


'''simple docstring'''

import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Dict				   =    BertTokenizer
		A_     :      Tuple				   =    BertTokenizerFast
		A_     :      int				   =    True
		A_     :      Optional[int]				   =    True
		A_     :      Union[str, Any]				   =    filter_non_english







		def        __lowerCAmelCase		( self		:						int      )  ->			Optional[int]:
			super().setUp()

			__magic_name__	:   Optional[int]					     =					[
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    '[PAD]',
			    '[MASK]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			__magic_name__	:   Dict					     =					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']      )
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8'      ) as vocab_writer:
				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						Dict      )  ->			int:
			__magic_name__	:   Any					     =					'UNwant\u00E9d,running'
			__magic_name__	:   Any					     =					'unwanted, running'
			return input_text, output_text







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   Tuple					     =					self.tokenizer_class(self.vocab_file      )

			__magic_name__	:   Union[str, Any]					     =					tokenizer.tokenize('UNwant\u00E9d,running'      )
			self.assertListEqual(_A  ,					['un', '##want', '##ed', ',', 'runn', '##ing']      )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )  ,					[9, 6, 7, 12, 10, 11]      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Optional[int]:
			if not self.test_rust_tokenizer:
				return

			__magic_name__	:   Optional[Any]					     =					self.get_tokenizer()
			__magic_name__	:   int					     =					self.get_rust_tokenizer()

			__magic_name__	:   Optional[int]					     =					'UNwant\u00E9d,running'

			__magic_name__	:   str					     =					tokenizer.tokenize(_A      )
			__magic_name__	:   Tuple					     =					rust_tokenizer.tokenize(_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   Optional[Any]					     =					tokenizer.encode(_A  ,					add_special_tokens=_A      )
			__magic_name__	:   Optional[Any]					     =					rust_tokenizer.encode(_A  ,					add_special_tokens=_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   List[Any]					     =					self.get_rust_tokenizer()
			__magic_name__	:   List[Any]					     =					tokenizer.encode(_A      )
			__magic_name__	:   List[Any]					     =					rust_tokenizer.encode(_A      )
			self.assertListEqual(_A  ,					_A      )

			# With lower casing
			__magic_name__	:   List[str]					     =					self.get_tokenizer(do_lower_case=_A      )
			__magic_name__	:   str					     =					self.get_rust_tokenizer(do_lower_case=_A      )

			__magic_name__	:   Any					     =					'UNwant\u00E9d,running'

			__magic_name__	:   str					     =					tokenizer.tokenize(_A      )
			__magic_name__	:   Optional[Any]					     =					rust_tokenizer.tokenize(_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   Any					     =					tokenizer.encode(_A  ,					add_special_tokens=_A      )
			__magic_name__	:   int					     =					rust_tokenizer.encode(_A  ,					add_special_tokens=_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   Tuple					     =					self.get_rust_tokenizer()
			__magic_name__	:   Union[str, Any]					     =					tokenizer.encode(_A      )
			__magic_name__	:   Optional[Any]					     =					rust_tokenizer.encode(_A      )
			self.assertListEqual(_A  ,					_A      )







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			str:
			__magic_name__	:   Any					     =					BasicTokenizer()

			self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      )  ,					['ah', '\u535A', '\u63A8', 'zz']      )







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			str:
			__magic_name__	:   Any					     =					BasicTokenizer(do_lower_case=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )  ,					['hello', '!', 'how', 'are', 'you', '?']      )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,					['hello']      )







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			int:
			__magic_name__	:   List[str]					     =					BasicTokenizer(do_lower_case=_A  ,					strip_accents=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,					['hällo', '!', 'how', 'are', 'you', '?']      )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,					['h\u00E9llo']      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Tuple:
			__magic_name__	:   Any					     =					BasicTokenizer(do_lower_case=_A  ,					strip_accents=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,					['hallo', '!', 'how', 'are', 'you', '?']      )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,					['hello']      )







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Any:
			__magic_name__	:   Dict					     =					BasicTokenizer(do_lower_case=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,					['hallo', '!', 'how', 'are', 'you', '?']      )
			self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,					['hello']      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Union[str, Any]:
			__magic_name__	:   Any					     =					BasicTokenizer(do_lower_case=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )  ,					['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Union[str, Any]:
			__magic_name__	:   int					     =					BasicTokenizer(do_lower_case=_A  ,					strip_accents=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,					['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			str:
			__magic_name__	:   str					     =					BasicTokenizer(do_lower_case=_A  ,					strip_accents=_A      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,					['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			int:
			__magic_name__	:   List[str]					     =					BasicTokenizer(do_lower_case=_A  ,					never_split=['[UNK]']      )

			self.assertListEqual(
			    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      )  ,					['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			int:
			__magic_name__	:   List[Any]					     =					BasicTokenizer()
			__magic_name__	:   Dict					     =					'a\n\'ll !!to?\'d of, can\'t.'
			__magic_name__	:   Union[str, Any]					     =					['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
			self.assertListEqual(tokenizer.tokenize(_A      )  ,					_A      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Dict:
			__magic_name__	:   Optional[Any]					     =					['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

			__magic_name__	:   Dict					     =					{}
			for i, token in enumerate(_A      ):
				__magic_name__	:   str					     =					i
			__magic_name__	:   Tuple					     =					WordpieceTokenizer(vocab=_A  ,					unk_token='[UNK]'      )

			self.assertListEqual(tokenizer.tokenize(''      )  ,					[]      )

			self.assertListEqual(tokenizer.tokenize('unwanted running'      )  ,					['un', '##want', '##ed', 'runn', '##ing']      )

			self.assertListEqual(tokenizer.tokenize('unwantedX running'      )  ,					['[UNK]', 'runn', '##ing']      )







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[int]:
			self.assertTrue(_is_whitespace(' '      )      )
			self.assertTrue(_is_whitespace('\t'      )      )
			self.assertTrue(_is_whitespace('\r'      )      )
			self.assertTrue(_is_whitespace('\n'      )      )
			self.assertTrue(_is_whitespace('\u00A0'      )      )

			self.assertFalse(_is_whitespace('A'      )      )
			self.assertFalse(_is_whitespace('-'      )      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Tuple:
			self.assertTrue(_is_control('\u0005'      )      )

			self.assertFalse(_is_control('A'      )      )
			self.assertFalse(_is_control(' '      )      )
			self.assertFalse(_is_control('\t'      )      )
			self.assertFalse(_is_control('\r'      )      )







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			List[str]:
			self.assertTrue(_is_punctuation('-'      )      )
			self.assertTrue(_is_punctuation('$'      )      )
			self.assertTrue(_is_punctuation('`'      )      )
			self.assertTrue(_is_punctuation('.'      )      )

			self.assertFalse(_is_punctuation('A'      )      )
			self.assertFalse(_is_punctuation(' '      )      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Union[str, Any]:
			__magic_name__	:   List[Any]					     =					self.get_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_rust_tokenizer()

			# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
			self.assertListEqual([tokenizer.tokenize(_A      ) for t in ['Test', '\xad', 'test']]  ,					[['[UNK]'], [], ['[UNK]']]      )

			self.assertListEqual(
			    [rust_tokenizer.tokenize(_A      ) for t in ['Test', '\xad', 'test']]  ,					[['[UNK]'], [], ['[UNK]']]      )







		@slow
		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[Any]:
			__magic_name__	:   Dict					     =					self.tokenizer_class.from_pretrained('bert-base-uncased'      )

			__magic_name__	:   str					     =					tokenizer.encode('sequence builders'  ,					add_special_tokens=_A      )
			__magic_name__	:   Any					     =					tokenizer.encode('multi-sequence build'  ,					add_special_tokens=_A      )

			__magic_name__	:   int					     =					tokenizer.build_inputs_with_special_tokens(_A      )
			__magic_name__	:   List[str]					     =					tokenizer.build_inputs_with_special_tokens(_A  ,					_A      )

			assert encoded_sentence == [101] + text + [102]
			assert encoded_pair == [101] + text + [102] + text_a + [102]







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
					__magic_name__	:   Any					     =					self.rust_tokenizer_class.from_pretrained(_A  ,					**_A      )

					__magic_name__	:   str					     =					F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
					__magic_name__	:   int					     =					tokenizer_r.encode_plus(
					    _A  ,					return_attention_mask=_A  ,					return_token_type_ids=_A  ,					return_offsets_mapping=_A  ,					add_special_tokens=_A  ,					)

					__magic_name__	:   List[str]					     =					tokenizer_r.do_lower_case if hasattr(_A  ,					'do_lower_case'      ) else False
					__magic_name__	:   List[Any]					     =					(
					    [
					        ((0, 0), tokenizer_r.cls_token),
					        ((0, 1), 'A'),
					        ((1, 2), ','),
					        ((3, 5), 'na'),
					        ((5, 6), '##ï'),
					        ((6, 8), '##ve'),
					        ((9, 15), tokenizer_r.mask_token),
					        ((16, 21), 'Allen'),
					        ((21, 23), '##NL'),
					        ((23, 24), '##P'),
					        ((25, 33), 'sentence'),
					        ((33, 34), '.'),
					        ((0, 0), tokenizer_r.sep_token),
					    ]
					    if not do_lower_case
					    else [
					        ((0, 0), tokenizer_r.cls_token),
					        ((0, 1), 'a'),
					        ((1, 2), ','),
					        ((3, 8), 'naive'),
					        ((9, 15), tokenizer_r.mask_token),
					        ((16, 21), 'allen'),
					        ((21, 23), '##nl'),
					        ((23, 24), '##p'),
					        ((25, 33), 'sentence'),
					        ((33, 34), '.'),
					        ((0, 0), tokenizer_r.sep_token),
					    ]
					)

					self.assertEqual(
					    [e[1] for e in expected_results]  ,					tokenizer_r.convert_ids_to_tokens(tokens['input_ids']      )      )
					self.assertEqual([e[0] for e in expected_results]  ,					tokens['offset_mapping']      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
			__magic_name__	:   int					     =					['的', '人', '有']
			__magic_name__	:   Tuple					     =					''.join(_A      )
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
					__magic_name__	:   Optional[Any]					     =					True
					__magic_name__	:   Any					     =					self.tokenizer_class.from_pretrained(_A  ,					**_A      )
					__magic_name__	:   Optional[Any]					     =					self.rust_tokenizer_class.from_pretrained(_A  ,					**_A      )

					__magic_name__	:   Dict					     =					tokenizer_p.encode(_A  ,					add_special_tokens=_A      )
					__magic_name__	:   Optional[int]					     =					tokenizer_r.encode(_A  ,					add_special_tokens=_A      )

					__magic_name__	:   Any					     =					tokenizer_r.convert_ids_to_tokens(_A      )
					__magic_name__	:   str					     =					tokenizer_p.convert_ids_to_tokens(_A      )

					# it is expected that each Chinese character is not preceded by "##"
					self.assertListEqual(_A  ,					_A      )
					self.assertListEqual(_A  ,					_A      )

					__magic_name__	:   Tuple					     =					False
					__magic_name__	:   Optional[int]					     =					self.rust_tokenizer_class.from_pretrained(_A  ,					**_A      )
					__magic_name__	:   Dict					     =					self.tokenizer_class.from_pretrained(_A  ,					**_A      )

					__magic_name__	:   Dict					     =					tokenizer_r.encode(_A  ,					add_special_tokens=_A      )
					__magic_name__	:   Optional[int]					     =					tokenizer_p.encode(_A  ,					add_special_tokens=_A      )

					__magic_name__	:   Any					     =					tokenizer_r.convert_ids_to_tokens(_A      )
					__magic_name__	:   Union[str, Any]					     =					tokenizer_p.convert_ids_to_tokens(_A      )

					# it is expected that only the first Chinese character is not preceded by "##".
					__magic_name__	:   List[Any]					     =					[
					    F'##{token}' if idx != 0 else token for idx, token in enumerate(_A      )
					]
					self.assertListEqual(_A  ,					_A      )
					self.assertListEqual(_A  ,					_A      )



'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      int				   =    ["""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 / 255  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						Optional[Any]  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'shortest_edge': 256}
			__magic_name__	:   str					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   List[str]					     =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[Any]					     =					size
			__magic_name__	:   List[str]					     =					resample
			__magic_name__	:   Dict					     =					do_center_crop
			__magic_name__	:   List[str]					     =					crop_size
			__magic_name__	:   int					     =					do_rescale
			__magic_name__	:   Tuple					     =					rescale_factor
			__magic_name__	:   List[str]					     =					do_normalize
			__magic_name__	:   Union[str, Any]					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   Dict					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[str]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[Any]					     =					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()}'      )
			__magic_name__	:   Dict					     =					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        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Optional[int]  ,					)  ->			np.ndarray:
			__magic_name__	:   int					     =					get_size_dict(_A      )
			return center_crop(_A  ,					size=(size['height'], size['width'])  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						np.ndarray  ,					_A		:						float  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Tuple      )  ->			np.ndarray:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						np.ndarray  ,					_A		:						Union[float, List[float]]  ,					_A		:						Union[float, List[float]]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						int  ,					)  ->			np.ndarray:
			return normalize(_A  ,					mean=_A  ,					std=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_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]  ,					)  ->			List[str]:
			__magic_name__	:   int					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   Tuple					     =					size if size is not None else self.size
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[Any]					     =					do_center_crop if do_center_crop is not None else self.do_center_crop
			__magic_name__	:   Dict					     =					crop_size if crop_size is not None else self.crop_size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   Any					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   Tuple					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   Union[str, Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   int					     =					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.
			__magic_name__	:   List[Any]					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   Union[str, Any]					     =					[self.resize(image=_A  ,					size=_A  ,					resample=_A      ) for image in images]

			if do_center_crop:
				__magic_name__	:   Union[str, Any]					     =					[self.center_crop(image=_A  ,					size=_A      ) for image in images]

			if do_rescale:
				__magic_name__	:   List[Any]					     =					[self.rescale(image=_A  ,					scale=_A      ) for image in images]

			if do_normalize:
				__magic_name__	:   Optional[Any]					     =					[self.normalize(image=_A  ,					mean=_A  ,					std=_A      ) for image in images]

			__magic_name__	:   Union[str, Any]					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   List[str]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )