




'''simple docstring'''


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 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						str				,		UpperCamelCase :						int				,		UpperCamelCase :						Any				,		UpperCamelCase :						Any				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Any				,		UpperCamelCase :						Optional[int]				,		):





							'''simple docstring'''


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





							'''simple docstring'''


							_a			  =       {
							    '''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,
							}
							_a			  =       input_paths[compression_format]
							if input_path is None:
														_a			  =       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(UpperCamelCase   )
							_a			  =       Extractor.infer_extractor_format(UpperCamelCase   )
							assert extractor_format is not None
							_a			  =       tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
							Extractor.extract(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							if is_archive:
														assert output_path.is_dir()
														for file_path in output_path.iterdir():
																					assert file_path.name == text_file.name
																					_a			  =       file_path.read_text(encoding='''utf-8'''   )
							else:
														_a			  =       output_path.read_text(encoding='''utf-8'''   )
							_a			  =       text_file.read_text(encoding='''utf-8'''   )
							assert extracted_file_content == expected_file_content
@pytest.fixture
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							import tarfile

							_a			  =       tmp_path / '''data_dot_dot'''
							directory.mkdir()
							_a			  =       directory / '''tar_file_with_dot_dot.tar'''
							with tarfile.TarFile(UpperCamelCase				,		'''w'''   ) as f:
														f.add(UpperCamelCase				,		arcname=os.path.join('''..'''				,		text_file.name   )   )
							return path
@pytest.fixture
def 	snake_case_					(UpperCamelCase :						Optional[Any]   ):





							'''simple docstring'''


							import tarfile

							_a			  =       tmp_path / '''data_sym_link'''
							directory.mkdir()
							_a			  =       directory / '''tar_file_with_sym_link.tar'''
							os.symlink('''..'''				,		directory / '''subdir'''				,		target_is_directory=UpperCamelCase   )
							with tarfile.TarFile(UpperCamelCase				,		'''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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


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

def 	snake_case_					(UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							_a			  =       tmpdir / '''not_a_zip_file'''
							# From: https://github.com/python/cpython/pull/5053
							_a			  =       (
							    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(UpperCamelCase   )
							assert zipfile.is_zipfile(str(UpperCamelCase   )   )  # is a false positive for `zipfile`
							assert not ZipExtractor.is_extractable(UpperCamelCase   )  # but we're right







'''simple docstring'''


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

_snake_case   :   str     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							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					(	_a					):
							lowercase_           =       ['pixel_values']







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = PILImageResampling.BILINEAR							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Union[int, float] = 1 / 2_55							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    **lowerCAmelCase_ :						List[str]							,    )    ->							None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_      )
														_a			  =       size if size is not None else {'''shortest_edge''': 2_56}
														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														_a			  =       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a			  =       get_size_dict(lowerCAmelCase_							,    param_name='''crop_size'''      )

														_a			  =       do_resize
														_a			  =       size
														_a			  =       do_center_crop
														_a			  =       crop_size
														_a			  =       resample
														_a			  =       do_rescale
														_a			  =       rescale_factor
														_a			  =       offset
														_a			  =       do_normalize
														_a			  =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a			  =       image_std if image_std is not None else IMAGENET_STANDARD_STD







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Dict[str, int]							,    lowerCAmelCase_ :						PILImageResampling = PILImageResampling.BILINEAR							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							np.ndarray:


														"""simple docstring"""


														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														if "shortest_edge" in size:
																					_a			  =       get_resize_output_image_size(lowerCAmelCase_							,    size['''shortest_edge''']							,    default_to_square=lowerCAmelCase_      )
														elif "height" in size and "width" in size:
																					_a			  =       (size['''height'''], size['''width'''])
														else:
																					raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
														return resize(lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Dict[str, int]							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Tuple							,    )    ->							np.ndarray:


														"""simple docstring"""


														_a			  =       get_size_dict(lowerCAmelCase_      )
														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(lowerCAmelCase_							,    size=(size['''height'''], size['''width'''])							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Union[int, float]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Any							,    )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       image.astype(np.floataa      )
														if offset:
																					_a			  =       image - (scale / 2)
														return rescale(lowerCAmelCase_							,    scale=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Union[float, List[float]]							,    lowerCAmelCase_ :						Union[float, List[float]]							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							np.ndarray:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Tuple							,    lowerCAmelCase_ :						ImageInput							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						float = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[ChannelDimension] = ChannelDimension.FIRST							,    )    ->							np.ndarray:


														"""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			  =       to_numpy_array(lowerCAmelCase_      )

														if do_resize:
																					_a			  =       self.resize(image=lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_      )

														if do_center_crop:
																					_a			  =       self.center_crop(lowerCAmelCase_							,    size=lowerCAmelCase_      )

														if do_rescale:
																					_a			  =       self.rescale(image=lowerCAmelCase_							,    scale=lowerCAmelCase_							,    offset=lowerCAmelCase_      )

														if do_normalize:
																					_a			  =       self.normalize(image=lowerCAmelCase_							,    mean=lowerCAmelCase_							,    std=lowerCAmelCase_      )

														_a			  =       to_channel_dimension_format(lowerCAmelCase_							,    lowerCAmelCase_      )
														return image







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						ImageInput							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						float = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[str, TensorType]] = None							,    lowerCAmelCase_ :						ChannelDimension = ChannelDimension.FIRST							,    **lowerCAmelCase_ :						Any							,    )    ->							PIL.Image.Image:


														"""simple docstring"""


														_a			  =       do_resize if do_resize is not None else self.do_resize
														_a			  =       resample if resample is not None else self.resample
														_a			  =       do_center_crop if do_center_crop is not None else self.do_center_crop
														_a			  =       do_rescale if do_rescale is not None else self.do_rescale
														_a			  =       rescale_factor if rescale_factor is not None else self.rescale_factor
														_a			  =       offset if offset is not None else self.offset
														_a			  =       do_normalize if do_normalize is not None else self.do_normalize
														_a			  =       image_mean if image_mean is not None else self.image_mean
														_a			  =       image_std if image_std is not None else self.image_std

														_a			  =       size if size is not None else self.size
														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														_a			  =       crop_size if crop_size is not None else self.crop_size
														_a			  =       get_size_dict(lowerCAmelCase_							,    param_name='''crop_size'''      )

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

														_a			  =       make_batched(lowerCAmelCase_      )

														_a			  =       [
														    [
														        self._preprocess_image(
														            image=lowerCAmelCase_							,    do_resize=lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_							,    do_center_crop=lowerCAmelCase_							,    crop_size=lowerCAmelCase_							,    do_rescale=lowerCAmelCase_							,    rescale_factor=lowerCAmelCase_							,    offset=lowerCAmelCase_							,    do_normalize=lowerCAmelCase_							,    image_mean=lowerCAmelCase_							,    image_std=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    )
														        for img in video
														    ]
														    for video in videos
														]

														_a			  =       {'''pixel_values''': videos}
														return BatchFeature(data=lowerCAmelCase_							,    tensor_type=lowerCAmelCase_      )

