
'''simple docstring'''



import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         LayoutLMForMaskedLM,
         LayoutLMForQuestionAnswering,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMModel,
     )

class       _a :





       """simple docstring"""




       def __init__(							self				,							A__				,							A__=13				,							A__=7				,							A__=True				,							A__=True				,							A__=True				,							A__=True				,							A__=99				,							A__=32				,							A__=5				,							A__=4				,							A__=37				,							A__="gelu"				,							A__=0.1				,							A__=0.1				,							A__=5_12				,							A__=16				,							A__=2				,							A__=0.02				,							A__=3				,							A__=4				,							A__=None				,							A__=10_00				,							)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = parent
          _SCREAMING_SNAKE_CASE          = batch_size
          _SCREAMING_SNAKE_CASE          = seq_length
          _SCREAMING_SNAKE_CASE          = is_training
          _SCREAMING_SNAKE_CASE          = use_input_mask
          _SCREAMING_SNAKE_CASE          = use_token_type_ids
          _SCREAMING_SNAKE_CASE          = use_labels
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = hidden_size
          _SCREAMING_SNAKE_CASE          = num_hidden_layers
          _SCREAMING_SNAKE_CASE          = num_attention_heads
          _SCREAMING_SNAKE_CASE          = intermediate_size
          _SCREAMING_SNAKE_CASE          = hidden_act
          _SCREAMING_SNAKE_CASE          = hidden_dropout_prob
          _SCREAMING_SNAKE_CASE          = attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE          = max_position_embeddings
          _SCREAMING_SNAKE_CASE          = type_vocab_size
          _SCREAMING_SNAKE_CASE          = type_sequence_label_size
          _SCREAMING_SNAKE_CASE          = initializer_range
          _SCREAMING_SNAKE_CASE          = num_labels
          _SCREAMING_SNAKE_CASE          = num_choices
          _SCREAMING_SNAKE_CASE          = scope
          _SCREAMING_SNAKE_CASE          = range_bbox






       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE          = ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size					)

          _SCREAMING_SNAKE_CASE          = ids_tensor([self.batch_size, self.seq_length, 4]				,							self.range_bbox					)
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]					):
             for j in range(bbox.shape[1]					):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                   _SCREAMING_SNAKE_CASE          = bbox[i, j, 3]
                   _SCREAMING_SNAKE_CASE          = bbox[i, j, 1]
                   _SCREAMING_SNAKE_CASE          = t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                   _SCREAMING_SNAKE_CASE          = bbox[i, j, 2]
                   _SCREAMING_SNAKE_CASE          = bbox[i, j, 0]
                   _SCREAMING_SNAKE_CASE          = t

          _SCREAMING_SNAKE_CASE          = None
          if self.use_input_mask:
             _SCREAMING_SNAKE_CASE          = ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2					)

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

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

          _SCREAMING_SNAKE_CASE          = self.get_config()

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






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return LayoutLMConfig(
              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				,							)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__					)			->	str:
          _SCREAMING_SNAKE_CASE          = LayoutLMModel(config=A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__				,							A__				,							attention_mask=A__				,							token_type_ids=A__					)
          _SCREAMING_SNAKE_CASE          = model(A__				,							A__				,							token_type_ids=A__					)
          _SCREAMING_SNAKE_CASE          = model(A__				,							A__					)
          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 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = LayoutLMForMaskedLM(config=A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__				,							A__				,							attention_mask=A__				,							token_type_ids=A__				,							labels=A__					)
          self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)					)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = self.num_labels
          _SCREAMING_SNAKE_CASE          = LayoutLMForSequenceClassification(A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(
              A__				,							A__				,							attention_mask=A__				,							token_type_ids=A__				,							labels=A__					)
          self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)					)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE          = self.num_labels
          _SCREAMING_SNAKE_CASE          = LayoutLMForTokenClassification(config=A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__				,							A__				,							attention_mask=A__				,							token_type_ids=A__				,							labels=A__					)
          self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)					)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = LayoutLMForQuestionAnswering(config=A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(
              A__				,							bbox=A__				,							attention_mask=A__				,							token_type_ids=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 				UpperCamelCase (							self					)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = self.prepare_config_and_inputs()
          (
              (
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							(
              _SCREAMING_SNAKE_CASE
          )       ,							
          )          = config_and_inputs
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": input_ids,
              """bbox""": bbox,
              """token_type_ids""": token_type_ids,
              """attention_mask""": input_mask,
          }
          return config, inputs_dict






@require_torch
class       _a (_lowerCamelCase     ,    _lowerCamelCase     ,    unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     (
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       SCREAMING_SNAKE_CASE					=     (
           {
               'document-question-answering': LayoutLMForQuestionAnswering,
               'feature-extraction': LayoutLMModel,
               'fill-mask': LayoutLMForMaskedLM,
               'text-classification': LayoutLMForSequenceClassification,
               'token-classification': LayoutLMForTokenClassification,
               'zero-shot': LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE					=     True






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE          = LayoutLMModelTester(self					)
          _SCREAMING_SNAKE_CASE          = ConfigTester(self				,							config_class=A__				,							hidden_size=37					)






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          self.config_tester.run_common_tests()






       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__					)






       def 				UpperCamelCase (							self					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             _SCREAMING_SNAKE_CASE          = type
             self.model_tester.create_and_check_model(*A__					)






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A__					)






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A__					)






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A__					)






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A__					)






       @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
       def 				UpperCamelCase (							self					)			->	str:
          pass






def       lowerCAmelCase_		( )   ->   str:




   """simple docstring"""
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   _SCREAMING_SNAKE_CASE          = torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]			,					device=SCREAMING_SNAKE_CASE_  )  # noqa: E231
   _SCREAMING_SNAKE_CASE          = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]			,					device=SCREAMING_SNAKE_CASE_  )  # noqa: E231
   _SCREAMING_SNAKE_CASE          = torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]			,					device=SCREAMING_SNAKE_CASE_  )  # noqa: E231
   _SCREAMING_SNAKE_CASE          = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]			,					device=SCREAMING_SNAKE_CASE_  )  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   _SCREAMING_SNAKE_CASE          = torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]			,					device=SCREAMING_SNAKE_CASE_  )  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class       _a (unittest.TestCase):





       """simple docstring"""




       @slow
       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE          = LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""					).to(A__					)

          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = prepare_layoutlm_batch_inputs()

          # forward pass
          _SCREAMING_SNAKE_CASE          = model(input_ids=A__				,							bbox=A__				,							attention_mask=A__				,							token_type_ids=A__					)

          # test the sequence output on [0, :3, :3]
          _SCREAMING_SNAKE_CASE          = torch.tensor(
              [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]				,							device=A__				,							)

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]				,							A__				,							atol=1E-3					)					)

          # test the pooled output on [1, :3]
          _SCREAMING_SNAKE_CASE          = torch.tensor([-0.6580, -0.0214, 0.8552]				,							device=A__					)

          self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]				,							A__				,							atol=1E-3					)					)






       @slow
       def 				UpperCamelCase (							self					)			->	List[str]:
          # initialize model with randomly initialized sequence classification head
          _SCREAMING_SNAKE_CASE          = LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""				,							num_labels=2					).to(
              A__					)

          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = prepare_layoutlm_batch_inputs()

          # forward pass
          _SCREAMING_SNAKE_CASE          = model(
              input_ids=A__				,							bbox=A__				,							attention_mask=A__				,							token_type_ids=A__				,							labels=torch.tensor([1, 1]				,							device=A__					)				,							)

          # test whether we get a loss as a scalar
          _SCREAMING_SNAKE_CASE          = outputs.loss
          _SCREAMING_SNAKE_CASE          = torch.Size([]					)
          self.assertEqual(loss.shape				,							A__					)

          # test the shape of the logits
          _SCREAMING_SNAKE_CASE          = outputs.logits
          _SCREAMING_SNAKE_CASE          = torch.Size((2, 2)					)
          self.assertEqual(logits.shape				,							A__					)






       @slow
       def 				UpperCamelCase (							self					)			->	List[str]:
          # initialize model with randomly initialized token classification head
          _SCREAMING_SNAKE_CASE          = LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""				,							num_labels=13					).to(
              A__					)

          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = prepare_layoutlm_batch_inputs()

          # forward pass
          _SCREAMING_SNAKE_CASE          = model(
              input_ids=A__				,							bbox=A__				,							attention_mask=A__				,							token_type_ids=A__				,							labels=A__					)

          # test the loss calculation to be around 2.65
          # expected_loss = torch.tensor(2.65, device=torch_device)

          # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
          # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

          # test the shape of the logits
          _SCREAMING_SNAKE_CASE          = outputs.logits
          _SCREAMING_SNAKE_CASE          = torch.Size((2, 25, 13)					)
          self.assertEqual(logits.shape				,							A__					)






       @slow
       def 				UpperCamelCase (							self					)			->	List[Any]:
          # initialize model with randomly initialized token classification head
          _SCREAMING_SNAKE_CASE          = LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""					).to(A__					)

          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = prepare_layoutlm_batch_inputs()

          # forward pass
          _SCREAMING_SNAKE_CASE          = model(input_ids=A__				,							bbox=A__				,							attention_mask=A__				,							token_type_ids=A__					)

          # test the shape of the logits
          _SCREAMING_SNAKE_CASE          = torch.Size((2, 25)					)
          self.assertEqual(outputs.start_logits.shape				,							A__					)
          self.assertEqual(outputs.end_logits.shape				,							A__					)













import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					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__(__A							, __A						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							@property
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



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


							@property
							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



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