
import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _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 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Any     =			RoCBertTokenizer
			UpperCamelCase					:List[str]     =			None
			UpperCamelCase					:Dict     =			False
			UpperCamelCase					:Tuple     =			True
			UpperCamelCase					:Tuple     =			filter_non_english





			def        _snake_case    (self				):
						super().setUp()

						lowerCamelCase__ :      Tuple								=		["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
						lowerCamelCase__ :      Optional[Any]								=		{}
						lowerCamelCase__ :      Any								=		{}
						for i, value in enumerate(__lowerCAmelCase				):
									lowerCamelCase__ :      Optional[Any]								=		i
									lowerCamelCase__ :      str								=		i
						lowerCamelCase__ :      Any								=		os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES["""vocab_file"""]				)
						lowerCamelCase__ :      int								=		os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES["""word_shape_file"""]				)
						lowerCamelCase__ :      Optional[Any]								=		os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES["""word_pronunciation_file"""]				)
						with open(self.vocab_file							,    """w"""							,    encoding="""utf-8"""				) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)
						with open(self.word_shape_file							,    """w"""							,    encoding="""utf-8"""				) as word_shape_writer:
									json.dump(__lowerCAmelCase							,    __lowerCAmelCase							,    ensure_ascii=__lowerCAmelCase				)
						with open(self.word_pronunciation_file							,    """w"""							,    encoding="""utf-8"""				) as word_pronunciation_writer:
									json.dump(__lowerCAmelCase							,    __lowerCAmelCase							,    ensure_ascii=__lowerCAmelCase				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.tokenizer_class(self.vocab_file							,    self.word_shape_file							,    self.word_pronunciation_file				)

						lowerCamelCase__ :      int								=		tokenizer.tokenize("""你好[SEP]你是谁"""				)
						self.assertListEqual(__lowerCAmelCase							,    ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]				)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase				)							,    [5, 6, 2, 5, 7, 8]				)
						self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__lowerCAmelCase				)							,    [5, 6, 2, 5, 7, 8]				)
						self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__lowerCAmelCase				)							,    [5, 6, 2, 5, 7, 8]				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		RoCBertBasicTokenizer()

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase							,    strip_accents=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase							,    strip_accents=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase							,    strip_accents=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase							,    strip_accents=__lowerCAmelCase				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		RoCBertBasicTokenizer(do_lower_case=__lowerCAmelCase							,    never_split=["""[UNK]"""]				)

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

						lowerCamelCase__ :      Any								=		{}
						for i, token in enumerate(__lowerCAmelCase				):
									lowerCamelCase__ :      List[Any]								=		i
						lowerCamelCase__ :      Union[str, Any]								=		RoCBertWordpieceTokenizer(vocab=__lowerCAmelCase							,    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        _snake_case    (self				):
						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        _snake_case    (self				):
						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        _snake_case    (self				):
						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        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		self.get_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(__lowerCAmelCase				) for t in ["""Test""", """\xad""", """test"""]]							,    [["""[UNK]"""], [], ["""[UNK]"""]]				)

						if self.test_rust_tokenizer:
									lowerCamelCase__ :      Tuple								=		self.get_rust_tokenizer()
									self.assertListEqual(
									    [rust_tokenizer.tokenize(__lowerCAmelCase				) for t in ["""Test""", """\xad""", """test"""]]							,    [["""[UNK]"""], [], ["""[UNK]"""]]				)





			def        _snake_case    (self				):







						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
												lowerCamelCase__ :      int								=		self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase							,    **__lowerCAmelCase				)

												lowerCamelCase__ :      Any								=		f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
												lowerCamelCase__ :      List[str]								=		tokenizer_r.encode_plus(
												    __lowerCAmelCase							,    return_attention_mask=__lowerCAmelCase							,    return_token_type_ids=__lowerCAmelCase							,    return_offsets_mapping=__lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase							,    )

												lowerCamelCase__ :      List[str]								=		tokenizer_r.do_lower_case if hasattr(__lowerCAmelCase							,    """do_lower_case"""				) else False
												lowerCamelCase__ :      List[str]								=		(
												    [
												        ((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        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		["""的""", """人""", """有"""]
						lowerCamelCase__ :      Dict								=		"""""".join(__lowerCAmelCase				)
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
												lowerCamelCase__ :      int								=		True
												lowerCamelCase__ :      List[str]								=		self.tokenizer_class.from_pretrained(__lowerCAmelCase							,    **__lowerCAmelCase				)
												lowerCamelCase__ :      Tuple								=		self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase							,    **__lowerCAmelCase				)

												lowerCamelCase__ :      Dict								=		tokenizer_p.encode(__lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase				)
												lowerCamelCase__ :      List[str]								=		tokenizer_r.encode(__lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase				)

												lowerCamelCase__ :      Union[str, Any]								=		tokenizer_r.convert_ids_to_tokens(__lowerCAmelCase				)
												lowerCamelCase__ :      Any								=		tokenizer_p.convert_ids_to_tokens(__lowerCAmelCase				)

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

												lowerCamelCase__ :      List[str]								=		False
												lowerCamelCase__ :      Optional[int]								=		self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase							,    **__lowerCAmelCase				)
												lowerCamelCase__ :      str								=		self.tokenizer_class.from_pretrained(__lowerCAmelCase							,    **__lowerCAmelCase				)

												lowerCamelCase__ :      int								=		tokenizer_r.encode(__lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase				)
												lowerCamelCase__ :      Optional[int]								=		tokenizer_p.encode(__lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase				)

												lowerCamelCase__ :      Dict								=		tokenizer_r.convert_ids_to_tokens(__lowerCAmelCase				)
												lowerCamelCase__ :      Any								=		tokenizer_p.convert_ids_to_tokens(__lowerCAmelCase				)

												# it is expected that only the first Chinese character is not preceded by "##".
												lowerCamelCase__ :      Optional[int]								=		[
												    f"##{token}" if idx != 0 else token for idx, token in enumerate(__lowerCAmelCase				)
												]
												self.assertListEqual(__lowerCAmelCase							,    __lowerCAmelCase				)
												self.assertListEqual(__lowerCAmelCase							,    __lowerCAmelCase				)





			@slow
			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.tokenizer_class(self.vocab_file							,    self.word_shape_file							,    self.word_pronunciation_file				)

						lowerCamelCase__ :      Optional[int]								=		tokenizer.encode("""你好"""							,    add_special_tokens=__lowerCAmelCase				)
						lowerCamelCase__ :      str								=		tokenizer.encode("""你是谁"""							,    add_special_tokens=__lowerCAmelCase				)

						lowerCamelCase__ :      int								=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				)
						lowerCamelCase__ :      int								=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase							,    __lowerCAmelCase				)

						assert encoded_sentence == [1] + text + [2]
						assert encoded_pair == [1] + text + [2] + text_a + [2]





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		self.get_tokenizers(do_lower_case=__lowerCAmelCase				)
						for tokenizer in tokenizers:
									with self.subTest(f"{tokenizer.__class__.__name__}"				):
												lowerCamelCase__ :      Optional[Any]								=		"""你好，你是谁"""
												lowerCamelCase__ :      int								=		tokenizer.tokenize(__lowerCAmelCase				)
												lowerCamelCase__ :      Optional[Any]								=		tokenizer.convert_tokens_to_ids(__lowerCAmelCase				)
												lowerCamelCase__ :      Tuple								=		tokenizer.convert_tokens_to_shape_ids(__lowerCAmelCase				)
												lowerCamelCase__ :      Dict								=		tokenizer.convert_tokens_to_pronunciation_ids(__lowerCAmelCase				)
												lowerCamelCase__ :      Tuple								=		tokenizer.prepare_for_model(
												    __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase				)

												lowerCamelCase__ :      Dict								=		tokenizer.encode_plus(__lowerCAmelCase							,    add_special_tokens=__lowerCAmelCase				)

												self.assertEqual(__lowerCAmelCase							,    __lowerCAmelCase				)








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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''pixel_values''']





			def __init__(self							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = 1 / 255							,    __magic_name__ = True							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(**__magic_name__				)
						lowerCamelCase__ :      str								=		size if size is not None else {"""height""": 256, """width""": 256}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      str								=		do_resize
						lowerCamelCase__ :      Any								=		size
						lowerCamelCase__ :      Tuple								=		resample
						lowerCamelCase__ :      Union[str, Any]								=		do_center_crop
						lowerCamelCase__ :      List[str]								=		crop_size
						lowerCamelCase__ :      int								=		do_rescale
						lowerCamelCase__ :      Any								=		rescale_factor
						lowerCamelCase__ :      Dict								=		do_normalize
						lowerCamelCase__ :      Dict								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCamelCase__ :      int								=		image_std if image_std is not None else IMAGENET_STANDARD_STD





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						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(
						    __magic_name__							,    size=(size["""height"""], size["""width"""])							,    resample=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						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(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return normalize(__magic_name__							,    mean=__magic_name__							,    std=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = ChannelDimension.FIRST							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		do_resize if do_resize is not None else self.do_resize
						lowerCamelCase__ :      str								=		resample if resample is not None else self.resample
						lowerCamelCase__ :      str								=		do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase__ :      Optional[int]								=		do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase__ :      str								=		rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase__ :      Union[str, Any]								=		do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase__ :      Tuple								=		image_mean if image_mean is not None else self.image_mean
						lowerCamelCase__ :      List[Any]								=		image_std if image_std is not None else self.image_std

						lowerCamelCase__ :      str								=		size if size is not None else self.size
						lowerCamelCase__ :      List[str]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      Optional[Any]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      Optional[int]								=		make_list_of_images(__magic_name__				)

						if not valid_images(__magic_name__				):
									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.
						lowerCamelCase__ :      Optional[int]								=		[to_numpy_array(__magic_name__				) for image in images]

						if do_resize:
									lowerCamelCase__ :      int								=		[self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				) for image in images]

						if do_center_crop:
									lowerCamelCase__ :      List[Any]								=		[self.center_crop(image=__magic_name__							,    size=__magic_name__				) for image in images]

						if do_rescale:
									lowerCamelCase__ :      Dict								=		[self.rescale(image=__magic_name__							,    scale=__magic_name__				) for image in images]

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

						lowerCamelCase__ :      List[str]								=		[to_channel_dimension_format(__magic_name__							,    __magic_name__				) for image in images]

						lowerCamelCase__ :      Dict								=		{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)



