimeError, match="Response отсутствует"): + await rd._read_head_bytes(100) + + async def test_fetch_meta_auth_untrusted_raises(self): + rd = RangeDownloader( + "https://evil.com/f.jpg", headers={"Authorization": "Bearer x"} + ) + with pytest.raises(aiohttp.ClientResponseError): + await rd._fetch_meta() + + async def test_fetch_meta_auth_untrusted_cookie(self): + rd = RangeDownloader( + "https://evil.com/f.jpg", headers={"Cookie": "x=y"} + ) + with pytest.raises(aiohttp.ClientResponseError): + await rd._fetch_meta() + + async def test_fetch_meta_auth_allowed_with_flag(self): + rd = RangeDownloader( + "https://evil.com/f.jpg", + headers={"Authorization": "Bearer x"}, + allow_external_auth=True, + ) + resp = AsyncMock( + status=200, + ok=True, + url=URL("https://evil.com/f.jpg"), + headers={}, + history=(), + request_info=Mock(), + release=Mock(), + ) + resp.content = AsyncMock() + resp.content.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") + resp.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") + rd.session = AsyncMock() + rd.session.get = AsyncMock(return_value=resp) + await rd._fetch_meta() + assert rd._meta is not None + + async def test_fetch_meta_bad_content_length(self): + rd = RangeDownloader("https://x.com/f") + resp = AsyncMock( + status=200, + ok=True, + url=URL("https://x.com/f"), + headers={"Content-Type": "image/jpeg", "Content-Length": "abc"}, + history=(), + request_info=Mock(), + release=Mock(), + ) + resp.content = AsyncMock() + resp.content.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") + resp.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") + rd.session = AsyncMock() + rd.session.get = AsyncMock(return_value=resp) + await rd._fetch_meta() + assert rd._meta is not None + assert rd._meta.file_size is None + + async def test_fetch_chunk_no_meta_raises(self): + rd = RangeDownloader("https://x.com/f") + rd._meta = None + with pytest.raises(RuntimeError, match="Метаинформация не загружена"): + await rd._fetch_chunk(100, tail=True) + + async def test_fetch_chunk_tail_range_check_returns_empty(self): + rd = RangeDownloader("https://x.com/f") + rd._meta = Mock(url="https://x.com/f") + resp = AsyncMock( + status=200, + ok=True, + url=URL("https://x.com/f"), + headers={}, + history=(), + request_info=Mock(), + release=Mock(), + ) + resp.content = AsyncMock() + resp.content.read = AsyncMock(return_value=b"data") + + async def get_side(url, **kw): + return resp + + rd.session = AsyncMock() + rd.session.get = AsyncMock(side_effect=get_side) + rd.head = b"data" + result = await rd._fetch_chunk(100, tail=True) + assert result == b"" + + async def test_fetch_chunk_tail_not_200_206_returns_empty(self): + rd = RangeDownloader("https://x.com/f") + rd._meta = Mock(url="https://x.com/f") + resp = AsyncMock( + status=204, + ok=True, + url=URL("https://x.com/f"), + headers={}, + history=(), + request_info=Mock(), + release=Mock(), + ) + resp.content = AsyncMock() + resp.content.read = AsyncMock(return_value=b"") + + async def get_side(url, **kw): + return resp + + rd.session = AsyncMock() + rd.session.get = AsyncMock(side_effect=get_side) + result = await rd._fetch_chunk(100, tail=True) + assert result == b"" + + async def test_fetch_chunk_head_no_response_raises(self): + rd = RangeDownloader("https://x.com/f") + rd._meta = Mock(url="https://x.com/f") + rd._response = None + with pytest.raises(RuntimeError, match="Response отсутвует"): + await rd._fetch_chunk(100, tail=False) + + async def test_expand_head_closed_response(self): + rd = RangeDownloader("https://x.com/f") + rd._response = Mock(closed=True) + assert await rd._expand_head() == b"" + assert await rd._expand_head(target=100) == b"" + + async def test_expand_head_no_allowed_space(self): + rd = RangeDownloader("https://x.com/f") + rd._response = Mock(closed=False) + rd.head = b"x" * 128_000 + rd.tail = b"y" * 128_000 + rd.max_total = 256_000 + assert await rd._expand_head() == b"" + + async def test_expand_head_target_need_zero(self): + rd = RangeDownloader("https://x.com/f") + rd._response = Mock(closed=False) + rd.head = b"x" * 6000 + rd.max_total = 256_000 + assert await rd._expand_head(target=100) == b"" + + async def test_expand_head_exception_during_read(self): + rd = RangeDownloader("https://x.com/f") + rd._response = Mock(closed=False) + rd._response.content = AsyncMock() + rd._response.content.read = AsyncMock(side_effect=ValueError("boom")) + result = await rd._expand_head() + assert result == b"" + + async def test_request_with_retry_no_session_raises(self): + rd = RangeDownloader("https://x.com/f") + rd.session = None + with pytest.raises(RuntimeError, match="Сессия не установлена"): + await rd._request_with_retry("https://x.com/f") + + async def test_request_with_retry_connection_error(self): + rd = RangeDownloader( + "https://x.com/f", max_retries=1, retry_backoff_factor=0 + ) + rd.session = AsyncMock() + rd.session.get = AsyncMock( + side_effect=aiohttp.ClientConnectionError("fail") + ) + with pytest.raises(aiohttp.ClientConnectionError): + await rd._request_with_retry("https://x.com/f") + + async def test_request_with_retry_connection_then_success(self): + rd = RangeDownloader( + "https://x.com/f", max_retries=1, retry_backoff_factor=0 + ) + good = Mock( + status=200, + ok=True, + url=URL("https://x.com/f"), + headers={}, + release=Mock(), + request_info=Mock(), + history=(), + ) + rd.session = AsyncMock() + rd.session.get = AsyncMock( + side_effect=[aiohttp.ClientConnectionError("fail"), good] + ) + resp = await rd._request_with_retry("https://x.com/f") + assert resp.status == 200 + + async def test_request_with_retry_4xx_no_retry(self): + rd = RangeDownloader( + "https://x.com/f", max_retries=2, retry_backoff_factor=0 + ) + resp = Mock( + status=403, + ok=False, + release=Mock(), + headers={}, + request_info=Mock(), + history=(), + ) + rd.session = AsyncMock() + rd.session.get = AsyncMock(return_value=resp) + with pytest.raises(aiohttp.ClientResponseError) as exc: + await rd._request_with_retry("https://x.com/f") + assert exc.value.status == 403 + + async def test_request_with_retry_server_error_retry_then_raise(self): + rd = RangeDownloader( + "https://x.com/f", + max_retries=1, + retry_backoff_factor=0, + retry_on_statuses=(500,), + ) + resp = Mock( + status=500, + ok=False, + release=Mock(), + headers={}, + request_info=Mock( + url=URL("https://x.com/f"), method="GET", headers=Mock() + ), + history=(), + ) + rd.session = AsyncMock() + rd.session.get = AsyncMock(return_value=resp) + with pytest.raises(aiohttp.ClientResponseError) as exc: + await rd._request_with_retry("https://x.com/f") + assert exc.value.status == 500 + + async def test_extract_filename_content_disposition_multiple_parts(self): + h = { + "Content-Disposition": 'form-data; name="file"; filename="rl.jpg"' + } + assert ( + RangeDownloader._extract_filename(h, "https://x.com/123") + == "rl.jpg" + ) + + async def test_extract_filename_from_url_path(self): + assert ( + RangeDownloader._extract_filename({}, "https://x.com/photo.jpg") + == "photo.jpg" + ) + + async def test_extract_filename_url_without_path(self): + assert ( + RangeDownloader._extract_filename({}, "https://x.com/") + == "unknown" + ) + + async def test_extract_filename_url_with_query(self): + assert ( + RangeDownloader._extract_filename( + {}, "https://x.com/photo.jpg?w=100" + ) + == "photo.jpg" + ) + + async def test_extract_filename_url_encoded(self): + name = RangeDownloader._extract_filename( + {}, "https://x.com/%D1%82%D0%B5%D1%81%D1%82.txt" + ) + assert name == "тест.txt" + + async def test_aiter_with_file_size_under_max_head(self): + dl = RangeDownloader("https://x.com/f") + dl.file_size = 100 + dl._meta = Mock( + url="https://x.com/f", content_type="", file_name="", file_size=100 + ) + dl._fetched_meta = True + dl.session = AsyncMock() + resp = AsyncMock( + status=200, + ok=True, + url=URL("https://x.com/f"), + headers={"Content-Type": "", "Content-Length": "100"}, + history=(), + request_info=Mock(), + release=Mock(), + ) + resp.content = AsyncMock() + resp.content.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") + resp.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") + dl._response = resp + count = 0 + async for _ in dl: + count += 1 + assert count >= 1 + + async def test_close_twice(self): + rd = RangeDownloader("https://x.com/f") + await rd.close() + await rd.close() + + async def test_aexit_closes_own_session(self): + rd = RangeDownloader("https://x.com/f") + rd._own_session = True + session = AsyncMock() + rd.session = session + await rd.__aexit__(None, None, None) + session.close.assert_awaited_once() + assert rd.session is None + + async def test_satisfy_pending_need_head_expand(self, tmp_path): + from maxapi.utils.file_inspector import RangeFileReader + + fp = tmp_path / "t.bin" + fp.write_bytes(b"x" * 500) + reader = RangeFileReader(str(fp)) + async for _ in reader: + reader.pending_needs = {"_need_head": -1} + break + result = await reader._satisfy_pending_needs() + assert not result + + async def test_range_file_reader_expand_head_after_loop(self, tmp_path): + from maxapi.utils.file_inspector import RangeFileReader + + fp = tmp_path / "t2.bin" + fp.write_bytes(b"data for test file reader") + reader = RangeFileReader(str(fp)) + async for _ in reader: + pass + assert await reader._expand_head() == b"" + assert await reader._fetch_tail(100) == b"" + + async def test_file_reader_non_existent(self): + info = await FileInspector().inspect_file(r"R:\nonexistent\file.jpg") + assert info.status == "error" + assert info.parse_note == "Файл не найден" + + +class TestRangeBytesReaderEdgeCases: + """RangeBytesReader — BytesIO/NamedBytesIO, expand/satisfy.""" + + async def test_bytesio_constructor(self): + from maxapi.utils.file_inspector import RangeBytesReader + + reader = RangeBytesReader(BytesIO(b"test data for bytes"), "f.txt") + assert reader.file_size == 19 + + async def test_bytesio_without_name(self): + from maxapi.utils.file_inspector import RangeBytesReader + + reader = RangeBytesReader(BytesIO(b"test")) + assert reader.file_size == 4 + + async def test_named_bytesio_uses_its_name(self): + from maxapi.utils.file_inspector import RangeBytesReader + + nbio = NamedBytesIO(b"test data") + nbio.name = "test.txt" + reader = RangeBytesReader(nbio) + assert reader.file_name == "test.txt" + + async def test_expand_head_target_exhausted(self): + from maxapi.utils.file_inspector import RangeBytesReader + + reader = RangeBytesReader(b"hello") + async for _ in reader: + pass + assert await reader._expand_head(target=50) == b"" + + async def test_expand_head_zero_chunk(self): + from maxapi.utils.file_inspector import RangeBytesReader + + reader = RangeBytesReader(b"ab") + async for _ in reader: + pass + assert await reader._expand_head() == b"" + + async def test_aiter_with_pending_needs_continues(self): + from maxapi.utils.file_inspector import RangeBytesReader + + data = b"A" * 8192 + b"B" * 8192 + reader = RangeBytesReader(data) + reader.pending_needs = {"_need_tail": 100} + count = 0 + async for _ in reader: + count += 1 + if count >= 3: + break + assert count >= 2 + + async def test_small_file_read_fully(self): + from maxapi.utils.file_inspector import RangeBytesReader + + data = ( + b"\x89PNG\r\n\x1a\n" + + b"\x00" * 16 + + b"IHDR\x00\x00\x00\x01\x00\x00\x00\x01" + ) + reader = RangeBytesReader(data, "t.png", full_read_threshold=100) + async for _ in reader: + pass + assert len(reader.head) == len(data) + + async def test_one_iteration_without_pending(self): + from maxapi.utils.file_inspector import RangeBytesReader + + reader = RangeBytesReader(b"test") + count = 0 + async for _ in reader: + count += 1 + assert count == 1 + + async def test_fetch_tail_from_bytes_reader(self): + from maxapi.utils.file_inspector import RangeBytesReader + + data = b"x" * 100 + b"y" * 100 + reader = RangeBytesReader(data) + async for _ in reader: + pass + tail = await reader._fetch_tail(50) + assert tail == data[-50:] + + +class TestParsersEdgeCases: + """Прямые тесты статических методов парсеров (для покрытия).""" + + # --- JPEG --- + def test_jpeg_no_sof_returns_partial_with_need(self): + """JPEG без SOF -> partial + need_head/need_tail.""" + data = b"\xff\xd8\xff\xe1\x00\x10\x00" + b"\x00" * 20 + r = FileInspector._jpeg_parse(data) + assert r is not None + assert r["_status"] == "partial" + + def test_jpeg_sof_found_returns_dims(self): + """JPEG с SOF -> размеры изображения.""" + data = ( + b"\xff\xd8\xff\xc0\x00\x0b\x08\x00\x10" + b"\x00\x0e\x03\x01\x22\x00\x02\x11\x01" + ) + r = FileInspector._jpeg_parse(data) + assert r is not None + assert r["width"] == 14 + assert r["height"] == 16 + + def test_jpeg_not_jpeg(self): + """Не JPEG - не подходит.""" + assert FileInspector._jpeg_parse(b"\x00\x00") is None + + # --- PNG checks --- + def test_png_check_short(self): + """PNG check: слишком короткий.""" + assert not FileInspector._png_check( + b"\x89PNG\r\n\x1a\n\x00\x00\x00\x0d" + ) + + def test_png_check_valid(self): + """PNG check: сигнатура совпадает.""" + assert FileInspector._png_check( + b"\x89PNG\r\n\x1a\n\x00\x00\x00\x0dIHDR" + b"\x00" * 8 + ) + + # --- WEBP checks --- + def test_webp_check_short(self): + """WEBP check: слишком короткий.""" + assert not FileInspector._webp_check(b"RIFF\x00\x00\x00\x00") + + def test_webp_check_valid(self): + """WEBP check: сигнатура совпадает.""" + assert FileInspector._webp_check( + b"\x00\x00\x00\x00\x00\x00\x00\x00WEBP" + ) + + # --- GIF checks --- + def test_gif_check_short(self): + """GIF check: слишком короткий.""" + assert not FileInspector._gif_check(b"GIF87") + + def test_gif_check_valid(self): + """GIF check: сигнатура совпадает.""" + assert FileInspector._gif_check(b"GIF89a" + b"\x00" * 4) + + # --- MP3 checks --- + def test_mp3_check_id3(self): + """MP3 check: ID3-тег.""" + assert FileInspector._mp3_check(b"ID3") + + def test_mp3_check_frame_sync(self): + """MP3 check: синхронизация фрейма.""" + assert FileInspector._mp3_check(b"\xff\xe0") + + def test_mp3_check_no_match(self): + """MP3 check: нет совпадения.""" + assert not FileInspector._mp3_check(b"\x00\x00") + + # --- WAV check --- + def test_wav_check_valid(self): + """WAV check: сигнатура RIFF + WAVE.""" + assert FileInspector._wav_check(b"RIFF\x00\x00\x00\x00WAVE") + + def test_wav_check_no_wave(self): + """WAV check: без WAVE-идентификатора.""" + assert not FileInspector._wav_check(b"RIFF\x00\x00\x00\x00AVI ") + + def test_wav_check_short(self): + """WAV check: слишком короткий.""" + assert not FileInspector._wav_check(b"RIFF") + + # --- FLAC check --- + def test_flac_check(self): + """FLAC check: сигнатура fLaC.""" + assert FileInspector._flac_check(b"fLaC") + assert not FileInspector._flac_check(b"FLAC") + + # --- OGG check --- + def test_ogg_ogv_check(self): + """OGG/OGV check: сигнатура OggS.""" + assert FileInspector._ogg_ogv_check(b"OggS") + assert not FileInspector._ogg_ogv_check(b"OggX") + + # --- AAC check --- + def test_aac_check_valid(self): + """AAC check: синхронизация фрейма.""" + assert FileInspector._aac_check(b"\xff\xf1") + + def test_aac_check_no_match(self): + """AAC check: нет совпадения.""" + assert not FileInspector._aac_check(b"\x00\x00") + + # --- M4A check --- + def test_m4a_check_valid(self): + """M4A check: ftyp M4A.""" + assert FileInspector._m4a_check(b"\x00\x00\x00\x10ftypM4A ") + + def test_m4a_check_no_match(self): + """M4A check: нет совпадения.""" + assert not FileInspector._m4a_check(b"\x00\x00\x00\x10ftypmp42") + + # --- WMA check --- + def test_wma_check_valid(self): + """WMA check: GUID ASF_Header.""" + guid = ( + b"\x30\x26\xb2\x75\x8e\x66\xcf\x11\xa6\xd9\x00\xaa\x00\x62\xce\x6c" + ) + assert FileInspector._wma_check(guid) + + def test_wma_check_no_match(self): + """WMA check: нет совпадения.""" + assert not FileInspector._wma_check(b"\x00" * 16) + + # --- MP4 check --- + def test_mp4_check_short(self): + """MP4 check: слишком короткий.""" + assert not FileInspector._mp4_check(b"abc") + + def test_mp4_check_ftyp_video(self): + """MP4 check: ftyp для видео.""" + assert FileInspector._mp4_check( + b"\x00\x00\x00\x10ftypmp42\x00\x00\x00\x00" + ) + + def test_mp4_check_old_qt(self): + """MP4 check: старый QuickTime (moov).""" + assert FileInspector._mp4_check(b"moov") + + def test_mp4_check_not_mp4(self): + """MP4 check: не MP4.""" + assert not FileInspector._mp4_check(b"\x00\x00\x00\x10ftypM4A ") + + # --- AVI check --- + def test_avi_check_valid(self): + """AVI check: RIFF + AVI.""" + assert FileInspector._avi_check(b"RIFF\x00\x00\x00\x00AVI ") + + def test_avi_check_no_match(self): + """AVI check: нет совпадения.""" + assert not FileInspector._avi_check(b"RIFF\x00\x00\x00\x00WAVE") + + def test_avi_check_short(self): + """AVI check: слишком короткий.""" + assert not FileInspector._avi_check(b"RIFF") + + # --- WebM/MKV check --- + def test_webm_mkv_check_valid(self): + """WebM/MKV check: EBML + docType.""" + assert FileInspector._webm_mkv_check(b"\x00\x45\xdf\xa3") + + def test_webm_mkv_check_short(self): + """WebM/MKV check: слишком короткий.""" + assert not FileInspector._webm_mkv_check(b"abc") + + # --- MP4 parsing --- + def test_mp4_find_moov_not_found(self): + """moov не найден в данных.""" + assert FileInspector._mp4_find_moov(b"\x00" * 100) is None + + def test_mp4_find_moov_short(self): + """Недостаточно данных для moov.""" + assert FileInspector._mp4_find_moov(b"moov") is None + + def test_mp4_moov_parse_empty(self): + """Пустой moov - нет mvhd.""" + assert FileInspector._mp4_moov_parse(b"\x00\x00\x00\x00") is None + + def test_mp4_moov_parse_mvhd(self): + """moov c mvhd - длительность + шкала.""" + mvhd = ( + b"\x00\x00\x00\x30mvhd\x00\x00\x00\x00\x00\x00\x00\x00" + b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01" + b"\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00" + b"\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" + ) + # doesn't have enough for mvhd parsing, but tests the path + result = FileInspector._mp4_moov_parse(mvhd) + assert result is None or "duration" not in result + + def test_mp4_moov_parse_size_one_too_short(self): + """size=1 (64-бит), но данных мало.""" + result = FileInspector._mp4_moov_parse(b"\x00\x00\x00\x01mvhd\x00") + assert result is None + + def test_mp4_parse_trak_for_dims_no_tkhd(self): + """trak без tkhd - нет размеров.""" + result = FileInspector._mp4_parse_trak_for_dims( + b"\x00\x00\x00\x08mdia\x00\x00\x00\x08" + ) + assert result is None + + def test_mp4_parse_trak_for_dims_size_one(self): + """trak с size=1 (64-бит).""" + data = b"\x00\x00\x00\x01" + b"tkhd" + b"\x00" * 12 + b"\x00" * 88 + result = FileInspector._mp4_parse_trak_for_dims(data) + assert result is None or len(result) == 0 + + def test_mp4_parse_tkhd_version0(self): + """tkhd version=0 - ширина/высота.""" + data = bytearray(84) + data[76:80] = b"\x00\x01\x00\x00" + data[80:84] = b"\x00\x01\x00\x00" + r = FileInspector._mp4_parse_tkhd(bytes(data)) + assert r is not None + assert r["width"] == 1 + + def test_mp4_parse_tkhd_version0_too_short(self): + """tkhd v0: слишком короткий.""" + assert FileInspector._mp4_parse_tkhd(b"\x00" + b"\x00" * 70) is None + + def test_mp4_parse_tkhd_version1(self): + """tkhd version=1 - ширина/высота.""" + data = bytearray(100) + data[0] = 1 + data[92:96] = b"\x00\x01\x00\x00" + data[96:100] = b"\x00\x01\x00\x00" + r = FileInspector._mp4_parse_tkhd(bytes(data)) + assert r is not None + assert r["width"] == 1 + + def test_mp4_parse_tkhd_version1_too_short(self): + """tkhd v1: слишком короткий.""" + assert FileInspector._mp4_parse_tkhd(b"\x01" + b"\x00" * 90) is None + + def test_mp4_parse_tkhd_unknown_version(self): + """tkhd: неизвестная версия.""" + assert FileInspector._mp4_parse_tkhd(b"\x02" + b"\x00" * 100) is None + + def test_mp4_is_valid_dims_16384(self): + """Размер 16384 - допустим.""" + assert not FileInspector._mp4_is_valid_video_dims( + {"width": 16384, "height": 16384} + ) + + def test_mp4_is_valid_dims_too_small(self): + """Размер < 2 - недопустим.""" + assert not FileInspector._mp4_is_valid_video_dims( + {"width": 1, "height": 1} + ) + + def test_mp4_is_valid_dims_too_large(self): + """Размер > 16384 - недопустим.""" + assert not FileInspector._mp4_is_valid_video_dims( + {"width": 20000, "height": 20000} + ) + + def test_mp4_is_valid_dims_none(self): + """None - недопустим.""" + assert not FileInspector._mp4_is_valid_video_dims(None) + + def test_mp4_is_valid_dims_ok(self): + """Корректные размеры - допустимы.""" + assert FileInspector._mp4_is_valid_video_dims( + {"width": 640, "height": 480} + ) + + def test_mp4_parse_sample_rate_not_found(self): + """Частота дискретизации не найдена.""" + assert FileInspector._mp4_parse_sample_rate(b"\x00" * 100) is None + + def test_mp4_m4a_not_mp4(self): + """Не MP4 - не обрабатывается как M4A.""" + assert FileInspector._mp4_m4a_parse_info(b"\x00" * 16) is None + + def test_mp4_m4a_no_moov_no_tail(self): + """M4A без moov и tail.""" + r = FileInspector._mp4_m4a_parse_info( + b"\x00\x00\x00\x10ftypmp42\x00\x00\x00\x00" + ) + assert r is not None + assert r["_status"] == "partial" + assert "_need_tail" in r + + def test_m4a_parse_mvhd_too_short(self): + """mvhd: слишком короткий заголовок.""" + assert FileInspector._m4a_parse_mvhd_duration(b"\x00" * 10) is None + + def test_m4a_parse_mvhd_version0_duration(self): + """mvhd v0: duration = 100 / 1 = 100.0.""" + data = ( + b"\x00" * 8 + + b"\x00\x00\x00\x00" + + b"\x00" * 8 + + b"\x00\x00\x00\x01\x00\x00\x00\x64" + ) + r = FileInspector._m4a_parse_mvhd_duration(data) + assert r == 100.0 + + def test_m4a_parse_mvhd_version1_duration(self): + """mvhd v1: duration = 100 / 1 = 100.0 (QWORD-поле).""" + data = ( + b"\x00" * 8 + + b"\x01\x00\x00\x00" + + b"\x00" * 8 + + b"\x00\x00\x00\x01" + + struct.pack(">Q", 100) + ) + r = FileInspector._m4a_parse_mvhd_duration(data) + assert r == 100.0 + + # --- AVI parsing --- + def test_avi_parse_too_small(self): + """AVI: слишком маленький.""" + assert FileInspector._avi_parse_info(b"RIF") is None + + def test_avi_parse_not_avi(self): + """AVI: не AVI-файл.""" + assert ( + FileInspector._avi_parse_info(b"RIFF\x00\x00\x00\x00WAVE") is None + ) + + def test_avi_parse_no_hdrl(self): + """AVI: нет hdrl-листа.""" + data = b"RIFF\x00\x00\x00\x00AVI " + b"\x00" * 100 + r = FileInspector._avi_parse_info(data) + assert r is not None + assert r["format"] == "AVI" + + def test_parse_avih_too_short(self): + """avih: слишком короткий.""" + r = {"format": "AVI", "width": None} + FileInspector._parse_avih(b"", 0, 10, r) + assert r["width"] is None + + def test_parse_avih_with_data(self): + """avih: длительность + флаги.""" + r = { + "format": "AVI", + "width": None, + "total_frames": None, + "bitrate": None, + "height": None, + } + content = ( + b"\x00" * 4 + + b"\x01\x00\x00\x00" + + b"\x00" * 8 + + b"\x64\x00\x00\x00" + + b"\x00" * 12 + + b"\x80\x07\x00\x00\x38\x04\x00\x00" + + b"\x00" * 16 + ) + FileInspector._parse_avih(content, 0, len(content), r) + assert r["total_frames"] == 100 + + def test_parse_strh_too_short(self): + """strh: слишком короткий.""" + r = {"format": "AVI", "width": None, "sample_rate": None, "fps": None} + FileInspector._parse_strh(b"", 0, 10, r) + assert r["width"] is None + + def test_parse_strh_vids_with_dims(self): + """strh vids: ширина/высота.""" + r = {"format": "AVI", "width": None, "sample_rate": None, "fps": None} + data = ( + b"vids" + + b"\x00" * 16 + + struct.pack("f", 3.14) + data = b"\x42\x86\x84" + val + r = FileInspector._webm_read_ebml_float(data, b"\x42\x86") + assert r is not None + assert abs(r - 3.14) < 0.01 + + def test_webm_read_ebml_float_8byte(self): + """EBML float: 8 байт.""" + import struct + + val = struct.pack(">d", 3.14159) + data = b"\x42\x86\x88" + val + r = FileInspector._webm_read_ebml_float(data, b"\x42\x86") + assert r is not None + assert abs(r - 3.14159) < 0.001 + + def test_webm_read_ebml_string_ok(self): + """EBML string: успешное чтение.""" + data = b"\x42\x82\x84webm" + assert ( + FileInspector._webm_read_ebml_string(data, b"\x42\x82") == "webm" + ) + + def test_webm_read_ebml_string_unicode_error(self): + """EBML string: ошибка Unicode.""" + data = b"\x42\x82\x84\xff\xff\xff\xff" + assert FileInspector._webm_read_ebml_string(data, b"\x42\x82") is None + + def test_webm_read_duration_seconds_no_duration(self): + """Нет Duration -> None.""" + assert FileInspector._webm_read_duration_seconds(b"\x00" * 10) is None + + def test_webm_read_duration_seconds_with_default_scale(self): + """Duration с TimecodeScale по умолчанию.""" + import struct + + dur = struct.pack(">d", 10000.0) + assert ( + FileInspector._webm_read_duration_seconds(b"\x44\x89\x88" + dur) + == 10 + ) + + def test_webm_read_duration_seconds_with_custom_scale(self): + """Duration с кастомным TimecodeScale.""" + import struct + + dur = struct.pack(">d", 10.0) + tc = b"\x2a\xd7\xb1\x84\x00\x00\x00\x01" + r = FileInspector._webm_read_duration_seconds( + b"\x44\x89\x88" + dur + tc + ) + assert r == 0 # 10 * 1 / 1e9 = 0 + + # --- MP3 parsing --- + def test_mp3_parse_not_enough_data(self): + """Недостаточно данных для парсинга.""" + r = FileInspector._mp3_parse_info(b"\x00\x00\x00\x00", file_size=100) + assert r is not None + assert r["_status"] == "partial" + + def test_mp3_parse_frame_found_with_xing(self): + """Xing с 154 кадрами → duration = 154*1152/44100 ≈ 4с.""" + header = b"\xff\xfb\x90\x00" + xing_body = ( + b"Xing" + b"\x00\x00\x00\x07" + + struct.pack(">I", 154) + + b"\x00\x00\x00\x00" + + b"\x00\x00\x0e\x10" + ) + padding_before = b"\x00" * 17 # offset from frame header to Xing = 21 + padding_after = b"\x00" * (100 - 4 - 17 - len(xing_body)) + data = header + padding_before + xing_body + padding_after + r = FileInspector._mp3_parse_info(data, file_size=10000) + assert r is not None + assert r.get("duration") == 4 + + def test_mp3_parse_vbri(self): + """VBRI-заголовок: длительность + битрейт.""" + header = b"\xff\xfb\x90\x00" + vbri = ( + b"\x00" * 32 + + b"VBRI" + + b"\x00\x00\x00\x00\x00\x00" + + b"\x00\x00\x00\x64" + ) + r = FileInspector._mp3_parse_info(header + vbri, file_size=100000) + assert r is not None + + def test_mp3_find_frame_header_none(self): + """Заголовок фрейма не найден.""" + assert ( + FileInspector._mp3_find_frame_header(b"\x00\x00\x00\x00", 0) + is None + ) + + def test_mp3_find_frame_header_found(self): + """Заголовок фрейма найден.""" + r = FileInspector._mp3_find_frame_header( + b"\x00\x00\xff\xfb\x90\x00" + b"\x00" * 20, 0 + ) + assert r == 2 + + def test_mp3_bitrate_table_reserved(self): + """Зарезервированный битрейт -> None.""" + assert all(x is None for x in FileInspector._mp3_bitrate_table(0, 0)) + + def test_mp3_parse_xing_not_found(self): + """Xing не найден.""" + assert ( + FileInspector._mp3_parse_xing_vbri( + b"\xff\xfb\x90\x00" + b"\x00" * 100, 0 + ) + is None + ) + + def test_mp3_parse_xing_vbri_too_short(self): + """Xing/VBRI слишком короткий.""" + assert FileInspector._mp3_parse_xing_vbri(b"", 0) is None + + # --- OGG parsing --- + def test_ogg_extract_last_granule_no_tail(self): + """Нет tail -> partial + need_tail.""" + assert FileInspector._ogg_extract_last_granule(None) is None + + def test_ogg_extract_last_granule_short_tail(self): + """tail < 27 байт -> partial + need_tail.""" + assert FileInspector._ogg_extract_last_granule(b"abc") is None + + def test_ogg_parse_info_no_head(self): + """Нет head -> error.""" + assert FileInspector._ogg_parse_info(b"\x00" * 10, None) is None + + def test_ogg_parse_info_short_tail(self): + """tail < 27 байт → NEED_TAIL, status partial.""" + vorbis_payload = ( + b"\x01vorbis" + + struct.pack(" duration None.""" + fmt = ( + b"fmt \x10\x00\x00\x00\x01\x00\x01\x00\x00\x00" + b"\x00\x00\x00\x00\x00\x00\x02\x00\x10\x00" + ) + wav = b"RIFF\x00\x00\x00\x00WAVE" + fmt + r = FileInspector._wav_parse_info(wav, total_size=200) + assert r is not None + assert r["_status"] == "partial" + + # --- FLAC parsing --- + def test_flac_not_flac(self): + """Не FLAC -> error.""" + assert FileInspector._flac_parse_info(b"xxxx") is None + + def test_flac_with_padding(self): + """FLAC c PADDING-блоком.""" + si = b"\x00" * 34 + data = b"fLaC\x01\x00\x00\x04\x00\x00\x00\x00\x80\x00\x00\x22" + si + assert FileInspector._flac_parse_info(data) is not None + + def test_flac_with_unknown_block(self): + """FLAC с неизвестным блоком.""" + si = b"\x00" * 34 + data = b"fLaC\x03\x00\x00\x04\x00\x00\x00\x00\x80\x00\x00\x22" + si + assert FileInspector._flac_parse_info(data) is not None + + def test_flac_last_block(self): + """FLAC: последний блок (bit 7 = 1).""" + si = b"\x00" * 34 + data = b"fLaC\x80\x00\x00\x22" + si + assert FileInspector._flac_parse_info(data) is not None + + def test_flac_streaminfo_too_short(self): + """FLAC STREAMINFO: слишком короткий.""" + data = b"fLaC\x80\x00\x00\x22" + b"\x00" * 10 + assert FileInspector._flac_parse_info(data) is None + + # --- AAC parsing --- + def test_aac_no_frames(self): + """Нет AAC-фреймов.""" + r = FileInspector._aac_parse_info(b"\x00" * 100) + assert r is not None + assert r["_status"] == "partial" + + def test_aac_with_id3(self): + """AAC с ID3-тегом в начале.""" + id3 = b"ID3\x04\x00\x00\x00\x00\x00\x23" + frame = b"\xff\xf1\x50\x80\x03\xff\xf9" + b"\x00" * 100 + r = FileInspector._aac_parse_info(id3 + b"\x00" * 35 + frame) + assert r is not None + assert r["format"] == "AAC" + + def test_aac_parse_sample_rate_out_of_range(self): + """Частота дискретизации вне диапазона.""" + frame = b"\xff\xf1\x70\x80\x03\xff\xf9" + b"\x00" * 100 + r = FileInspector._aac_parse_info(frame) + assert r is not None + + # --- parse_audio_dimensions --- + def test_parse_audio_dimensions_no_match(self): + """Аудио: нет совпадения -> None.""" + assert ( + FileInspector._parse_audio_dimensions(b"\x00" * 10, None, None) + is None + ) + + # --- parse_video_dimensions --- + def test_parse_video_dimensions_no_match(self): + """Видео: нет совпадения -> None.""" + assert ( + FileInspector._parse_video_dimensions(b"\x00" * 10, None, None) + is None + ) + + # --- parse_image_dimensions --- + def test_parse_image_dimensions_no_match(self): + """Изображение: нет совпадения -> None.""" + assert ( + FileInspector._parse_image_dimensions(b"\x00" * 10, None) is None + ) + + # --- _ogg_calculate_duration --- + def test_ogg_calculate_theora_no_fps(self): + """Theora: FPS отсутствует.""" + assert ( + FileInspector._ogg_calculate_duration({"type": "theora"}, 100) + is None + ) + + def test_ogg_calculate_theora_fps_zero(self): + """Theora: FPS = 0.""" + assert ( + FileInspector._ogg_calculate_duration( + {"type": "theora", "fps_num": 0, "fps_den": 1}, 100 + ) + is None + ) + + def test_ogg_calculate_vorbis_no_sample_rate(self): + """Vorbis: нет sample_rate.""" + assert ( + FileInspector._ogg_calculate_duration({"type": "vorbis"}, 100) + is None + ) + + def test_ogg_calculate_unknown_type(self): + """Неизвестный тип OGG-кодека.""" + assert ( + FileInspector._ogg_calculate_duration({"type": "opus"}, 100) + is None + ) + + # --- WMA edge --- + def test_wma_too_short(self): + """WMA: слишком короткий.""" + assert FileInspector._wma_parse_info(b"\x00" * 10) is None + + def test_wma_with_file_props(self): + """ + WMA c FileProperties + StreamProperties: format, duration, sample_rate. + """ + guid = ( + b"\x30\x26\xb2\x75\x8e\x66\xcf\x11\xa6\xd9\x00\xaa\x00\x62\xce\x6c" + ) + fp_guid = ( + b"\xa1\xdc\xab\x8c\x47\xa9\xcf\x11\x8e\xe4\x00\xc0\x0c\x20\x53\x65" + ) + sp_guid = ( + b"\x91\x07\xdc\xb7\xb7\xa9\xcf\x11\x8e\xe6\x00\xc0\x0c\x20\x53\x65" + ) + audio_guid = ( + b"\x40\x9e\x69\xf8\x4d\x5b\xcf\x11\xa8\xfd\x00\x80\x5f\x5c\x44\x2b" + ) + fp_obj = ( + fp_guid + + b"\x00" * 48 + + struct.pack(" нет потоков.""" + assert FileInspector._ogg_parse_all_streams(b"\x00" * 30) == [] + + # --- _wav_parse: data chunk with total_size --- + def test_wav_data_chunk_with_total_size(self): + """WAV: размер из data-чанка.""" + fmt = ( + b"fmt \x10\x00\x00\x00\x01\x00\x01\x00\x44" + b"\xac\x00\x00\x88\x58\x01\x00\x02\x00\x10\x00" + ) + wav = b"RIFF\x00\x00\x00\x00WAVE" + fmt + b"data\x00\x00\x00\x00" + r = FileInspector._wav_parse_info(wav) + assert r is not None + assert r["format"] == "WAV" From 0e0711aff2c732314318b9bccde36e2e7223e701 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Sat, 13 Jun 2026 01:30:37 +0300 Subject: [PATCH 29/31] fix: tests --- tests/test_utils/fileinfo/test_file_info.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_file_info.py index aca39b18..2e973976 100644 --- a/tests/test_utils/fileinfo/test_file_info.py +++ b/tests/test_utils/fileinfo/test_file_info.py @@ -273,7 +273,7 @@ async def test_client_error_returns_error_status(self): inspector = FileInspector() info = await inspector.inspect_url( - "https://example.com/test.jpg", + "https://x.com/x.jpg", session=session, max_retries=1, ) @@ -286,7 +286,7 @@ async def test_generic_exception_returns_error_status(self): inspector = FileInspector() info = await inspector.inspect_url( - "https://example.com/test.jpg", + "https://x.com/x.jpg", session=session, max_retries=1, allow_external_auth=True, @@ -810,8 +810,7 @@ async def test_fetch_meta_bad_content_length(self): resp.content = AsyncMock() resp.content.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") resp.read = AsyncMock(return_value=b"\xff\xd8\xff\xe0") - rd.session = AsyncMock() - rd.session.get = AsyncMock(return_value=resp) + rd.session = _make_mock_session(b"", None) await rd._fetch_meta() assert rd._meta is not None assert rd._meta.file_size is None @@ -873,7 +872,7 @@ async def test_fetch_chunk_head_no_response_raises(self): rd = RangeDownloader("https://x.com/f") rd._meta = Mock(url="https://x.com/f") rd._response = None - with pytest.raises(RuntimeError, match="Response отсутвует"): + with pytest.raises(RuntimeError, match="Response отсутствует"): await rd._fetch_chunk(100, tail=False) async def test_expand_head_closed_response(self): From 9f551f2ca8fe8f64f46772388018351f584b8a38 Mon Sep 17 00:00:00 2001 From: Pankovea <114323250+Pankovea@users.noreply.github.com> Date: Sat, 13 Jun 2026 01:39:08 +0300 Subject: [PATCH 30/31] fix: ruff --- maxapi/utils/file_inspector.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/maxapi/utils/file_inspector.py b/maxapi/utils/file_inspector.py index c4affb05..2c4fa15e 100644 --- a/maxapi/utils/file_inspector.py +++ b/maxapi/utils/file_inspector.py @@ -1454,9 +1454,7 @@ def _webp_parse( # noqa: C901 ) # 4. VP8L: Lossless изображение - elif ( - chunk_type == b"VP8L" and chunk_size >= 5 - ): + elif chunk_type == b"VP8L" and chunk_size >= 5: if payload_start + 5 <= len(data): bits = struct.unpack( " Date: Sat, 13 Jun 2026 02:12:20 +0300 Subject: [PATCH 31/31] add: test_file_info_class.py, rename test_file_info.py -> test_fileInspector.py --- ...est_file_info.py => test_fileInspector.py} | 0 .../fileinfo/test_file_info_class.py | 147 ++++++++++++++++++ 2 files changed, 147 insertions(+) rename tests/test_utils/fileinfo/{test_file_info.py => test_fileInspector.py} (100%) create mode 100644 tests/test_utils/fileinfo/test_file_info_class.py diff --git a/tests/test_utils/fileinfo/test_file_info.py b/tests/test_utils/fileinfo/test_fileInspector.py similarity index 100% rename from tests/test_utils/fileinfo/test_file_info.py rename to tests/test_utils/fileinfo/test_fileInspector.py diff --git a/tests/test_utils/fileinfo/test_file_info_class.py b/tests/test_utils/fileinfo/test_file_info_class.py new file mode 100644 index 00000000..a19038a9 --- /dev/null +++ b/tests/test_utils/fileinfo/test_file_info_class.py @@ -0,0 +1,147 @@ +""" +Прямые тесты для модели FileInfo (свойства, __eq__, __str__). +""" + +from maxapi.types.file_info import FileInfo + + +class TestFileInfoProperties: + """Свойства has_dimensions, is_image, is_audio, is_video.""" + + def test_has_dimensions_true(self): + assert FileInfo(url="u", width=100, height=200).has_dimensions + + def test_has_dimensions_false_none(self): + assert not FileInfo(url="u").has_dimensions + + def test_is_image_true(self): + assert FileInfo(url="u", mime_type="image/png").is_image + + def test_is_image_false(self): + assert not FileInfo(url="u", mime_type="video/mp4").is_image + + def test_is_audio_true(self): + assert FileInfo(url="u", mime_type="audio/mpeg").is_audio + + def test_is_audio_false(self): + assert not FileInfo(url="u", mime_type="image/png").is_audio + + def test_is_video_true(self): + assert FileInfo(url="u", mime_type="video/mp4").is_video + + def test_is_video_false(self): + assert not FileInfo(url="u", mime_type="audio/mpeg").is_video + + +class TestFileInfoFileSizeHuman: + """file_size_human во всех диапазонах.""" + + def test_none(self): + assert FileInfo(url="u").file_size_human == "неизвестно" + + def test_bytes(self): + assert FileInfo(url="u", file_size=500).file_size_human == "500 байт" + + def test_kb(self): + assert FileInfo(url="u", file_size=2048).file_size_human == "2 КБ" + + def test_kb_boundary(self): + assert FileInfo(url="u", file_size=1024).file_size_human == "1 КБ" + + def test_mb(self): + assert ( + FileInfo(url="u", file_size=1_048_576).file_size_human == "1.0 МБ" + ) + + def test_gb(self): + assert ( + FileInfo(url="u", file_size=2_147_483_648).file_size_human + == "2.00 ГБ" + ) + + +class TestFileInfoEq: + """__eq__: сравнение без url и file_name.""" + + def test_equal_same_content(self): + a = FileInfo(url="a", file_name="x", mime_type="image/png") + b = FileInfo(url="b", file_name="y", mime_type="image/png") + assert a == b + + def test_not_equal_different_content(self): + a = FileInfo(url="a", mime_type="image/png") + b = FileInfo(url="b", mime_type="image/jpeg") + assert a != b + + def test_not_implemented(self): + assert FileInfo(url="u").__eq__(42) is NotImplemented + + +class TestFileInfoStr: + """__str__: проверяем ветки форматирования.""" + + def test_minimal(self): + result = FileInfo(url="u").__str__() + assert "[Без имени]" in str(result) + assert "неизвестно" in str(result) + + def test_with_file_name(self): + result = FileInfo(url="u", file_name="test.jpg").__str__() + assert "Имя файла: test.jpg" in str(result) + + def test_with_format(self): + result = FileInfo(url="u", format="PNG").__str__() + assert "Формат: PNG" in str(result) + + def test_with_dimensions(self): + result = FileInfo(url="u", width=1920, height=1080).__str__() + assert "1920×1080 пикс" in str(result) + + def test_with_duration(self): + result = FileInfo(url="u", duration=120.5).__str__() + assert "120.5 сек" in str(result) + + def test_with_fps(self): + result = FileInfo(url="u", fps=29.97).__str__() + assert "29.97 к/с" in str(result) + + def test_with_sample_rate(self): + result = FileInfo(url="u", sample_rate=44100).__str__() + assert "44100 Гц" in str(result) + + def test_with_bitrate_nominal(self): + result = FileInfo(url="u", bitrate_nominal=320).__str__() + assert "320 кбит/с" in str(result) + + def test_with_bitrate_avg(self): + result = FileInfo(url="u", bitrate_avg=256).__str__() + assert "256 кбит/с" in str(result) + + def test_with_parse_note(self): + result = FileInfo(url="u", parse_note="some warning").__str__() + assert "some warning" in str(result) + + def test_full(self): + result = FileInfo( + url="u", + file_name="video.mp4", + file_size=50_000_000, + width=1920, + height=1080, + duration=120.0, + fps=24.0, + sample_rate=48000, + bitrate_nominal=5000, + bitrate_avg=4800, + format="MP4", + parse_note="ok", + ).__str__() + assert "video.mp4" in str(result) + assert "Формат: MP4" in str(result) + assert "1920×1080 пикс" in str(result) + assert "120.0 сек" in str(result) + assert "24.0 к/с" in str(result) + assert "48000 Гц" in str(result) + assert "5000 кбит/с" in str(result) + assert "4800 кбит/с" in str(result) + assert "ok" in str(result)