





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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      )     ->	int:
    lowerCamelCase__       : 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),
    }
    lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						input_paths_and_base_extractors[compression_format]
    if input_path is None:
        lowerCamelCase__       : Dict   					=						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					)
    lowerCamelCase__       : Any   					=						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
            lowerCamelCase__       : Tuple   					=						file_path.read_text(encoding='utf-8'					)
    else:
        lowerCamelCase__       : Optional[Any]   					=						output_path.read_text(encoding='utf-8'					)
    lowerCamelCase__       : Optional[int]   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      )     ->	List[str]:
    lowerCamelCase__       : int   					=						{
        '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,
    }
    lowerCamelCase__       : Tuple   					=						input_paths[compression_format]
    if input_path is None:
        lowerCamelCase__       : str   					=						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					)
    lowerCamelCase__       : List[Any]   					=						Extractor.infer_extractor_format(_UpperCAmelCase					)
    assert extractor_format is not None
    lowerCamelCase__       : List[str]   					=						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
            lowerCamelCase__       : Optional[Any]   					=						file_path.read_text(encoding='utf-8'					)
    else:
        lowerCamelCase__       : List[Any]   					=						output_path.read_text(encoding='utf-8'					)
    lowerCamelCase__       : Optional[Any]   					=						text_file.read_text(encoding='utf-8'					)
    assert extracted_file_content == expected_file_content


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    import tarfile

    lowerCamelCase__       : Tuple   					=						tmp_path / 'data_dot_dot'
    directory.mkdir()
    lowerCamelCase__       : Optional[Any]   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    import tarfile

    lowerCamelCase__       : Union[str, Any]   					=						tmp_path / 'data_sym_link'
    directory.mkdir()
    lowerCamelCase__       : int   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Any:
    lowerCamelCase__       : Any   					=						{
        'tar_file_with_dot_dot': tar_file_with_dot_dot,
        'tar_file_with_sym_link': tar_file_with_sym_link,
    }
    lowerCamelCase__       : str   					=						insecure_tar_files[insecure_tar_file]
    lowerCamelCase__       : Optional[int]   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	List[Any]:
    # We should have less false positives than zipfile.is_zipfile
    # We do that by checking only the magic number
    lowerCamelCase__       : Tuple   					=						tmpdir / 'not_a_zip_file'
    # From: https://github.com/python/cpython/pull/5053
    lowerCamelCase__       : 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(_UpperCAmelCase					)
    assert zipfile.is_zipfile(str(_UpperCAmelCase					)					)  # is a false positive for `zipfile`
    assert not ZipExtractor.is_extractable(_UpperCAmelCase					)  # but we're right












from typing import Dict, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = 0.9     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : int   					=						size if size is not None else {'shortest_edge': 224}
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__       : str   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Tuple   					=						do_resize
     lowerCamelCase__       : str   					=						size
     lowerCamelCase__       : List[str]   					=						crop_pct
     lowerCamelCase__       : Any   					=						resample
     lowerCamelCase__       : Tuple   					=						do_center_crop
     lowerCamelCase__       : Any   					=						crop_size
     lowerCamelCase__       : Optional[int]   					=						do_rescale
     lowerCamelCase__       : Optional[Any]   					=						rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     lowerCamelCase__       : Optional[Any]   					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[float] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Any   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
     if crop_pct is not None:
         if "shortest_edge" in size:
             lowerCamelCase__       : List[Any]   					=						int(size['shortest_edge'] / crop_pct      )
         elif "height" in size and "width" in size:
             if size["height"] == size["width"]:
                 lowerCamelCase__       : int   					=						int(size['height'] / crop_pct      )
             else:
                 lowerCamelCase__       : Any   					=						(int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

         lowerCamelCase__       : Union[str, Any]   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     else:
         if "shortest_edge" in size:
             lowerCamelCase__       : int   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=size['shortest_edge']     ,	default_to_square=UpperCAmelCase      )
         elif "height" in size and "width" in size:
             lowerCamelCase__       : List[Any]   					=						(size['height'], size['width'])
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Union[str, Any]   					=						get_size_dict(UpperCAmelCase      )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""      )
     return center_crop(UpperCAmelCase     ,	size=(size['height'], size['width'])     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      int:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )







 def 	A_      ( self    : str     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Any     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : Optional[Any]   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Any   					=						crop_pct if crop_pct is not None else self.crop_pct
     lowerCamelCase__       : Any   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Any   					=						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : str   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__       : int   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Any   					=						make_list_of_images(UpperCAmelCase      )

     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.'      )

     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_pct is None:
         raise ValueError('Crop_pct 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.'      )

     # All transformations expect numpy arrays.
     lowerCamelCase__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Any   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	crop_pct=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_center_crop:
         lowerCamelCase__       : Optional[Any]   					=						[self.center_crop(image=UpperCAmelCase     ,	size=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : int   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

     if do_normalize:
         lowerCamelCase__       : List[Any]   					=						[self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )





