


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    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    )	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{
          '''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),
      }
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		input_paths_and_base_extractors[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		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(A__							)
      assert base_extractor.is_extractable(A__							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
      base_extractor.extract(A__      ,    A__							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		file_path.read_text(encoding='''utf-8'''							)
      else:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		output_path.read_text(encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		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    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    )	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, 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,
      }
      SCREAMING_SNAKE_CASE__       :  int						     =		input_paths[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE__       :  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(A__							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		Extractor.infer_extractor_format(A__							)
      assert extractor_format is not None
      SCREAMING_SNAKE_CASE__       :  Any						     =		tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
      Extractor.extract(A__      ,    A__      ,    A__							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		file_path.read_text(encoding='''utf-8'''							)
      else:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		output_path.read_text(encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		text_file.read_text(encoding='''utf-8'''							)
      assert extracted_file_content == expected_file_content




@pytest.fixture
def    a   (							A__      ,    A__							)	-> str:




      '''simple docstring'''

      import tarfile

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tmp_path / '''data_dot_dot'''
      directory.mkdir()
      SCREAMING_SNAKE_CASE__       :  str						     =		directory / '''tar_file_with_dot_dot.tar'''
      with tarfile.TarFile(A__      ,    '''w'''							) as f:
            f.add(A__      ,    arcname=os.path.join('''..'''      ,    text_file.name							)							)
      return path




@pytest.fixture
def    a   (							A__							)	-> Union[str, Any]:




      '''simple docstring'''

      import tarfile

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tmp_path / '''data_sym_link'''
      directory.mkdir()
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		directory / '''tar_file_with_sym_link.tar'''
      os.symlink('''..'''      ,    directory / '''subdir'''      ,    target_is_directory=A__							)
      with tarfile.TarFile(A__      ,    '''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    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__							)	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		{
          '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
          '''tar_file_with_sym_link''': tar_file_with_sym_link,
      }
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		insecure_tar_files[insecure_tar_file]
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		tmp_path / '''extracted'''
      TarExtractor.extract(A__      ,    A__							)
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg





def    a   (							A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		tmpdir / '''not_a_zip_file'''
      # From: https://github.com/python/cpython/pull/5053
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		(
          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(A__							)
      assert zipfile.is_zipfile(str(A__							)							)  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(A__							)  # but we're right











from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[List[ImageInput]]:
				if isinstance(__UpperCamelCase	,				(list, tuple) ) and isinstance(videos[0]	,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(__UpperCamelCase	,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(__UpperCamelCase ):
								return [[videos]]

				raise ValueError(F'Could not make batched video from {videos}' )






class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:List[str] =       ["pixel_values"]



							def __init__(							self ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = 1 / 255 ,					__UpperCAmelCase = True ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__(**__UpperCAmelCase				)
											a__	:							int      						= size if size is not None else {"shortest_edge": 256}
											a__	:							str      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= crop_size if crop_size is not None else {"height": 224, "width": 224}
											a__	:							int      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											a__	:							str      						= do_resize
											a__	:							Union[str, Any]      						= size
											a__	:							Any      						= do_center_crop
											a__	:							Optional[int]      						= crop_size
											a__	:							Optional[int]      						= resample
											a__	:							Optional[Any]      						= do_rescale
											a__	:							Union[str, Any]      						= rescale_factor
											a__	:							Dict      						= offset
											a__	:							List[str]      						= do_normalize
											a__	:							Dict      						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__	:							Tuple      						= image_std if image_std is not None else IMAGENET_STANDARD_STD



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											if "shortest_edge" in size:
															a__	:							Optional[Any]      						= get_resize_output_image_size(__UpperCAmelCase ,					size["shortest_edge"] ,					default_to_square=__UpperCAmelCase				)
											elif "height" in size and "width" in size:
															a__	:							Union[str, Any]      						= (size["height"], size["width"])
											else:
															raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'				)
											return resize(__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= get_size_dict(__UpperCAmelCase				)
											if "height" not in size or "width" not in size:
															raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'				)
											return center_crop(__UpperCAmelCase ,					size=(size["height"], size["width"]) ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= image.astype(np.floataa				)
											if offset:
															a__	:							Dict      						= image - (scale / 2)
											return rescale(__UpperCAmelCase ,					scale=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											return normalize(__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					):







											"""simple docstring"""



											if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."				)

											if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."				)

											if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."				)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True."				)

											if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."				)

											# All transformations expect numpy arrays.
											a__	:							int      						= to_numpy_array(__UpperCAmelCase				)

											if do_resize:
															a__	:							List[Any]      						= self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase				)

											if do_center_crop:
															a__	:							str      						= self.center_crop(__UpperCAmelCase ,					size=__UpperCAmelCase				)

											if do_rescale:
															a__	:							Optional[Any]      						= self.rescale(image=__UpperCAmelCase ,					scale=__UpperCAmelCase ,					offset=__UpperCAmelCase				)

											if do_normalize:
															a__	:							Union[str, Any]      						= self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase				)

											a__	:							Optional[int]      						= to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase				)
											return image




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							List[Any]      						= do_resize if do_resize is not None else self.do_resize
											a__	:							Dict      						= resample if resample is not None else self.resample
											a__	:							Tuple      						= do_center_crop if do_center_crop is not None else self.do_center_crop
											a__	:							Optional[Any]      						= do_rescale if do_rescale is not None else self.do_rescale
											a__	:							Optional[int]      						= rescale_factor if rescale_factor is not None else self.rescale_factor
											a__	:							List[Any]      						= offset if offset is not None else self.offset
											a__	:							Optional[Any]      						= do_normalize if do_normalize is not None else self.do_normalize
											a__	:							int      						= image_mean if image_mean is not None else self.image_mean
											a__	:							List[Any]      						= image_std if image_std is not None else self.image_std

											a__	:							int      						= size if size is not None else self.size
											a__	:							Tuple      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							List[Any]      						= crop_size if crop_size is not None else self.crop_size
											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											if not valid_images(__UpperCAmelCase				):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."				)

											a__	:							Union[str, Any]      						= make_batched(__UpperCAmelCase				)

											a__	:							Optional[Any]      						= [
											    [
											        self._preprocess_image(
											            image=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					do_center_crop=__UpperCAmelCase ,					crop_size=__UpperCAmelCase ,					do_rescale=__UpperCAmelCase ,					rescale_factor=__UpperCAmelCase ,					offset=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					image_mean=__UpperCAmelCase ,					image_std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					)
											        for img in video
											    ]
											    for video in videos
											]

											a__	:							Any      						= {"pixel_values": videos}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase				)


