

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 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case=13						,							__snake_case=7						,							__snake_case=True						,							__snake_case=True						,							__snake_case=True						,							__snake_case=True						,							__snake_case=99						,							__snake_case=32						,							__snake_case=5						,							__snake_case=4						,							__snake_case=37						,							__snake_case="gelu"						,							__snake_case=0.1						,							__snake_case=0.1						,							__snake_case=5_12						,							__snake_case=16						,							__snake_case=2						,							__snake_case=0.0_2						,							__snake_case=3						,							__snake_case=4						,							__snake_case=None						,							__snake_case=10_00						,							):
			_UpperCamelCase :		Dict								=    parent
			_UpperCamelCase :		List[Any]								=    batch_size
			_UpperCamelCase :		List[Any]								=    seq_length
			_UpperCamelCase :		Dict								=    is_training
			_UpperCamelCase :		Optional[int]								=    use_input_mask
			_UpperCamelCase :		Any								=    use_token_type_ids
			_UpperCamelCase :		Optional[int]								=    use_labels
			_UpperCamelCase :		List[Any]								=    vocab_size
			_UpperCamelCase :		int								=    hidden_size
			_UpperCamelCase :		Any								=    num_hidden_layers
			_UpperCamelCase :		Optional[Any]								=    num_attention_heads
			_UpperCamelCase :		Optional[Any]								=    intermediate_size
			_UpperCamelCase :		Optional[int]								=    hidden_act
			_UpperCamelCase :		Dict								=    hidden_dropout_prob
			_UpperCamelCase :		Dict								=    attention_probs_dropout_prob
			_UpperCamelCase :		List[str]								=    max_position_embeddings
			_UpperCamelCase :		Union[str, Any]								=    type_vocab_size
			_UpperCamelCase :		Optional[Any]								=    type_sequence_label_size
			_UpperCamelCase :		List[str]								=    initializer_range
			_UpperCamelCase :		Any								=    num_labels
			_UpperCamelCase :		Dict								=    num_choices
			_UpperCamelCase :		str								=    scope
			_UpperCamelCase :		Optional[Any]								=    range_bbox


		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size)

			_UpperCamelCase :		List[Any]								=    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]:
						_UpperCamelCase :		Tuple								=    bbox[i, j, 3]
						_UpperCamelCase :		Tuple								=    bbox[i, j, 1]
						_UpperCamelCase :		Union[str, Any]								=    t
					if bbox[i, j, 2] < bbox[i, j, 0]:
						_UpperCamelCase :		List[str]								=    bbox[i, j, 2]
						_UpperCamelCase :		Union[str, Any]								=    bbox[i, j, 0]
						_UpperCamelCase :		Union[str, Any]								=    t

			_UpperCamelCase :		Optional[int]								=    None
			if self.use_input_mask:
				_UpperCamelCase :		Optional[Any]								=    ids_tensor([self.batch_size, self.seq_length]						,							vocab_size=2)

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

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

			_UpperCamelCase :		Union[str, Any]								=    self.get_config()

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


		def 		A__       (					self):
			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 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Tuple								=    LayoutLMModel(config=lowerCamelCase__)
			model.to(lowerCamelCase__)
			model.eval()
			_UpperCamelCase :		Any								=    model(lowerCamelCase__						,							lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__)
			_UpperCamelCase :		Optional[int]								=    model(lowerCamelCase__						,							lowerCamelCase__						,							token_type_ids=lowerCamelCase__)
			_UpperCamelCase :		int								=    model(lowerCamelCase__						,							lowerCamelCase__)
			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						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    LayoutLMForMaskedLM(config=lowerCamelCase__)
			model.to(lowerCamelCase__)
			model.eval()
			_UpperCamelCase :		Any								=    model(lowerCamelCase__						,							lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__						,							labels=lowerCamelCase__)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size))


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    self.num_labels
			_UpperCamelCase :		Any								=    LayoutLMForSequenceClassification(lowerCamelCase__)
			model.to(lowerCamelCase__)
			model.eval()
			_UpperCamelCase :		Union[str, Any]								=    model(
			    lowerCamelCase__						,							lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__						,							labels=lowerCamelCase__)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels))


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		int								=    self.num_labels
			_UpperCamelCase :		Optional[int]								=    LayoutLMForTokenClassification(config=lowerCamelCase__)
			model.to(lowerCamelCase__)
			model.eval()
			_UpperCamelCase :		Any								=    model(lowerCamelCase__						,							lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__						,							labels=lowerCamelCase__)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.num_labels))


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Any								=    LayoutLMForQuestionAnswering(config=lowerCamelCase__)
			model.to(lowerCamelCase__)
			model.eval()
			_UpperCamelCase :		Tuple								=    model(
			    lowerCamelCase__						,							bbox=lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__						,							start_positions=lowerCamelCase__						,							end_positions=lowerCamelCase__						,							)
			self.parent.assertEqual(result.start_logits.shape						,							(self.batch_size, self.seq_length))
			self.parent.assertEqual(result.end_logits.shape						,							(self.batch_size, self.seq_length))






		def 		A__       (					self):
			_UpperCamelCase :		str								=    self.prepare_config_and_inputs()
			(
			    _UpperCamelCase					
			) :		Optional[int]								=    config_and_inputs
			_UpperCamelCase :		Tuple								=    {
			    "input_ids": input_ids,
			    "bbox": bbox,
			    "token_type_ids": token_type_ids,
			    "attention_mask": input_mask,
			}
			return config, inputs_dict







@require_torch
class 						lowercase     (					_lowercase     ,				_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




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


		def 		A__       (					self):
			_UpperCamelCase :		Optional[int]								=    LayoutLMModelTester(self)
			_UpperCamelCase :		List[Any]								=    ConfigTester(self						,							config_class=lowerCamelCase__						,							hidden_size=37)


		def 		A__       (					self):
			self.config_tester.run_common_tests()


		def 		A__       (					self):
			_UpperCamelCase :		Any								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase__)


		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
				_UpperCamelCase :		Dict								=    type
				self.model_tester.create_and_check_model(*lowerCamelCase__)


		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__)


		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__)


		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__)






		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
		def 		A__       (					self):
			pass






def        lowerCamelCase_  (				)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]				,							device=_UpperCAmelCase						)  # noqa: E231
	_UpperCamelCase :		Dict								=    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=_UpperCAmelCase						)  # noqa: E231
	_UpperCamelCase :		List[Any]								=    torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]				,							device=_UpperCAmelCase						)  # noqa: E231
	_UpperCamelCase :		Any								=    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=_UpperCAmelCase						)  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	_UpperCamelCase :		int								=    torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]				,							device=_UpperCAmelCase						)  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		@slow
		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased').to(lowerCamelCase__)

			_UpperCamelCase :		Any								=    prepare_layoutlm_batch_inputs()

			# forward pass
			_UpperCamelCase :		Any								=    model(input_ids=lowerCamelCase__						,							bbox=lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__)

			# test the sequence output on [0, :3, :3]
			_UpperCamelCase :		Optional[Any]								=    torch.tensor(
			    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]						,							device=lowerCamelCase__						,							)

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

			# test the pooled output on [1, :3]
			_UpperCamelCase :		Union[str, Any]								=    torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]						,							device=lowerCamelCase__)

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


		@slow
		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,							num_labels=2).to(
			    lowerCamelCase__)

			_UpperCamelCase :		int								=    prepare_layoutlm_batch_inputs()

			# forward pass
			_UpperCamelCase :		List[Any]								=    model(
			    input_ids=lowerCamelCase__						,							bbox=lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__						,							labels=torch.tensor([1, 1]						,							device=lowerCamelCase__)						,							)

			# test whether we get a loss as a scalar
			_UpperCamelCase :		Optional[int]								=    outputs.loss
			_UpperCamelCase :		int								=    torch.Size([])
			self.assertEqual(loss.shape						,							lowerCamelCase__)

			# test the shape of the logits
			_UpperCamelCase :		int								=    outputs.logits
			_UpperCamelCase :		str								=    torch.Size((2, 2))
			self.assertEqual(logits.shape						,							lowerCamelCase__)


		@slow
		def 		A__       (					self):
			_UpperCamelCase :		str								=    LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,							num_labels=13).to(
			    lowerCamelCase__)

			_UpperCamelCase :		Union[str, Any]								=    prepare_layoutlm_batch_inputs()

			# forward pass
			_UpperCamelCase :		Tuple								=    model(
			    input_ids=lowerCamelCase__						,							bbox=lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__						,							labels=lowerCamelCase__)

			# 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
			_UpperCamelCase :		Tuple								=    outputs.logits
			_UpperCamelCase :		Dict								=    torch.Size((2, 25, 13))
			self.assertEqual(logits.shape						,							lowerCamelCase__)






		@slow
		def 		A__       (					self):
			_UpperCamelCase :		Any								=    LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased').to(lowerCamelCase__)

			_UpperCamelCase :		str								=    prepare_layoutlm_batch_inputs()

			# forward pass
			_UpperCamelCase :		int								=    model(input_ids=lowerCamelCase__						,							bbox=lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							token_type_ids=lowerCamelCase__)

			# test the shape of the logits
			_UpperCamelCase :		Any								=    torch.Size((2, 25))
			self.assertEqual(outputs.start_logits.shape						,							lowerCamelCase__)
			self.assertEqual(outputs.end_logits.shape						,							lowerCamelCase__)








"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




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







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





