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 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Dict ,	):
					SCREAMING_SNAKE_CASE           =    {
					    "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           =    input_paths_and_base_extractors[compression_format]
					if input_path is None:
										SCREAMING_SNAKE_CASE           =    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__					)
					SCREAMING_SNAKE_CASE           =    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
															SCREAMING_SNAKE_CASE           =    file_path.read_text(encoding="utf-8"					)
					else:
										SCREAMING_SNAKE_CASE           =    output_path.read_text(encoding="utf-8"					)
					SCREAMING_SNAKE_CASE           =    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 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict ,	):
					SCREAMING_SNAKE_CASE           =    {
					    "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           =    input_paths[compression_format]
					if input_path is None:
										SCREAMING_SNAKE_CASE           =    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__					)
					SCREAMING_SNAKE_CASE           =    Extractor.infer_extractor_format(UpperCAmelCase__					)
					assert extractor_format is not None
					SCREAMING_SNAKE_CASE           =    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
															SCREAMING_SNAKE_CASE           =    file_path.read_text(encoding="utf-8"					)
					else:
										SCREAMING_SNAKE_CASE           =    output_path.read_text(encoding="utf-8"					)
					SCREAMING_SNAKE_CASE           =    text_file.read_text(encoding="utf-8"					)
					assert extracted_file_content == expected_file_content





@pytest.fixture
def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Dict					):
					import tarfile

					SCREAMING_SNAKE_CASE           =    tmp_path / "data_dot_dot"
					directory.mkdir()
					SCREAMING_SNAKE_CASE           =    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 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					import tarfile

					SCREAMING_SNAKE_CASE           =    tmp_path / "data_sym_link"
					directory.mkdir()
					SCREAMING_SNAKE_CASE           =    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 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    {
					    "tar_file_with_dot_dot": tar_file_with_dot_dot,
					    "tar_file_with_sym_link": tar_file_with_sym_link,
					}
					SCREAMING_SNAKE_CASE           =    insecure_tar_files[insecure_tar_file]
					SCREAMING_SNAKE_CASE           =    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 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					# We should have less false positives than zipfile.is_zipfile
					# We do that by checking only the magic number
					SCREAMING_SNAKE_CASE           =    tmpdir / "not_a_zip_file"
					# From: https://github.com/python/cpython/pull/5053
					SCREAMING_SNAKE_CASE           =    (
					    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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


_lowerCamelCase   :				List[Any]     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							["""pixel_values"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[int, float] = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BICUBIC		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE           =    int(shortest_edge / crop_pct )
																	SCREAMING_SNAKE_CASE           =    get_resize_output_image_size(_UpperCamelCase		,							size=_UpperCamelCase		,							default_to_square=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




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



												'''simple docstring'''

												return rescale(_UpperCamelCase		,							scale=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




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



												'''simple docstring'''

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





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = 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:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384." )

												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.
												SCREAMING_SNAKE_CASE           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							crop_pct=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =    [self.rescale(image=_UpperCamelCase		,							scale=_UpperCamelCase ) for image in images]

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =    [self.normalize(image=_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase		,							tensor_type=_UpperCamelCase )




