



"""simple docstring"""

from typing import Dict, Iterable, 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, logging


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


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= ['''pixel_values''']





	def __init__(      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int] = None     ,       _SCREAMING_SNAKE_CASE				:       PILImageResampling = PILImageResampling.BICUBIC     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Union[int, float] = 1 / 255     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,       **_SCREAMING_SNAKE_CASE				:       Any     ,       )						->	None:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE						:							List[str]							=     do_resize
					SCREAMING_SNAKE_CASE						:							Any							=     size
					SCREAMING_SNAKE_CASE						:							List[Any]							=     resample
					SCREAMING_SNAKE_CASE						:							Dict							=     do_center_crop
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     crop_size
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     do_rescale
					SCREAMING_SNAKE_CASE						:							int							=     rescale_factor
					SCREAMING_SNAKE_CASE						:							Any							=     do_normalize
					SCREAMING_SNAKE_CASE						:							Any							=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image_std if image_std is not None else IMAGENET_DEFAULT_STD





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
					if "shortest_edge" in size:
									SCREAMING_SNAKE_CASE						:							Tuple							=     int((256 / 224) * size['shortest_edge']    )
									SCREAMING_SNAKE_CASE						:							Dict							=     get_resize_output_image_size(_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     {'height': output_size[0], 'width': output_size[1]}
					if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
					return resize(
					    _SCREAMING_SNAKE_CASE     ,       size=(size_dict['height'], size_dict['width'])     ,       resample=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       )						->	np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     get_size_dict(_SCREAMING_SNAKE_CASE    )
					if "height" not in size or "width" not in size:
									raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
					return center_crop(_SCREAMING_SNAKE_CASE     ,       size=(size['height'], size['width'])     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


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





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





					"""simple docstring"""


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







	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       ImageInput     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Dict[str, int]] = None     ,       _SCREAMING_SNAKE_CASE				:       PILImageResampling = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Dict[str, int]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[float] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, Iterable[float]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[float, Iterable[float]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[TensorType] = None     ,       _SCREAMING_SNAKE_CASE				:       ChannelDimension = ChannelDimension.FIRST     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       )						->	BatchFeature:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE						:							int							=     size if size is not None else self.size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     crop_size if crop_size is not None else self.crop_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       param_name='crop_size'    )

					SCREAMING_SNAKE_CASE						:							Dict							=     make_list_of_images(_SCREAMING_SNAKE_CASE    )

					if not valid_images(_SCREAMING_SNAKE_CASE    ):
									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.
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [to_numpy_array(_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_resize:
									SCREAMING_SNAKE_CASE						:							List[str]							=     [self.resize(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					if do_center_crop:
									SCREAMING_SNAKE_CASE						:							Tuple							=     [self.center_crop(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE						:							List[str]							=     [self.rescale(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					if do_normalize:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     [self.normalize(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					SCREAMING_SNAKE_CASE						:							Any							=     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					SCREAMING_SNAKE_CASE						:							List[Any]							=     {'pixel_values': images}
					return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,       tensor_type=_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard
def  __snake_case							(	__A      :						str     )     ->       Dict:






				'''simple docstring'''
				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Union[str, Any]:






				'''simple docstring'''
				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     'mock-s3-bucket'
				SCREAMING_SNAKE_CASE						:							Tuple							=     F"""s3://{mock_bucket}"""
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path.startswith('s3://'     ) is False

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     './local/path'
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path == new_dataset_path
def  __snake_case							(	__A      :						Tuple     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     is_remote_filesystem(__A     )
				assert is_remote is True

				SCREAMING_SNAKE_CASE						:							Dict							=     fsspec.filesystem('file'     )

				SCREAMING_SNAKE_CASE						:							Dict							=     is_remote_filesystem(__A     )
				assert is_remote is False
@pytest.mark.parametrize('compression_fs_class'							,		__A     )
def  __snake_case							(	__A      :						Any							,		__A      :						Optional[int]							,		__A      :						List[Any]							,		__A      :						Optional[Any]							,		__A      :						str							,		__A      :						Tuple							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
				SCREAMING_SNAKE_CASE						:							List[Any]							=     input_paths[compression_fs_class.protocol]
				if input_path is None:
								SCREAMING_SNAKE_CASE						:							str							=     F"""for '{compression_fs_class.protocol}' compression protocol, """
								if compression_fs_class.protocol == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_fs_class.protocol == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(__A     )
				SCREAMING_SNAKE_CASE						:							Any							=     fsspec.filesystem(compression_fs_class.protocol							,		fo=__A     )
				assert isinstance(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     os.path.basename(__A     )
				SCREAMING_SNAKE_CASE						:							Dict							=     expected_filename[: expected_filename.rindex('.'     )]
				assert fs.glob('*'     ) == [expected_filename]
				with fs.open(__A							,		'r'							,		encoding='utf-8'     ) as f, open(__A							,		encoding='utf-8'     ) as expected_file:
								assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol'							,		['zip', 'gzip']     )
def  __snake_case							(	__A      :						Any							,		__A      :						int							,		__A      :						Union[str, Any]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[Any]							=     {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     compressed_file_paths[protocol]
				SCREAMING_SNAKE_CASE						:							str							=     'dataset.jsonl'
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     F"""{protocol}://{member_file_path}::{compressed_file_path}"""
				SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						:							Any							=     fsspec.get_fs_token_paths(__A     )
				assert fs.isfile(__A     )
				assert not fs.isfile('non_existing_' + member_file_path     )
@pytest.mark.integration
def  __snake_case							(	__A      :						List[str]							,		__A      :						int							,		__A      :						int							,		__A      :						Optional[int]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     hf_api.dataset_info(__A							,		token=__A     )
				SCREAMING_SNAKE_CASE						:							str							=     HfFileSystem(repo_info=__A							,		token=__A     )
				assert sorted(hffs.glob('*'     )     ) == [".gitattributes", "data"]
				assert hffs.isdir('data'     )
				assert hffs.isfile('.gitattributes'     ) and hffs.isfile('data/text_data.txt'     )
				with open(__A     ) as f:
								assert hffs.open('data/text_data.txt'							,		'r'     ).read() == f.read()







def  __snake_case							(	)     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     'bz2'

				# Import module
				import datasets.filesystems

				# Overwrite protocol and reload
				register_implementation(__A							,		__A							,		clobber=__A     )
				with pytest.warns(__A     ) as warning_info:
								importlib.reload(datasets.filesystems     )

				assert len(__A     ) == 1
				assert (
				    str(warning_info[0].message     )
				    == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
				)





