



'''simple docstring'''



import unittest

from transformers import SqueezeBertConfig, is_torch_available
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 (
							    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    SqueezeBertForMaskedLM,
							    SqueezeBertForMultipleChoice,
							    SqueezeBertForQuestionAnswering,
							    SqueezeBertForSequenceClassification,
							    SqueezeBertForTokenClassification,
							    SqueezeBertModel,
							)
class 	lowerCamelCase_							(					_lowerCamelCase			):







						def __init__(  self   :      str				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Tuple=13				,    lowerCAmelCase__   :      int=7				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      int=True				,    lowerCAmelCase__   :      Any=False				,    lowerCAmelCase__   :      Any=True				,    lowerCAmelCase__   :      List[Any]=99				,    lowerCAmelCase__   :      Dict=32				,    lowerCAmelCase__   :      Dict=5				,    lowerCAmelCase__   :      Dict=4				,    lowerCAmelCase__   :      int=64				,    lowerCAmelCase__   :      Tuple="gelu"				,    lowerCAmelCase__   :      int=0.1				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      Optional[int]=5_12				,    lowerCAmelCase__   :      List[str]=16				,    lowerCAmelCase__   :      Union[str, Any]=2				,    lowerCAmelCase__   :      int=0.02				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Union[str, Any]=4				,    lowerCAmelCase__   :      List[Any]=None				,    lowerCAmelCase__   :      Tuple=2				,    lowerCAmelCase__   :      Optional[Any]=2				,    lowerCAmelCase__   :      int=2				,    lowerCAmelCase__   :      List[str]=2				,    lowerCAmelCase__   :      Optional[int]=4				,    lowerCAmelCase__   :      List[Any]=1				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			parent
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      Any     					=			seq_length
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			is_training
													SCREAMING_SNAKE_CASE					:      Any     					=			use_input_mask
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      int     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Dict     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      int     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      Dict     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      str     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Tuple     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      str     					=			type_vocab_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_labels
													SCREAMING_SNAKE_CASE					:      int     					=			num_choices
													SCREAMING_SNAKE_CASE					:      str     					=			scope
													SCREAMING_SNAKE_CASE					:      Dict     					=			q_groups
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			k_groups
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			v_groups
													SCREAMING_SNAKE_CASE					:      Tuple     					=			post_attention_groups
													SCREAMING_SNAKE_CASE					:      Dict     					=			intermediate_groups
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output_groups







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      str     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			None
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			None
													SCREAMING_SNAKE_CASE					:      str     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.batch_size]				,    self.type_sequence_label_size				)
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size]				,    self.num_choices				)

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_config()

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







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													return SqueezeBertConfig(
													    embedding_size=self.hidden_size				,    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				,    attention_probs_dropout_prob=self.hidden_dropout_prob				,    attention_dropout=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    initializer_range=self.initializer_range				,    q_groups=self.q_groups				,    k_groups=self.k_groups				,    v_groups=self.v_groups				,    post_attention_groups=self.post_attention_groups				,    intermediate_groups=self.intermediate_groups				,    output_groups=self.output_groups				,    )







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			SqueezeBertModel(config=A__				)
													model.to(A__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(A__				,    A__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(A__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			SqueezeBertForMaskedLM(config=A__				)
													model.to(A__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(A__				,    attention_mask=A__				,    labels=A__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			SqueezeBertForQuestionAnswering(config=A__				)
													model.to(A__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      int     					=			model(
													    A__				,    attention_mask=A__				,    start_positions=A__				,    end_positions=A__				)
													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 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Any     					=			SqueezeBertForSequenceClassification(A__				)
													model.to(A__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(A__				,    attention_mask=A__				,    labels=A__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			SqueezeBertForTokenClassification(config=A__				)
													model.to(A__				)
													model.eval()

													SCREAMING_SNAKE_CASE					:      Any     					=			model(A__				,    attention_mask=A__				,    labels=A__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.num_choices
													SCREAMING_SNAKE_CASE					:      List[str]     					=			SqueezeBertForMultipleChoice(config=A__				)
													model.to(A__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			input_ids.unsqueeze(1				).expand(-1				,    self.num_choices				,    -1				).contiguous()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			input_mask.unsqueeze(1				).expand(-1				,    self.num_choices				,    -1				).contiguous()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(
													    A__				,    attention_mask=A__				,    labels=A__				,    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.prepare_config_and_inputs()
													((SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE))					:      str     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					_lowerCamelCase    ,				_lowerCamelCase    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Tuple	     =				(
						    (
						        SqueezeBertModel,
						        SqueezeBertForMaskedLM,
						        SqueezeBertForMultipleChoice,
						        SqueezeBertForQuestionAnswering,
						        SqueezeBertForSequenceClassification,
						        SqueezeBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						_lowerCAmelCase     :   Optional[int]	     =				(
						    {
						        'feature-extraction': SqueezeBertModel,
						        'fill-mask': SqueezeBertForMaskedLM,
						        'question-answering': SqueezeBertForQuestionAnswering,
						        'text-classification': SqueezeBertForSequenceClassification,
						        'token-classification': SqueezeBertForTokenClassification,
						        'zero-shot': SqueezeBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   int	     =				False
						_lowerCAmelCase     :   List[str]	     =				True
						_lowerCAmelCase     :   Optional[Any]	     =				False







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			SqueezeBertModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Any     					=			ConfigTester(self				,    config_class=A__				,    dim=37				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_model(*A__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_masked_lm(*A__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_question_answering(*A__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_token_classification(*A__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A__				)







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			SqueezeBertModel.from_pretrained(A__				)
																				self.assertIsNotNone(A__				)


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







						@slow
						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(A__				)[0]
													SCREAMING_SNAKE_CASE					:      str     					=			torch.Size((1, 3)				)
													self.assertEqual(output.shape				,    A__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor([[0.6401, -0.0349, -0.6041]]				)
													self.assertTrue(torch.allclose(A__				,    A__				,    atol=1e-4				)				)









'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['image_processor', 'tokenizer']
						_lowerCAmelCase     :   Union[str, Any]	     =				'LayoutLMv3ImageProcessor'
						_lowerCAmelCase     :   List[Any]	     =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      str=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,    lowerCAmelCase__				,    )
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			kwargs.pop('''feature_extractor'''				)

													SCREAMING_SNAKE_CASE					:      Any     					=			image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																				raise ValueError('''You need to specify an `image_processor`.'''				)
													if tokenizer is None:
																				raise ValueError('''You need to specify a `tokenizer`.'''				)

													super().__init__(lowerCAmelCase__				,    lowerCAmelCase__				)







						def __call__(  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,    lowerCAmelCase__   :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,    lowerCAmelCase__   :      Union[List[List[int]], List[List[List[int]]]] = None				,    lowerCAmelCase__   :      Optional[Union[List[int], List[List[int]]]] = None				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Union[bool, str, PaddingStrategy] = False				,    lowerCAmelCase__   :      Union[bool, str, TruncationStrategy] = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      int = 0				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Optional[Union[str, TensorType]] = None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													# verify input
													if self.image_processor.apply_ocr and (boxes is not None):
																				raise ValueError(
																				    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

													if self.image_processor.apply_ocr and (word_labels is not None):
																				raise ValueError(
																				    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

													# first, apply the image processor
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processor(images=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				)

													# second, apply the tokenizer
													if text is not None and self.image_processor.apply_ocr and text_pair is None:
																				if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			features['''words''']

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.tokenizer(
													    text=text if text is not None else features['''words''']				,    text_pair=text_pair if text_pair is not None else None				,    boxes=boxes if boxes is not None else features['''boxes''']				,    word_labels=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )

													# add pixel values
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.pop('''pixel_values'''				)
													if return_overflowing_tokens is True:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.get_overflowing_images(lowerCAmelCase__				,    encoded_inputs['''overflow_to_sample_mapping''']				)
													SCREAMING_SNAKE_CASE					:      Any     					=			images

													return encoded_inputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for sample_idx in overflow_to_sample_mapping:
																				images_with_overflow.append(images[sample_idx]				)

													if len(lowerCAmelCase__				) != len(lowerCAmelCase__				):
																				raise ValueError(
																				    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																				    F""" {len(lowerCAmelCase__				)} and {len(lowerCAmelCase__				)}"""				)

													return images_with_overflow







						def 					__lowercase    (  self   :      Any				,    *lowerCAmelCase__   :      Union[str, Any]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return ["input_ids", "bbox", "attention_mask", "pixel_values"]







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor_class







						@property
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor




