
import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_PRETRAINING_MAPPING,
			    AlbertForMaskedLM,
			    AlbertForMultipleChoice,
			    AlbertForPreTraining,
			    AlbertForQuestionAnswering,
			    AlbertForSequenceClassification,
			    AlbertForTokenClassification,
			    AlbertModel,
			)
			from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=13				,				_UpperCamelCase=7				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=99				,				_UpperCamelCase=16				,				_UpperCamelCase=36				,				_UpperCamelCase=6				,				_UpperCamelCase=6				,				_UpperCamelCase=6				,				_UpperCamelCase=37				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=16				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=3				,				_UpperCamelCase=4				,				_UpperCamelCase=None				,				)				->      Dict:
							lowerCAmelCase_						=	parent
							lowerCAmelCase_						=	batch_size
							lowerCAmelCase_						=	seq_length
							lowerCAmelCase_						=	is_training
							lowerCAmelCase_						=	use_input_mask
							lowerCAmelCase_						=	use_token_type_ids
							lowerCAmelCase_						=	use_labels
							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	embedding_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_hidden_groups
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	type_vocab_size
							lowerCAmelCase_						=	type_sequence_label_size
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	num_labels
							lowerCAmelCase_						=	num_choices
							lowerCAmelCase_						=	scope




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size     )

							lowerCAmelCase_						=	None
							if self.use_input_mask:
										lowerCAmelCase_						=	random_attention_mask([self.batch_size, self.seq_length]     )

							lowerCAmelCase_						=	None
							if self.use_token_type_ids:
										lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size     )

							lowerCAmelCase_						=	None
							lowerCAmelCase_						=	None
							lowerCAmelCase_						=	None
							if self.use_labels:
										lowerCAmelCase_						=	ids_tensor([self.batch_size]				,				self.type_sequence_label_size     )
										lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels     )
										lowerCAmelCase_						=	ids_tensor([self.batch_size]				,				self.num_choices     )

							lowerCAmelCase_						=	self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def      __a						(					self     )				->      Union[str, Any]:
							return AlbertConfig(
							    vocab_size=self.vocab_size				,				hidden_size=self.hidden_size				,				num_hidden_layers=self.num_hidden_layers				,				num_attention_heads=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				hidden_act=self.hidden_act				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				max_position_embeddings=self.max_position_embeddings				,				type_vocab_size=self.type_vocab_size				,				initializer_range=self.initializer_range				,				num_hidden_groups=self.num_hidden_groups				,				)




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	AlbertModel(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase     )
							self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)     )
							self.parent.assertEqual(result.pooler_output.shape				,				(self.batch_size, self.hidden_size)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	AlbertForPreTraining(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase				,				sentence_order_label=_UpperCamelCase				,				)
							self.parent.assertEqual(result.prediction_logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)     )
							self.parent.assertEqual(result.sop_logits.shape				,				(self.batch_size, config.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	AlbertForMaskedLM(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase     )
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Tuple:
							lowerCAmelCase_						=	AlbertForQuestionAnswering(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				start_positions=_UpperCamelCase				,				end_positions=_UpperCamelCase				,				)
							self.parent.assertEqual(result.start_logits.shape				,				(self.batch_size, self.seq_length)     )
							self.parent.assertEqual(result.end_logits.shape				,				(self.batch_size, self.seq_length)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	self.num_labels
							lowerCAmelCase_						=	AlbertForSequenceClassification(_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase     )
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.num_labels
							lowerCAmelCase_						=	AlbertForTokenClassification(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase     )
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	self.num_choices
							lowerCAmelCase_						=	AlbertForMultipleChoice(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	input_ids.unsqueeze(1     ).expand(-1				,				self.num_choices				,				-1     ).contiguous()
							lowerCAmelCase_						=	token_type_ids.unsqueeze(1     ).expand(-1				,				self.num_choices				,				-1     ).contiguous()
							lowerCAmelCase_						=	input_mask.unsqueeze(1     ).expand(-1				,				self.num_choices				,				-1     ).contiguous()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase				,				)
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_choices)     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							(
							    lowerCAmelCase_  
							)						=	config_and_inputs
							lowerCAmelCase_						=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict





@require_torch
class 	_lowerCAmelCase					(	__snake_case					,      __snake_case					,      unittest.TestCase       ):
				_lowercase					=(
				    (
				        AlbertModel,
				        AlbertForPreTraining,
				        AlbertForMaskedLM,
				        AlbertForMultipleChoice,
				        AlbertForSequenceClassification,
				        AlbertForTokenClassification,
				        AlbertForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowercase					=(
				    {
				        '''feature-extraction''': AlbertModel,
				        '''fill-mask''': AlbertForMaskedLM,
				        '''question-answering''': AlbertForQuestionAnswering,
				        '''text-classification''': AlbertForSequenceClassification,
				        '''token-classification''': AlbertForTokenClassification,
				        '''zero-shot''': AlbertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowercase					=True




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=False     )				->      Tuple:
							lowerCAmelCase_						=	super()._prepare_for_class(_UpperCamelCase				,				_UpperCamelCase				,				return_labels=_UpperCamelCase     )

							if return_labels:
										if model_class in get_values(_UpperCamelCase     ):
													lowerCAmelCase_						=	torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)				,				dtype=torch.long				,				device=_UpperCamelCase     )
													lowerCAmelCase_						=	torch.zeros(
													    self.model_tester.batch_size				,				dtype=torch.long				,				device=_UpperCamelCase     )
							return inputs_dict




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	AlbertModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCamelCase				,				hidden_size=37     )




				def      __a						(					self     )				->      int:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase_						=	type
										self.model_tester.create_and_check_model(*_UpperCamelCase     )




				@slow
				def      __a						(					self     )				->      Tuple:






							for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_						=	AlbertModel.from_pretrained(_UpperCamelCase     )
										self.assertIsNotNone(_UpperCamelCase     )





@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):




				@slow
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	AlbertModel.from_pretrained("albert-base-v2"     )
							lowerCAmelCase_						=	torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]     )
							lowerCAmelCase_						=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
							with torch.no_grad():
										lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase     )[0]
							lowerCAmelCase_						=	torch.Size((1, 11, 768)     )
							self.assertEqual(output.shape				,				_UpperCamelCase     )
							lowerCAmelCase_						=	torch.tensor(
							    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,				_UpperCamelCase				,				atol=1e-4     )     )





'''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, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''pixel_values''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 1 / 255						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				None:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE	)
         A       :   str					=						size if size is not None else {'''shortest_edge''': 384}
         A       :   Tuple					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   str					=						do_resize
         A       :   List[Any]					=						size
         # Default value set here for backwards compatibility where the value in config is None
         A       :   List[Any]					=						crop_pct if crop_pct is not None else 224 / 256
         A       :   Optional[int]					=						resample
         A       :   Union[str, Any]					=						do_rescale
         A       :   List[str]					=						rescale_factor
         A       :   Union[str, Any]					=						do_normalize
         A       :   Any					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A       :   Optional[int]					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   str					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
         if "shortest_edge" not in size:
             raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
         A       :   Any					=						size['''shortest_edge''']

         if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             A       :   Dict					=						int(shortest_edge / crop_pct	)
             A       :   str					=						get_resize_output_image_size(SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
             A       :   int					=						resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






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





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






         """simple docstring"""






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

         A       :   Union[str, Any]					=						size if size is not None else self.size
         A       :   List[Any]					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						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 or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('''crop_pct must be specified if size < 384.'''	)

         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.
         A       :   Optional[int]					=						[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

         if do_resize:
             A       :   Any					=						[self.resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			crop_pct=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE	) for image in images]

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

         if do_normalize:
             A       :   Dict					=						[self.normalize(image=SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE	) for image in images]

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

         A       :   Optional[int]					=						{'''pixel_values''': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE						,			tensor_type=SCREAMING_SNAKE_CASE	)




