


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      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							RoCBertTokenizer
  _SCREAMING_SNAKE_CASE		     =							None
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							True
  _SCREAMING_SNAKE_CASE		     =							filter_non_english



  def        lowerCAmelCase__	(  self							:							int	) ->			Tuple:




     """simple docstring"""
     super().setUp()

     snake_case_            =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
     snake_case_            =       {}
     snake_case_            =       {}
     for i, value in enumerate(_lowerCAmelCase	):
        snake_case_            =       i
        snake_case_            =       i
     snake_case_            =       os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["vocab_file"]	)
     snake_case_            =       os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["word_shape_file"]	)
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Any	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       self.tokenizer_class(self.vocab_file     ,					self.word_shape_file     ,					self.word_pronunciation_file	)

     snake_case_            =       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        lowerCAmelCase__	(  self							:							Any	) ->			str:




     """simple docstring"""
     snake_case_            =       RoCBertBasicTokenizer()

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



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Any	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Dict	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       RoCBertBasicTokenizer(do_lower_case=_lowerCAmelCase	)

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



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       RoCBertBasicTokenizer(do_lower_case=_lowerCAmelCase     ,					strip_accents=_lowerCAmelCase	)

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



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




     """simple docstring"""
     snake_case_            =       RoCBertBasicTokenizer(do_lower_case=_lowerCAmelCase     ,					strip_accents=_lowerCAmelCase	)

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



  def        lowerCAmelCase__	(  self							:							int	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       RoCBertBasicTokenizer(do_lower_case=_lowerCAmelCase     ,					never_split=["[UNK]"]	)

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



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

     snake_case_            =       {}
     for i, token in enumerate(_lowerCAmelCase	):
        snake_case_            =       i
     snake_case_            =       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        lowerCAmelCase__	(  self							:							List[str]	) ->			Any:




     """simple docstring"""
     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							:							int	) ->			Optional[Any]:




     """simple docstring"""
     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]	) ->			Union[str, Any]:




     """simple docstring"""
     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]	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       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:
        snake_case_            =       self.get_rust_tokenizer()
        self.assertListEqual(
            [rust_tokenizer.tokenize(_lowerCAmelCase	) for t in ["Test", "\xad", "test"]]     ,					[["[UNK]"], [], ["[UNK]"]]	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Optional[Any]:




     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           snake_case_            =       self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase     ,					**_lowerCAmelCase	)

           snake_case_            =       F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
           snake_case_            =       tokenizer_r.encode_plus(
               _lowerCAmelCase     ,					return_attention_mask=_lowerCAmelCase     ,					return_token_type_ids=_lowerCAmelCase     ,					return_offsets_mapping=_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase     ,					)

           snake_case_            =       tokenizer_r.do_lower_case if hasattr(_lowerCAmelCase     ,					"do_lower_case"	) else False
           snake_case_            =       (
               [
                   ((0, 0), tokenizer_r.cls_token),
                   ((0, 1), "A"),
                   ((1, 2), ","),
                   ((3, 5), "na"),
                   ((5, 6), "##ï"),
                   ((6, 8), "##ve"),
                   ((9, 1_5), tokenizer_r.mask_token),
                   ((1_6, 2_1), "Allen"),
                   ((2_1, 2_3), "##NL"),
                   ((2_3, 2_4), "##P"),
                   ((2_5, 3_3), "sentence"),
                   ((3_3, 3_4), "."),
                   ((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, 1_5), tokenizer_r.mask_token),
                   ((1_6, 2_1), "allen"),
                   ((2_1, 2_3), "##nl"),
                   ((2_3, 2_4), "##p"),
                   ((2_5, 3_3), "sentence"),
                   ((3_3, 3_4), "."),
                   ((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							:							Dict	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       ["的", "人", "有"]
     snake_case_            =       "".join(_lowerCAmelCase	)
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           snake_case_            =       True
           snake_case_            =       self.tokenizer_class.from_pretrained(_lowerCAmelCase     ,					**_lowerCAmelCase	)
           snake_case_            =       self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase     ,					**_lowerCAmelCase	)

           snake_case_            =       tokenizer_p.encode(_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase	)
           snake_case_            =       tokenizer_r.encode(_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase	)

           snake_case_            =       tokenizer_r.convert_ids_to_tokens(_lowerCAmelCase	)
           snake_case_            =       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	)

           snake_case_            =       False
           snake_case_            =       self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase     ,					**_lowerCAmelCase	)
           snake_case_            =       self.tokenizer_class.from_pretrained(_lowerCAmelCase     ,					**_lowerCAmelCase	)

           snake_case_            =       tokenizer_r.encode(_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase	)
           snake_case_            =       tokenizer_p.encode(_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase	)

           snake_case_            =       tokenizer_r.convert_ids_to_tokens(_lowerCAmelCase	)
           snake_case_            =       tokenizer_p.convert_ids_to_tokens(_lowerCAmelCase	)

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



  @slow
  def        lowerCAmelCase__	(  self							:							Any	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.tokenizer_class(self.vocab_file     ,					self.word_shape_file     ,					self.word_pronunciation_file	)

     snake_case_            =       tokenizer.encode("你好"     ,					add_special_tokens=_lowerCAmelCase	)
     snake_case_            =       tokenizer.encode("你是谁"     ,					add_special_tokens=_lowerCAmelCase	)

     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	)
     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase     ,					_lowerCAmelCase	)

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



  def        lowerCAmelCase__	(  self							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.get_tokenizers(do_lower_case=_lowerCAmelCase	)
     for tokenizer in tokenizers:
        with self.subTest(F'''{tokenizer.__class__.__name__}'''	):
           snake_case_            =       "你好，你是谁"
           snake_case_            =       tokenizer.tokenize(_lowerCAmelCase	)
           snake_case_            =       tokenizer.convert_tokens_to_ids(_lowerCAmelCase	)
           snake_case_            =       tokenizer.convert_tokens_to_shape_ids(_lowerCAmelCase	)
           snake_case_            =       tokenizer.convert_tokens_to_pronunciation_ids(_lowerCAmelCase	)
           snake_case_            =       tokenizer.prepare_for_model(
               _lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase	)

           snake_case_            =       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


SCREAMING_SNAKE_CASE						:List[Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							['pixel_values']



  def __init__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							PILImageResampling = PIL.Image.BICUBIC     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							Union[int, float] = 1 / 2_5_5     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					**_lowerCAmelCase							:							Optional[Any]     ,					) ->			None:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)
     snake_case_            =       size if size is not None else {"height": 2_5_6, "width": 2_5_6}
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     snake_case_            =       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
     snake_case_            =       get_size_dict(_lowerCAmelCase     ,					param_name="crop_size"	)

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



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




     """simple docstring"""
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     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(
         _lowerCAmelCase     ,					size=(size["height"], size["width"])     ,					resample=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



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




     """simple docstring"""
     snake_case_            =       get_size_dict(_lowerCAmelCase	)
     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(_lowerCAmelCase     ,					size=(size["height"], size["width"])     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



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




     """simple docstring"""
     return rescale(_lowerCAmelCase     ,					scale=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



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




     """simple docstring"""
     return normalize(_lowerCAmelCase     ,					mean=_lowerCAmelCase     ,					std=_lowerCAmelCase     ,					data_format=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							ImageInput     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							int=None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							float = None     ,					_lowerCAmelCase							:							bool = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[float, List[float]]] = None     ,					_lowerCAmelCase							:							Optional[Union[str, TensorType]] = None     ,					_lowerCAmelCase							:							ChannelDimension = ChannelDimension.FIRST     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			PIL.Image.Image:




     """simple docstring"""
     snake_case_            =       do_resize if do_resize is not None else self.do_resize
     snake_case_            =       resample if resample is not None else self.resample
     snake_case_            =       do_center_crop if do_center_crop is not None else self.do_center_crop
     snake_case_            =       do_rescale if do_rescale is not None else self.do_rescale
     snake_case_            =       rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_            =       do_normalize if do_normalize is not None else self.do_normalize
     snake_case_            =       image_mean if image_mean is not None else self.image_mean
     snake_case_            =       image_std if image_std is not None else self.image_std

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

     snake_case_            =       make_list_of_images(_lowerCAmelCase	)

     if not valid_images(_lowerCAmelCase	):
        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.
     snake_case_            =       [to_numpy_array(_lowerCAmelCase	) for image in images]

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

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

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

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

     snake_case_            =       [to_channel_dimension_format(_lowerCAmelCase     ,					_lowerCAmelCase	) for image in images]

     snake_case_            =       {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase     ,					tensor_type=_lowerCAmelCase	)




