



import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertModel,
		)





class  __magic_name__   :






		def __init__(						self      :     Dict				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[str]=13				,		UpperCamelCase__      :     Tuple=7				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     str=True				,		UpperCamelCase__      :     Optional[Any]=99				,		UpperCamelCase__      :     str=64				,		UpperCamelCase__      :     List[str]=32				,		UpperCamelCase__      :     List[str]=5				,		UpperCamelCase__      :     Tuple=4				,		UpperCamelCase__      :     str=37				,		UpperCamelCase__      :     Optional[Any]="gelu"				,		UpperCamelCase__      :     List[Any]=0.1				,		UpperCamelCase__      :     Optional[Any]=0.1				,		UpperCamelCase__      :     List[Any]=5_12				,		UpperCamelCase__      :     Tuple=16				,		UpperCamelCase__      :     Optional[int]=2				,		UpperCamelCase__      :     List[Any]=0.02				,		UpperCamelCase__      :     Union[str, Any]=3				,		UpperCamelCase__      :     Dict=4				,		UpperCamelCase__      :     int=None				,		)				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     seq_length
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     use_input_mask
								UpperCAmelCase         =     use_token_type_ids
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     embedding_size
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     type_vocab_size
								UpperCAmelCase         =     type_sequence_label_size
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     num_labels
								UpperCAmelCase         =     num_choices
								UpperCAmelCase         =     scope






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size      )

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

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

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

								UpperCAmelCase         =     self.get_config()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								return MobileBertConfig(
								    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				,		embedding_size=self.embedding_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				,		is_decoder=UpperCamelCase__				,		initializer_range=self.initializer_range				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertModel(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__      )
								UpperCAmelCase         =     model(UpperCamelCase__				,		token_type_ids=UpperCamelCase__      )
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Any      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForMaskedLM(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForNextSentencePrediction(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__				,		)
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, 2)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForPreTraining(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__				,		next_sentence_label=UpperCamelCase__				,		)
								self.parent.assertEqual(result.prediction_logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)      )
								self.parent.assertEqual(result.seq_relationship_logits.shape				,		(self.batch_size, 2)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForQuestionAnswering(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MobileBertForSequenceClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MobileBertForTokenClassification(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_choices
								UpperCAmelCase         =     MobileBertForMultipleChoice(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     input_ids.unsqueeze(1      ).expand(-1				,		self.num_choices				,		-1      ).contiguous()
								UpperCAmelCase         =     token_type_ids.unsqueeze(1      ).expand(-1				,		self.num_choices				,		-1      ).contiguous()
								UpperCAmelCase         =     input_mask.unsqueeze(1      ).expand(-1				,		self.num_choices				,		-1      ).contiguous()
								UpperCAmelCase         =     model(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__				,		)
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_choices)      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)       ,		(
								    UpperCAmelCase
								)       ,		(
								    UpperCAmelCase
								)       ,		(
								    UpperCAmelCase
								)       ,		(
								    UpperCAmelCase
								)       ,		(
								    UpperCAmelCase
								)       ,		(
								    UpperCAmelCase
								)       ,		
								)         =     config_and_inputs
								UpperCAmelCase         =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict





@require_torch
class  __magic_name__   (							A__,  A__,  unittest.TestCase						):
		lowercase      :       int          =(
		    (
		        MobileBertModel,
		        MobileBertForMaskedLM,
		        MobileBertForMultipleChoice,
		        MobileBertForNextSentencePrediction,
		        MobileBertForPreTraining,
		        MobileBertForQuestionAnswering,
		        MobileBertForSequenceClassification,
		        MobileBertForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase      :       str          =(
		    {
		        '''feature-extraction''': MobileBertModel,
		        '''fill-mask''': MobileBertForMaskedLM,
		        '''question-answering''': MobileBertForQuestionAnswering,
		        '''text-classification''': MobileBertForSequenceClassification,
		        '''token-classification''': MobileBertForTokenClassification,
		        '''zero-shot''': MobileBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase      :       List[str]          =True






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[int]=False      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     super()._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertModelTester(self      )
								UpperCAmelCase         =     ConfigTester(self				,		config_class=UpperCamelCase__				,		hidden_size=37      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__      )

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[str]:
						return torch.tensor(
						    lowerCamelCase_    ,   dtype=torch.long    ,   device=lowerCamelCase_    ,   )


__lowerCamelCase     :							str  							=				1E-3





@require_torch
@require_sentencepiece
@require_tokenizers
class  __magic_name__   (							unittest.TestCase						):






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertModel.from_pretrained("google/mobilebert-uncased"      ).to(UpperCamelCase__      )
								UpperCAmelCase         =     _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      )
								with torch.no_grad():
														UpperCAmelCase         =     model(UpperCamelCase__      )[0]
								UpperCAmelCase         =     torch.Size((1, 9, 5_12)      )
								self.assertEqual(output.shape				,		UpperCamelCase__      )
								UpperCAmelCase         =     torch.tensor(
								    [
								        [
								            [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
								            [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
								            [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
								        ]
								    ]				,		device=UpperCamelCase__				,		)

								# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
								# ~1 difference, it's therefore not a good idea to measure using addition.
								# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
								# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
								UpperCAmelCase         =     torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE      )
								UpperCAmelCase         =     torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE      )

								self.assertTrue(lower_bound and upper_bound      )








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


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





class  __magic_name__   (							A__						):
		lowercase      :       Tuple          =['''pixel_values''']






		def __init__(						self      :     Any				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BILINEAR				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Union[int, float] = 1 / 2_55				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		**UpperCamelCase__      :     List[str]				,		)				->					None:
								'''simple docstring'''







								super().__init__(**UpperCamelCase__      )
								UpperCAmelCase         =     size if size is not None else {"shortest_edge": 3_84}
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size
								# Default value set here for backwards compatibility where the value in config is None
								UpperCAmelCase         =     crop_pct if crop_pct is not None else 2_24 / 2_56
								UpperCAmelCase         =     resample
								UpperCAmelCase         =     do_rescale
								UpperCAmelCase         =     rescale_factor
								UpperCAmelCase         =     do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase         =     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Dict[str, int]				,		UpperCamelCase__      :     float				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BICUBIC				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					np.ndarray:
								'''simple docstring'''







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

								if shortest_edge < 3_84:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														UpperCAmelCase         =     int(shortest_edge / crop_pct      )
														UpperCAmelCase         =     get_resize_output_image_size(UpperCamelCase__				,		size=UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
														UpperCAmelCase         =     resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
								else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[int, float]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					List[str]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					np.ndarray:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     ImageInput				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[str, TensorType]] = None				,		UpperCamelCase__      :     ChannelDimension = ChannelDimension.FIRST				,		**UpperCamelCase__      :     Any				,		)				->					PIL.Image.Image:
								'''simple docstring'''







								UpperCAmelCase         =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase         =     crop_pct if crop_pct is not None else self.crop_pct
								UpperCAmelCase         =     resample if resample is not None else self.resample
								UpperCAmelCase         =     do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase         =     rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase         =     do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase         =     image_std if image_std is not None else self.image_std

								UpperCAmelCase         =     size if size is not None else self.size
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     make_list_of_images(UpperCamelCase__      )

								if not valid_images(UpperCamelCase__      ):
														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"] < 3_84 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.
								UpperCAmelCase         =     [to_numpy_array(UpperCamelCase__      ) for image in images]

								if do_resize:
														UpperCAmelCase         =     [self.resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		crop_pct=UpperCamelCase__				,		resample=UpperCamelCase__      ) for image in images]

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

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

								UpperCAmelCase         =     [to_channel_dimension_format(UpperCamelCase__				,		UpperCamelCase__      ) for image in images]

								UpperCAmelCase         =     {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase__				,		tensor_type=UpperCamelCase__      )



