






"""simple docstring"""



import os
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    NezhaForMaskedLM,
						    NezhaForMultipleChoice,
						    NezhaForNextSentencePrediction,
						    NezhaForPreTraining,
						    NezhaForQuestionAnswering,
						    NezhaForSequenceClassification,
						    NezhaForTokenClassification,
						    NezhaModel,
						)
						from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST





class 					_UpperCAmelCase       :




					def __init__(				self     :						List[Any]    ,						A     :						List[Any]    ,						A     :						Any=13    ,						A     :						str=7    ,						A     :						Union[str, Any]=True    ,						A     :						Any=True    ,						A     :						int=True    ,						A     :						Optional[int]=True    ,						A     :						List[str]=99    ,						A     :						str=32    ,						A     :						Dict=5    ,						A     :						List[str]=4    ,						A     :						Dict=37    ,						A     :						Optional[int]="gelu"    ,						A     :						int=0.1    ,						A     :						List[Any]=0.1    ,						A     :						Tuple=1_28    ,						A     :						Union[str, Any]=32    ,						A     :						str=16    ,						A     :						List[str]=2    ,						A     :						Optional[int]=0.02    ,						A     :						Any=3    ,						A     :						Any=4    ,						A     :						Optional[Any]=None    ,						)  ->  Any:
							lowercase_ : Optional[Any]							       =   parent
							lowercase_ : Optional[int]							       =   batch_size
							lowercase_ : int							       =   seq_length
							lowercase_ : Optional[Any]							       =   is_training
							lowercase_ : int							       =   use_input_mask
							lowercase_ : Tuple							       =   use_token_type_ids
							lowercase_ : List[str]							       =   use_labels
							lowercase_ : List[str]							       =   vocab_size
							lowercase_ : Any							       =   hidden_size
							lowercase_ : Any							       =   num_hidden_layers
							lowercase_ : List[Any]							       =   num_attention_heads
							lowercase_ : List[str]							       =   intermediate_size
							lowercase_ : Tuple							       =   hidden_act
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : Tuple							       =   attention_probs_dropout_prob
							lowercase_ : List[Any]							       =   max_position_embeddings
							lowercase_ : Dict							       =   type_vocab_size
							lowercase_ : int							       =   type_sequence_label_size
							lowercase_ : Union[str, Any]							       =   initializer_range
							lowercase_ : List[Any]							       =   num_labels
							lowercase_ : Optional[int]							       =   num_choices
							lowercase_ : Any							       =   scope




					def   A  (				self     :						Optional[Any]   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

							lowercase_ : Any							       =   None
							if self.use_input_mask:
									lowercase_ : Tuple							       =   random_attention_mask([self.batch_size, self.seq_length]   )

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

							lowercase_ : Optional[Any]							       =   None
							lowercase_ : Dict							       =   None
							lowercase_ : List[str]							       =   None
							if self.use_labels:
									lowercase_ : List[Any]							       =   ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )
									lowercase_ : str							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels   )
									lowercase_ : int							       =   ids_tensor([self.batch_size]    ,						self.num_choices   )

							lowercase_ : str							       =   self.get_config()

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




					def   A  (				self     :						str   )  ->  str:
							return NezhaConfig(
							    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    ,						is_decoder=__lowercase    ,						initializer_range=self.initializer_range    ,						)




					def   A  (				self     :						int   )  ->  Optional[Any]:
							(
							    lowercase_ 
							) : List[str]							       =   self.prepare_config_and_inputs()

							lowercase_ : str							       =   True
							lowercase_ : Union[str, Any]							       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							lowercase_ : Tuple							       =   ids_tensor([self.batch_size, self.seq_length]    ,						vocab_size=2   )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)




					def   A  (				self     :						str    ,						A     :						Union[str, Any]    ,						A     :						str    ,						A     :						Optional[int]    ,						A     :						Dict    ,						A     :						Union[str, Any]    ,						A     :						str    ,						A     :						int   )  ->  Any:
							lowercase_ : Union[str, Any]							       =   NezhaModel(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : int							       =   model(__lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase   )
							lowercase_ : Optional[Any]							       =   model(__lowercase    ,						token_type_ids=__lowercase   )
							lowercase_ : List[Any]							       =   model(__lowercase   )
							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     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						Optional[int]    ,						A     :						Dict    ,						A     :						Optional[int]    ,						A     :						Any    ,						A     :						str    ,						A     :						Tuple    ,						A     :						Optional[Any]    ,						A     :						Tuple    ,						)  ->  Optional[int]:
							lowercase_ : List[Any]							       =   True
							lowercase_ : Optional[Any]							       =   NezhaModel(__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : List[Any]							       =   model(
							    __lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						encoder_hidden_states=__lowercase    ,						encoder_attention_mask=__lowercase    ,						)
							lowercase_ : Optional[Any]							       =   model(
							    __lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						encoder_hidden_states=__lowercase    ,						)
							lowercase_ : List[str]							       =   model(__lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase   )
							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     :						Any    ,						A     :						int    ,						A     :						str    ,						A     :						Dict    ,						A     :						Union[str, Any]    ,						A     :						Tuple    ,						A     :						Optional[int]    ,						A     :						Optional[int]   )  ->  Optional[int]:
							lowercase_ : Optional[int]							       =   NezhaForMaskedLM(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : Optional[int]							       =   model(__lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						labels=__lowercase   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )




					def   A  (				self     :						Dict    ,						A     :						str    ,						A     :						Optional[Any]    ,						A     :						int    ,						A     :						List[str]    ,						A     :						Dict    ,						A     :						str    ,						A     :						str   )  ->  Dict:
							lowercase_ : Optional[int]							       =   NezhaForNextSentencePrediction(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : Union[str, Any]							       =   model(
							    __lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						labels=__lowercase    ,						)
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, 2)   )




					def   A  (				self     :						Tuple    ,						A     :						Any    ,						A     :						Dict    ,						A     :						Optional[int]    ,						A     :						Tuple    ,						A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						List[str]   )  ->  int:
							lowercase_ : Optional[Any]							       =   NezhaForPreTraining(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : int							       =   model(
							    __lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						labels=__lowercase    ,						next_sentence_label=__lowercase    ,						)
							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   A  (				self     :						Tuple    ,						A     :						List[str]    ,						A     :						Any    ,						A     :						Tuple    ,						A     :						Optional[Any]    ,						A     :						Dict    ,						A     :						List[Any]    ,						A     :						Dict   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   NezhaForQuestionAnswering(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : Any							       =   model(
							    __lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						start_positions=__lowercase    ,						end_positions=__lowercase    ,						)
							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     :						Tuple    ,						A     :						Dict    ,						A     :						Any    ,						A     :						Any    ,						A     :						Union[str, Any]    ,						A     :						int    ,						A     :						Dict    ,						A     :						List[Any]   )  ->  Optional[Any]:
							lowercase_ : Optional[int]							       =   self.num_labels
							lowercase_ : Union[str, Any]							       =   NezhaForSequenceClassification(__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : Optional[Any]							       =   model(__lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						labels=__lowercase   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )




					def   A  (				self     :						Tuple    ,						A     :						List[Any]    ,						A     :						Union[str, Any]    ,						A     :						str    ,						A     :						List[Any]    ,						A     :						List[str]    ,						A     :						Dict    ,						A     :						Dict   )  ->  str:
							lowercase_ : Union[str, Any]							       =   self.num_labels
							lowercase_ : Dict							       =   NezhaForTokenClassification(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : Dict							       =   model(__lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						labels=__lowercase   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels)   )




					def   A  (				self     :						Tuple    ,						A     :						Any    ,						A     :						List[Any]    ,						A     :						Dict    ,						A     :						Tuple    ,						A     :						List[Any]    ,						A     :						Any    ,						A     :						int   )  ->  Optional[int]:
							lowercase_ : List[str]							       =   self.num_choices
							lowercase_ : int							       =   NezhaForMultipleChoice(config=__lowercase   )
							model.to(__lowercase   )
							model.eval()
							lowercase_ : List[str]							       =   input_ids.unsqueeze(1   ).expand(-1    ,						self.num_choices    ,						-1   ).contiguous()
							lowercase_ : Tuple							       =   token_type_ids.unsqueeze(1   ).expand(-1    ,						self.num_choices    ,						-1   ).contiguous()
							lowercase_ : int							       =   input_mask.unsqueeze(1   ).expand(-1    ,						self.num_choices    ,						-1   ).contiguous()
							lowercase_ : Tuple							       =   model(
							    __lowercase    ,						attention_mask=__lowercase    ,						token_type_ids=__lowercase    ,						labels=__lowercase    ,						)
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_choices)   )






					def   A  (				self     :						Dict   )  ->  List[Any]:
							lowercase_ : Optional[int]							       =   self.prepare_config_and_inputs()
							(
							    lowercase_ 
							) : Tuple							       =   config_and_inputs
							lowercase_ : Optional[Any]							       =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict





@require_torch
class 					_UpperCAmelCase       ( lowercase__   ,						lowercase__   ,						lowercase__   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   (
					    (
					        NezhaModel,
					        NezhaForMaskedLM,
					        NezhaForMultipleChoice,
					        NezhaForNextSentencePrediction,
					        NezhaForPreTraining,
					        NezhaForQuestionAnswering,
					        NezhaForSequenceClassification,
					        NezhaForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   (
					    {
					        'feature-extraction': NezhaModel,
					        'fill-mask': NezhaForMaskedLM,
					        'question-answering': NezhaForQuestionAnswering,
					        'text-classification': NezhaForSequenceClassification,
					        'token-classification': NezhaForTokenClassification,
					        'zero-shot': NezhaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:				Dict							    =   True




					def   A  (				self     :						Optional[int]    ,						A     :						str    ,						A     :						Dict    ,						A     :						int=False   )  ->  Dict:
							lowercase_ : Optional[Any]							       =   super()._prepare_for_class(__lowercase    ,						__lowercase    ,						return_labels=__lowercase   )

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




					def   A  (				self     :						Any   )  ->  int:
							lowercase_ : Tuple							       =   NezhaModelTester(self   )
							lowercase_ : List[Any]							       =   ConfigTester(self    ,						config_class=__lowercase    ,						hidden_size=37   )




					def   A  (				self     :						Dict   )  ->  List[str]:
							self.config_tester.run_common_tests()




					def   A  (				self     :						str   )  ->  List[Any]:
							lowercase_ : Optional[int]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase   )




					def   A  (				self     :						Optional[Any]   )  ->  Tuple:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*__lowercase   )




					def   A  (				self     :						int   )  ->  str:
							# This regression test was failing with PyTorch < 1.3
							(
							    lowercase_ 
							) : Tuple							       =   self.model_tester.prepare_config_and_inputs_for_decoder()

							lowercase_ : int							       =   None

							self.model_tester.create_and_check_model_as_decoder(
							    __lowercase    ,						__lowercase    ,						__lowercase    ,						__lowercase    ,						__lowercase    ,						__lowercase    ,						__lowercase    ,						__lowercase    ,						__lowercase    ,						)




					def   A  (				self     :						Dict   )  ->  Union[str, Any]:
							lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowercase   )




					def   A  (				self     :						int   )  ->  Dict:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__lowercase   )




					def   A  (				self     :						List[str]   )  ->  List[Any]:
							lowercase_ : int							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_next_sequence_prediction(*__lowercase   )




					def   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							lowercase_ : Optional[int]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__lowercase   )




					def   A  (				self     :						int   )  ->  Dict:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowercase   )




					def   A  (				self     :						List[str]   )  ->  str:
							lowercase_ : Union[str, Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowercase   )




					def   A  (				self     :						Optional[Any]   )  ->  str:
							lowercase_ : int							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowercase   )




					@slow
					def   A  (				self     :						str   )  ->  Any:
							for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : int							       =   NezhaModel.from_pretrained(__lowercase   )
									self.assertIsNotNone(__lowercase   )






					@slow
					@require_torch_gpu
					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									# NezhaForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == NezhaForMultipleChoice:
											return

									lowercase_ : Optional[Any]							       =   True
									lowercase_ : Dict							       =   model_class(config=__lowercase   )

									lowercase_ : Union[str, Any]							       =   self._prepare_for_class(__lowercase    ,						__lowercase   )
									lowercase_ : Union[str, Any]							       =   torch.jit.trace(
									    __lowercase    ,						(inputs_dict['''input_ids'''].to('''cpu'''   ), inputs_dict['''attention_mask'''].to('''cpu'''   ))   )

									with tempfile.TemporaryDirectory() as tmp:
											torch.jit.save(__lowercase    ,						os.path.join(__lowercase    ,						'''bert.pt'''   )   )
											lowercase_ : Optional[int]							       =   torch.jit.load(os.path.join(__lowercase    ,						'''bert.pt'''   )    ,						map_location=__lowercase   )
											loaded(inputs_dict['''input_ids'''].to(__lowercase   )    ,						inputs_dict['''attention_mask'''].to(__lowercase   )   )





@require_torch
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						Any   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''   )
							lowercase_ : List[Any]							       =   torch.tensor([[0, 1, 2, 3, 4, 5]]   )
							lowercase_ : Any							       =   torch.tensor([[0, 1, 1, 1, 1, 1]]   )
							with torch.no_grad():
									lowercase_ : Optional[Any]							       =   model(__lowercase    ,						attention_mask=__lowercase   )[0]
							lowercase_ : Dict							       =   torch.Size((1, 6, 7_68)   )
							self.assertEqual(output.shape    ,						__lowercase   )
							lowercase_ : Any							       =   torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]   )

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






					@slow
					def   A  (				self     :						str   )  ->  List[str]:
							lowercase_ : int							       =   NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''   )
							lowercase_ : Tuple							       =   torch.tensor([[0, 1, 2, 3, 4, 5]]   )
							lowercase_ : Any							       =   torch.tensor([[1, 1, 1, 1, 1, 1]]   )
							with torch.no_grad():
									lowercase_ : Dict							       =   model(__lowercase    ,						attention_mask=__lowercase   )[0]
							lowercase_ : Union[str, Any]							       =   torch.Size((1, 6, 2_11_28)   )
							self.assertEqual(output.shape    ,						__lowercase   )
							lowercase_ : int							       =   torch.tensor(
							    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]   )

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











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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


a		:       Union[str, Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Any							       =	['pixel_values']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = 0.9		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			Union[int, float] = 1 / 255		,      __lowercase					:			bool = True		,      __lowercase					:			bool = True		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      **__lowercase					:			Any		,      )							->      None:
        super().__init__(**__lowercase					)
        __UpperCAmelCase							:	Tuple           =  size if size is not None else {"""shortest_edge""": 224}
        __UpperCAmelCase							:	Union[str, Any]           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Union[str, Any]           =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:	Any           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	Dict           =  do_resize
        __UpperCAmelCase							:	Dict           =  size
        __UpperCAmelCase							:	Tuple           =  crop_pct
        __UpperCAmelCase							:	List[Any]           =  resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop
        __UpperCAmelCase							:	List[Any]           =  crop_size
        __UpperCAmelCase							:	Any           =  do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor
        __UpperCAmelCase							:	int           =  do_normalize
        __UpperCAmelCase							:	List[Any]           =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __UpperCAmelCase							:	List[str]           =  image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[float] = None		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Optional[int]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
          raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
        if crop_pct is not None:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	Union[str, Any]           =  int(size["""shortest_edge"""] / crop_pct					)
          elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
              __UpperCAmelCase							:	Tuple           =  int(size["""height"""] / crop_pct					)
            else:
              __UpperCAmelCase							:	str           =  (int(size["""height"""] / crop_pct					), int(size["""width"""] / crop_pct					))
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

          __UpperCAmelCase							:	str           =  get_resize_output_image_size(__lowercase		,      size=__lowercase		,      default_to_square=__lowercase					)
        else:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	List[str]           =  get_resize_output_image_size(__lowercase		,      size=size["""shortest_edge"""]		,      default_to_square=__lowercase					)
          elif "height" in size and "width" in size:
            __UpperCAmelCase							:	int           =  (size["""height"""], size["""width"""])
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

        return resize(__lowercase		,      size=__lowercase		,      resample=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Union[str, Any]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Optional[Any]           =  get_size_dict(__lowercase					)
        if "height" not in size or "width" not in size:
          raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""					)
        return center_crop(__lowercase		,      size=(size["""height"""], size["""width"""])		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[int, float]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			int		,      )							->      int:
        return rescale(__lowercase		,      scale=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			List[Any]		,      )							->      np.ndarray:
        return normalize(__lowercase		,      mean=__lowercase		,      std=__lowercase		,      data_format=__lowercase		,      **__lowercase					)




      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			ImageInput		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = None		,      __lowercase					:			PILImageResampling = None		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			bool = None		,      __lowercase					:			float = None		,      __lowercase					:			bool = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[str, TensorType]] = None		,      __lowercase					:			ChannelDimension = ChannelDimension.FIRST		,      **__lowercase					:			List[str]		,      )							->      PIL.Image.Image:
        __UpperCAmelCase							:	Any           =  do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase							:	Optional[int]           =  crop_pct if crop_pct is not None else self.crop_pct
        __UpperCAmelCase							:	Optional[Any]           =  resample if resample is not None else self.resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase							:	Dict           =  do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase							:	Union[str, Any]           =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase							:	Tuple           =  image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase							:	Any           =  image_std if image_std is not None else self.image_std

        __UpperCAmelCase							:	Optional[int]           =  size if size is not None else self.size
        __UpperCAmelCase							:	Dict           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Tuple           =  crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	Dict           =  make_list_of_images(__lowercase					)

        if not valid_images(__lowercase					):
          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_pct is None:
          raise ValueError("""Crop_pct 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.
        __UpperCAmelCase							:	str           =  [to_numpy_array(__lowercase					) for image in images]

        if do_resize:
          __UpperCAmelCase							:	str           =  [self.resize(image=__lowercase		,      size=__lowercase		,      crop_pct=__lowercase		,      resample=__lowercase					) for image in images]

        if do_center_crop:
          __UpperCAmelCase							:	Any           =  [self.center_crop(image=__lowercase		,      size=__lowercase					) for image in images]

        if do_rescale:
          __UpperCAmelCase							:	List[str]           =  [self.rescale(image=__lowercase		,      scale=__lowercase					) for image in images]

        if do_normalize:
          __UpperCAmelCase							:	str           =  [self.normalize(image=__lowercase		,      mean=__lowercase		,      std=__lowercase					) for image in images]

        __UpperCAmelCase							:	List[str]           =  [to_channel_dimension_format(__lowercase		,      __lowercase					) for image in images]

        __UpperCAmelCase							:	Any           =  {"""pixel_values""": images}
        return BatchFeature(data=__lowercase		,      tensor_type=__lowercase					)


