


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard



@pytest.mark.parametrize(
    'compression_format, is_archive'							, [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]							, )
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, )    ->						List[str]:
    _lowercase       :							Dict      		=		{
        '7z': (seven_zip_file, SevenZipExtractor),
        'bz2': (bza_file, BzipaExtractor),
        'gzip': (gz_file, GzipExtractor),
        'lz4': (lza_file, LzaExtractor),
        'tar': (tar_file, TarExtractor),
        'xz': (xz_file, XzExtractor),
        'zip': (zip_file, ZipExtractor),
        'zstd': (zstd_file, ZstdExtractor),
    }
    _lowercase		,				_lowercase       :							int      		=		input_paths_and_base_extractors[compression_format]
    if input_path is None:
        _lowercase       :							Optional[int]      		=		F'''for \'{compression_format}\' compression_format, '''
        if compression_format == "7z":
            reason += require_pyazr.kwargs["reason"]
        elif compression_format == "lz4":
            reason += require_lza.kwargs["reason"]
        elif compression_format == "zstd":
            reason += require_zstandard.kwargs["reason"]
        pytest.skip(lowerCamelCase_ )
    assert base_extractor.is_extractable(lowerCamelCase_ )
    _lowercase       :							str      		=		tmp_path / ('extracted' if is_archive else 'extracted.txt')
    base_extractor.extract(lowerCamelCase_							, lowerCamelCase_ )
    if is_archive:
        assert output_path.is_dir()
        for file_path in output_path.iterdir():
            assert file_path.name == text_file.name
            _lowercase       :							Union[str, Any]      		=		file_path.read_text(encoding='utf-8' )
    else:
        _lowercase       :							List[Any]      		=		output_path.read_text(encoding='utf-8' )
    _lowercase       :							Tuple      		=		text_file.read_text(encoding='utf-8' )
    assert extracted_file_content == expected_file_content



@pytest.mark.parametrize(
    'compression_format, is_archive'							, [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]							, )
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, )    ->						List[str]:
    _lowercase       :							List[Any]      		=		{
        '7z': seven_zip_file,
        'bz2': bza_file,
        'gzip': gz_file,
        'lz4': lza_file,
        'tar': tar_file,
        'xz': xz_file,
        'zip': zip_file,
        'zstd': zstd_file,
    }
    _lowercase       :							int      		=		input_paths[compression_format]
    if input_path is None:
        _lowercase       :							Tuple      		=		F'''for \'{compression_format}\' compression_format, '''
        if compression_format == "7z":
            reason += require_pyazr.kwargs["reason"]
        elif compression_format == "lz4":
            reason += require_lza.kwargs["reason"]
        elif compression_format == "zstd":
            reason += require_zstandard.kwargs["reason"]
        pytest.skip(lowerCamelCase_ )
    _lowercase       :							List[Any]      		=		Extractor.infer_extractor_format(lowerCamelCase_ )
    assert extractor_format is not None
    _lowercase       :							int      		=		tmp_path / ('extracted' if is_archive else 'extracted.txt')
    Extractor.extract(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
    if is_archive:
        assert output_path.is_dir()
        for file_path in output_path.iterdir():
            assert file_path.name == text_file.name
            _lowercase       :							Optional[int]      		=		file_path.read_text(encoding='utf-8' )
    else:
        _lowercase       :							Any      		=		output_path.read_text(encoding='utf-8' )
    _lowercase       :							Any      		=		text_file.read_text(encoding='utf-8' )
    assert extracted_file_content == expected_file_content



@pytest.fixture
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    import tarfile

    _lowercase       :							Union[str, Any]      		=		tmp_path / 'data_dot_dot'
    directory.mkdir()
    _lowercase       :							Tuple      		=		directory / 'tar_file_with_dot_dot.tar'
    with tarfile.TarFile(lowerCamelCase_							, 'w' ) as f:
        f.add(lowerCamelCase_							, arcname=os.path.join('..'							, text_file.name ) )
    return path



@pytest.fixture
def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Optional[Any]:
    import tarfile

    _lowercase       :							int      		=		tmp_path / 'data_sym_link'
    directory.mkdir()
    _lowercase       :							Union[str, Any]      		=		directory / 'tar_file_with_sym_link.tar'
    os.symlink('..'							, directory / 'subdir'							, target_is_directory=lowerCamelCase_ )
    with tarfile.TarFile(lowerCamelCase_							, 'w' ) as f:
        f.add(str(directory / 'subdir' )							, arcname='subdir' )  # str required by os.readlink on Windows and Python < 3.8
    return path



@pytest.mark.parametrize(
    'insecure_tar_file, error_log'							, [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]							, )
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						int:
    _lowercase       :							Dict      		=		{
        'tar_file_with_dot_dot': tar_file_with_dot_dot,
        'tar_file_with_sym_link': tar_file_with_sym_link,
    }
    _lowercase       :							List[Any]      		=		insecure_tar_files[insecure_tar_file]
    _lowercase       :							List[str]      		=		tmp_path / 'extracted'
    TarExtractor.extract(lowerCamelCase_							, lowerCamelCase_ )
    assert caplog.text
    for record in caplog.records:
        assert record.levelname == "ERROR"
        assert error_log in record.msg



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Union[str, Any]:
    # We should have less false positives than zipfile.is_zipfile
    # We do that by checking only the magic number
    _lowercase       :							List[Any]      		=		tmpdir / 'not_a_zip_file'
    # From: https://github.com/python/cpython/pull/5053
    _lowercase       :							Union[str, Any]      		=		(
        B'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
        B'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
        B'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
        B'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
    )
    with not_a_zip_file.open('wb' ) as f:
        f.write(lowerCamelCase_ )
    assert zipfile.is_zipfile(str(lowerCamelCase_ ) )  # is a false positive for `zipfile`
    assert not ZipExtractor.is_extractable(lowerCamelCase_ )  # but we're right










"""simple docstring"""





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class      a						(					nn.Module		):






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						list(model.children()					)[:-2]
      lowerCamelCase_         =						nn.Sequential(*__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						[json.loads(__SCREAMING_SNAKE_CASE					) for l in open(__SCREAMING_SNAKE_CASE					)]
      lowerCamelCase_         =						os.path.dirname(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						labels
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						max_seq_length

      lowerCamelCase_         =						transforms






    def __len__(       self     :							Any					)     ->		Any:
      return len(self.data					)






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

      lowerCamelCase_         =						torch.zeros(self.n_classes					)
      lowerCamelCase_         =						1

      lowerCamelCase_         =						Image.open(os.path.join(self.data_dir       ,						self.data[index]['img']					)					).convert('RGB'					)
      lowerCamelCase_         =						self.transforms(__SCREAMING_SNAKE_CASE					)

      return {
          "image_start_token": start_token,
          "image_end_token": end_token,
          "sentence": sentence,
          "image": image,
          "label": label,
      }






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,				_lowerCamelCase ) ):
    lowerCamelCase_         =						input_row['sentence']
    lowerCamelCase_         =						1

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_end_token'] for row in batch] )

  return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def      lowerCamelCase__       (							)      ->				List[str]:
  return [
      "Crime",
      "Drama",
      "Thriller",
      "Action",
      "Comedy",
      "Romance",
      "Documentary",
      "Short",
      "Mystery",
      "History",
      "Family",
      "Adventure",
      "Fantasy",
      "Sci-Fi",
      "Western",
      "Horror",
      "Sport",
      "War",
      "Music",
      "Musical",
      "Animation",
      "Biography",
      "Film-Noir",
  ]







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  return transforms.Compose(
      [
          transforms.Resize(256 ),
          transforms.CenterCrop(224 ),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,				),
      ] )




