"""simple docstring"""
import os
from typing import Optional

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



class      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Union[str, Any]	     =  """"""
				_lowerCAmelCase   :			Tuple	     =  (
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				_lowerCAmelCase   :			Optional[Any]	     =  None  # compression type in fsspec. ex: "gzip"
				_lowerCAmelCase   :			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						,			**__UpperCamelCase			)
										# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
										snake_case	    =	fsspec.open(
										    __UpperCamelCase						,			mode='rb'						,			protocol=__UpperCamelCase						,			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 {})						,			)
										snake_case	    =	os.path.basename(self.file.path.split('::'			)[0]			)
										snake_case	    =	(
										    self.compressed_name[: self.compressed_name.rindex('.'			)]
										    if '.' in self.compressed_name
										    else self.compressed_name
										)
										snake_case	    =	None

				@classmethod
				def 						snake_case       ( cls						,			lowerCAmelCase			):






										"""simple docstring"""

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

				def 						snake_case       ( self			):






										"""simple docstring"""

										if self.dir_cache is None:
																snake_case	    =	{**self.file.fs.info(self.file.path			), 'name': self.uncompressed_name}
																snake_case	    =	{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"""

										snake_case	    =	self._strip_protocol(__UpperCamelCase			)
										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      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Any	     =  """bz2"""
				_lowerCAmelCase   :			Tuple	     =  """bz2"""
				_lowerCAmelCase   :			List[str]	     =  """.bz2"""





class      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Any	     =  """gzip"""
				_lowerCAmelCase   :			int	     =  """gzip"""
				_lowerCAmelCase   :			Optional[Any]	     =  """.gz"""





class      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Union[str, Any]	     =  """lz4"""
				_lowerCAmelCase   :			List[Any]	     =  """lz4"""
				_lowerCAmelCase   :			Optional[Any]	     =  """.lz4"""





class      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Optional[Any]	     =  """xz"""
				_lowerCAmelCase   :			int	     =  """xz"""
				_lowerCAmelCase   :			Union[str, Any]	     =  """.xz"""





class      lowerCAmelCase_			(	lowercase_    ):



				"""simple docstring"""
				_lowerCAmelCase   :			List[Any]	     =  """zstd"""
				_lowerCAmelCase   :			Dict	     =  """zstd"""
				_lowerCAmelCase   :			Union[str, Any]	     =  """.zst"""

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






										"""simple docstring"""

										super().__init__(
										    fo=__UpperCamelCase						,			mode=__UpperCamelCase						,			target_protocol=__UpperCamelCase						,			target_options=__UpperCamelCase						,			block_size=__UpperCamelCase						,			**__UpperCamelCase						,			)
										# 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
										snake_case	    =	self.file.__enter__



										class      lowerCAmelCase_			:



														"""simple docstring"""

														def __init__( self						,			lowerCAmelCase			):






																				"""simple docstring"""

																				snake_case	    =	file_

														def __enter__( self			):






																				"""simple docstring"""

																				self._file.__enter__()
																				return self

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






																				"""simple docstring"""

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

														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						,			__UpperCamelCase			)

										def fixed_enter(*lowerCAmelCase						,			**lowerCAmelCase			):
																return WrappedFile(_enter(*__UpperCamelCase						,			**__UpperCamelCase			)			)

										snake_case	    =	fixed_enter



"""simple docstring"""







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


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 0.9     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	size if size is not None else {'shortest_edge': 224}
        lowercase_	:				Union[str, Any]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase_	:				Optional[int]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				List[str]													=	do_resize
        lowercase_	:				List[Any]													=	size
        lowercase_	:				int													=	crop_pct
        lowercase_	:				Dict													=	resample
        lowercase_	:				List[str]													=	do_center_crop
        lowercase_	:				Union[str, Any]													=	crop_size
        lowercase_	:				List[Any]													=	do_rescale
        lowercase_	:				Tuple													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Union[str, Any]													=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_	:				int													=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				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:
                  lowercase_	:				Union[str, Any]													=	int(size['shortest_edge'] / crop_pct			)
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_	:				Tuple													=	int(size['height'] / crop_pct			)
                  else:
                       lowercase_	:				Dict													=	(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        else:
             if "shortest_edge" in size:
                  lowercase_	:				Optional[int]													=	get_resize_output_image_size(__UpperCamelCase     ,size=size['shortest_edge']     ,default_to_square=__UpperCamelCase			)
             elif "height" in size and "width" in size:
                  lowercase_	:				Dict													=	(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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      str:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


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







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


        '''simple docstring'''


        lowercase_	:				List[Any]													=	do_resize if do_resize is not None else self.do_resize
        lowercase_	:				Optional[int]													=	crop_pct if crop_pct is not None else self.crop_pct
        lowercase_	:				List[str]													=	resample if resample is not None else self.resample
        lowercase_	:				str													=	do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_	:				Tuple													=	do_rescale if do_rescale is not None else self.do_rescale
        lowercase_	:				Union[str, Any]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_	:				str													=	do_normalize if do_normalize is not None else self.do_normalize
        lowercase_	:				str													=	image_mean if image_mean is not None else self.image_mean
        lowercase_	:				Tuple													=	image_std if image_std is not None else self.image_std

        lowercase_	:				Optional[Any]													=	size if size is not None else self.size
        lowercase_	:				Tuple													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else self.crop_size
        lowercase_	:				List[str]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				str													=	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.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

        if do_resize:
             lowercase_	:				str													=	[self.resize(image=__UpperCamelCase     ,size=__UpperCamelCase     ,crop_pct=__UpperCamelCase     ,resample=__UpperCamelCase			) for image in images]

        if do_center_crop:
             lowercase_	:				str													=	[self.center_crop(image=__UpperCamelCase     ,size=__UpperCamelCase			) for image in images]

        if do_rescale:
             lowercase_	:				Any													=	[self.rescale(image=__UpperCamelCase     ,scale=__UpperCamelCase			) for image in images]

        if do_normalize:
             lowercase_	:				int													=	[self.normalize(image=__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase			) for image in images]

        lowercase_	:				Dict													=	[to_channel_dimension_format(__UpperCamelCase     ,__UpperCamelCase			) for image in images]

        lowercase_	:				Any													=	{'pixel_values': images}
        return BatchFeature(data=__UpperCamelCase     ,tensor_type=__UpperCamelCase			)

