





import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Dict		      =     ""
				SCREAMING_SNAKE_CASE__     :   str		      =     (
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				SCREAMING_SNAKE_CASE__     :   str		      =     None  # compression type in fsspec. ex: "gzip"
				SCREAMING_SNAKE_CASE__     :   str		      =     None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






				def __init__(  self			, _lowerCAmelCase = ""			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(self			, **_lowerCAmelCase					)
										# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
										__SCREAMING_SNAKE_CASE:				int           =   fsspec.open(
										    _lowerCAmelCase			, mode='''rb'''			, protocol=_lowerCAmelCase			, compression=self.compression			, client_kwargs={
										        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
										        '''trust_env''': True,  # Enable reading proxy env variables.
										        **(target_options or {}).pop('''client_kwargs'''			, {}					),  # To avoid issues if it was already passed.
										    }			, **(target_options or {})			, )
										__SCREAMING_SNAKE_CASE:				int           =   os.path.basename(self.file.path.split('''::'''					)[0]					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   (
										    self.compressed_name[: self.compressed_name.rindex('''.'''					)]
										    if '''.''' in self.compressed_name
										    else self.compressed_name
										)
										__SCREAMING_SNAKE_CASE:				str           =   None






				@classmethod
				def 			snake_case_    (  cls			, _lowerCAmelCase					):

										"""simple docstring"""




										return super()._strip_protocol(_lowerCAmelCase					).lstrip('''/'''					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.dir_cache is None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   {**self.file.fs.info(self.file.path					), '''name''': self.uncompressed_name}
																__SCREAMING_SNAKE_CASE:				str           =   {f['''name''']: f}






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self.file.open().read()






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = "rb"			, _lowerCAmelCase=None			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self._strip_protocol(_lowerCAmelCase					)
										if mode != "rb":
																raise ValueError(f"""Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'"""					)
										return self.file.open()





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   int		      =     "bz2"
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     "bz2"
				SCREAMING_SNAKE_CASE__     :   Any		      =     ".bz2"





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Dict		      =     "gzip"
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     "gzip"
				SCREAMING_SNAKE_CASE__     :   int		      =     ".gz"





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     "lz4"
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     "lz4"
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     ".lz4"





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     "xz"
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     "xz"
				SCREAMING_SNAKE_CASE__     :   str		      =     ".xz"





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     "zstd"
				SCREAMING_SNAKE_CASE__     :   str		      =     "zstd"
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     ".zst"






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase = "rb"			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = DEFAULT_BLOCK_SIZE			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    fo=_lowerCAmelCase			, mode=_lowerCAmelCase			, target_protocol=_lowerCAmelCase			, target_options=_lowerCAmelCase			, block_size=_lowerCAmelCase			, **_lowerCAmelCase			, )
										# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
										#
										# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
										#     out.close = close
										# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
										#
										# see https://github.com/intake/filesystem_spec/issues/725
										__SCREAMING_SNAKE_CASE:				str           =   self.file.__enter__





										class 				a			:






														def __init__(  self			, _lowerCAmelCase					):

																				"""simple docstring"""




																				__SCREAMING_SNAKE_CASE:				List[str]           =   file_






														def __enter__(  self					):

																				"""simple docstring"""




																				self._file.__enter__()
																				return self






														def __exit__(  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

																				"""simple docstring"""




																				self._file.__exit__(*_lowerCAmelCase			, **_lowerCAmelCase					)






														def __iter__(  self					):

																				"""simple docstring"""




																				return iter(self._file					)






														def 			snake_case_    (  self					):

																				"""simple docstring"""




																				return next(self._file					)






														def __getattr__(  self			, _lowerCAmelCase					):

																				"""simple docstring"""




																				return getattr(self._file			, _lowerCAmelCase					)

										def fixed_enter(*_lowerCAmelCase			, **_lowerCAmelCase					):
																return WrappedFile(_enter(*_lowerCAmelCase			, **_lowerCAmelCase					)					)

										__SCREAMING_SNAKE_CASE:				str           =   fixed_enter





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, 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__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			str						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = PIL.Image.BICUBIC							,				a = True							,				a = None							,				a = 1 / 2_55							,				a = True							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""height""": 2_56, """width""": 2_56}
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

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

						def 			A_					(      self							,				a							,				a							,				a = PIL.Image.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return resize(
								    a							,				size=(size["""height"""], size["""width"""])							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return center_crop(a							,				size=(size["""height"""], size["""width"""])							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   List[str]:


								'''simple docstring'''

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

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

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







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a=None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

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

								_UpperCamelCase         =						size if size is not None else self.size
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else self.crop_size
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

								_UpperCamelCase         =						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 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."""					)

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

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

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

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

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

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

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



