'''simple docstring'''




from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__UpperCAmelCase     							=logging.get_logger(__name__)




class     a__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     Tuple							=["pixel_values"]






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





         """simple docstring"""



         super().__init__(**a    )
         __lowerCamelCase		=  size if size is not None else {'''shortest_edge''': 2_56}
         __lowerCamelCase		=  get_size_dict(a  ,				default_to_square=a    )
         __lowerCamelCase		=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         __lowerCamelCase		=  get_size_dict(a  ,				param_name='''crop_size'''    )
         __lowerCamelCase		=  do_resize
         __lowerCamelCase		=  size
         __lowerCamelCase		=  resample
         __lowerCamelCase		=  do_center_crop
         __lowerCamelCase		=  crop_size
         __lowerCamelCase		=  do_rescale
         __lowerCamelCase		=  rescale_factor
         __lowerCamelCase		=  do_normalize
         __lowerCamelCase		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __lowerCamelCase		=  image_std if image_std is not None else IMAGENET_STANDARD_STD






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		np.ndarray  ,				a		:		Dict[str, int]  ,				a		:		PILImageResampling = PILImageResampling.BICUBIC  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		Dict  ,				):





         """simple docstring"""



         __lowerCamelCase		=  get_size_dict(a  ,				default_to_square=a    )
         if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
         __lowerCamelCase		=  get_resize_output_image_size(a  ,				size=size['''shortest_edge''']  ,				default_to_square=a    )
         return resize(a  ,				size=a  ,				resample=a  ,				data_format=a  ,				**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				a		:		np.ndarray  ,				a		:		Dict[str, int]  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		Dict  ,				):





         """simple docstring"""



         __lowerCamelCase		=  get_size_dict(a    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""    )
         return center_crop(a  ,				size=(size['''height'''], size['''width'''])  ,				data_format=a  ,				**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		np.ndarray  ,				a		:		float  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		Optional[int]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				a		:		np.ndarray  ,				a		:		Union[float, List[float]]  ,				a		:		Union[float, List[float]]  ,				a		:		Optional[Union[str, ChannelDimension]] = None  ,				**a		:		Optional[Any]  ,				):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		ImageInput  ,				a		:		Optional[bool] = None  ,				a		:		Dict[str, int] = None  ,				a		:		PILImageResampling = None  ,				a		:		bool = None  ,				a		:		Dict[str, int] = None  ,				a		:		Optional[bool] = None  ,				a		:		Optional[float] = None  ,				a		:		Optional[bool] = None  ,				a		:		Optional[Union[float, List[float]]] = None  ,				a		:		Optional[Union[float, List[float]]] = None  ,				a		:		Optional[Union[str, TensorType]] = None  ,				a		:		Union[str, ChannelDimension] = ChannelDimension.FIRST  ,				**a		:		List[Any]  ,				):





         """simple docstring"""



         __lowerCamelCase		=  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase		=  size if size is not None else self.size
         __lowerCamelCase		=  get_size_dict(a  ,				default_to_square=a    )
         __lowerCamelCase		=  resample if resample is not None else self.resample
         __lowerCamelCase		=  do_center_crop if do_center_crop is not None else self.do_center_crop
         __lowerCamelCase		=  crop_size if crop_size is not None else self.crop_size
         __lowerCamelCase		=  get_size_dict(a  ,				param_name='''crop_size'''    )
         __lowerCamelCase		=  do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase		=  rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase		=  do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase		=  image_mean if image_mean is not None else self.image_mean
         __lowerCamelCase		=  image_std if image_std is not None else self.image_std

         __lowerCamelCase		=  make_list_of_images(a    )

         if not valid_images(a    ):
             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:
             raise ValueError('''Size 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.'''    )

         # All transformations expect numpy arrays.
         __lowerCamelCase		=  [to_numpy_array(a    ) for image in images]

         if do_resize:
             __lowerCamelCase		=  [self.resize(image=a  ,				size=a  ,				resample=a    ) for image in images]

         if do_center_crop:
             __lowerCamelCase		=  [self.center_crop(image=a  ,				size=a    ) for image in images]

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

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

         __lowerCamelCase		=  [to_channel_dimension_format(a  ,				a    ) for image in images]

         __lowerCamelCase		=  {'''pixel_values''': images}
         return BatchFeature(data=a  ,				tensor_type=a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				a		:		Tuple  ,				a		:		List[Tuple] = None    ):





         """simple docstring"""



         __lowerCamelCase		=  outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
             if len(a    ) != len(a    ):
                 raise ValueError(
                     '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''    )

             if is_torch_tensor(a    ):
                 __lowerCamelCase		=  target_sizes.numpy()

             __lowerCamelCase		=  []

             for idx in range(len(a    )    ):
                 __lowerCamelCase		=  torch.nn.functional.interpolate(
                     logits[idx].unsqueeze(dim=0    )  ,				size=target_sizes[idx]  ,				mode='''bilinear'''  ,				align_corners=a    )
                 __lowerCamelCase		=  resized_logits[0].argmax(dim=0    )
                 semantic_segmentation.append(a    )
         else:
             __lowerCamelCase		=  logits.argmax(dim=1    )
             __lowerCamelCase		=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

         return semantic_segmentation


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						(							snake_case__:					int						,   snake_case__:					Tuple						,   snake_case__:					Dict						,   snake_case__:					Dict						,   snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					str						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[str]						,   snake_case__:					int						,   ):


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


				'''simple docstring'''
				lowercase_       =					{
				    '''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_       =					input_paths[compression_format]
				if input_path is None:
								lowercase_       =					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(snake_case__					)
				lowercase_       =					Extractor.infer_extractor_format(snake_case__					)
				assert extractor_format is not None
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				Extractor.extract(snake_case__						,   snake_case__						,   snake_case__					)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												lowercase_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					text_file.read_text(encoding='''utf-8'''					)
				assert extracted_file_content == expected_file_content




@pytest.fixture
def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_dot_dot'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_dot_dot.tar'''
				with tarfile.TarFile(snake_case__						,   '''w'''					) as f:
								f.add(snake_case__						,   arcname=os.path.join('''..'''						,   text_file.name					)					)
				return path




@pytest.fixture
def 				a						(							snake_case__:					int					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_sym_link'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_sym_link.tar'''
				os.symlink('''..'''						,   directory / '''subdir'''						,   target_is_directory=snake_case__					)
				with tarfile.TarFile(snake_case__						,   '''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						(							snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					List[str]						,   snake_case__:					int						,   snake_case__:					Optional[Any]					):


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




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				# We should have less false positives than zipfile.is_zipfile
				# We do that by checking only the magic number
				lowercase_       =					tmpdir / '''not_a_zip_file'''
				# From: https://github.com/python/cpython/pull/5053
				lowercase_       =					(
				    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(snake_case__					)
				assert zipfile.is_zipfile(str(snake_case__					)					)  # is a false positive for `zipfile`
				assert not ZipExtractor.is_extractable(snake_case__					)  # but we're right




