
import unittest

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

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,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				)
				from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST




class 	UpperCAmelCase_      :


		'''simple docstring'''







		def __init__(			self      :			List[Any]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[Any]=13      ,							UpperCamelCase__      :			List[str]=7      ,							UpperCamelCase__      :			int=True      ,							UpperCamelCase__      :			List[str]=True      ,							UpperCamelCase__      :			Tuple=True      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			int=99      ,							UpperCamelCase__      :			List[str]=32      ,							UpperCamelCase__      :			str=5      ,							UpperCamelCase__      :			Tuple=4      ,							UpperCamelCase__      :			str=37      ,							UpperCamelCase__      :			List[str]="gelu"      ,							UpperCamelCase__      :			Optional[int]=0.1      ,							UpperCamelCase__      :			Optional[int]=0.1      ,							UpperCamelCase__      :			Dict=512      ,							UpperCamelCase__      :			Any=16      ,							UpperCamelCase__      :			Tuple=2      ,							UpperCamelCase__      :			List[Any]=0.02      ,							UpperCamelCase__      :			Any=3      ,							UpperCamelCase__      :			Optional[Any]=4      ,							UpperCamelCase__      :			List[str]=None      ,							)      ->     List[str]:







				"""simple docstring"""






				__magic_name__    					=	parent
				__magic_name__    					=	batch_size
				__magic_name__    					=	seq_length
				__magic_name__    					=	is_training
				__magic_name__    					=	use_input_mask
				__magic_name__    					=	use_token_type_ids
				__magic_name__    					=	use_labels
				__magic_name__    					=	vocab_size
				__magic_name__    					=	hidden_size
				__magic_name__    					=	num_hidden_layers
				__magic_name__    					=	num_attention_heads
				__magic_name__    					=	intermediate_size
				__magic_name__    					=	hidden_act
				__magic_name__    					=	hidden_dropout_prob
				__magic_name__    					=	attention_probs_dropout_prob
				__magic_name__    					=	max_position_embeddings
				__magic_name__    					=	type_vocab_size
				__magic_name__    					=	type_sequence_label_size
				__magic_name__    					=	initializer_range
				__magic_name__    					=	num_labels
				__magic_name__    					=	num_choices
				__magic_name__    					=	scope
		def 							_lowercase							(			self      :			Tuple   )      ->     str:







				"""simple docstring"""






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

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

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

				__magic_name__    					=	None
				__magic_name__    					=	None
				__magic_name__    					=	None
				if self.use_labels:
						__magic_name__    					=	ids_tensor([self.batch_size]      ,							self.type_sequence_label_size   )
						__magic_name__    					=	ids_tensor([self.batch_size, self.seq_length]      ,							self.num_labels   )
						__magic_name__    					=	ids_tensor([self.batch_size]      ,							self.num_choices   )
						__magic_name__    					=	ids_tensor([self.batch_size, self.seq_length]      ,							1   )

				__magic_name__    					=	self.get_config()

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    fake_token_labels,
				)
		def 							_lowercase							(			self      :			List[Any]   )      ->     Tuple:







				"""simple docstring"""






				return ElectraConfig(
				    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=UpperCamelCase__      ,							initializer_range=self.initializer_range      ,							)
		def 							_lowercase							(			self      :			Optional[int]   )      ->     int:







				"""simple docstring"""






				(
				    (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,   (
				    __magic_name__   
				)					,      
				)    					=	self.prepare_config_and_inputs()

				__magic_name__    					=	True
				__magic_name__    					=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
				__magic_name__    					=	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 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			str      ,							)      ->     Tuple:







				"""simple docstring"""






				__magic_name__    					=	ElectraModel(config=UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__   )
				__magic_name__    					=	model(UpperCamelCase__      ,							token_type_ids=UpperCamelCase__   )
				__magic_name__    					=	model(UpperCamelCase__   )
				self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)   )
		def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			int      ,							)      ->     Optional[int]:







				"""simple docstring"""






				__magic_name__    					=	True
				__magic_name__    					=	ElectraModel(UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(
				    UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							encoder_hidden_states=UpperCamelCase__      ,							encoder_attention_mask=UpperCamelCase__      ,							)
				__magic_name__    					=	model(
				    UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							encoder_hidden_states=UpperCamelCase__      ,							)
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__   )
				self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)   )
		def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Optional[int]      ,							)      ->     str:







				"""simple docstring"""






				__magic_name__    					=	ElectraForMaskedLM(config=UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							labels=UpperCamelCase__   )
				self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)   )
		def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			List[str]      ,							)      ->     Union[str, Any]:







				"""simple docstring"""






				__magic_name__    					=	ElectraForCausalLM(config=UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							labels=UpperCamelCase__   )
				self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)   )
		def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Optional[int]      ,							)      ->     int:







				"""simple docstring"""






				__magic_name__    					=	self.num_labels
				__magic_name__    					=	ElectraForTokenClassification(config=UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							labels=UpperCamelCase__   )
				self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.num_labels)   )
		def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Tuple      ,							)      ->     str:







				"""simple docstring"""






				__magic_name__    					=	self.num_labels
				__magic_name__    					=	ElectraForPreTraining(config=UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							labels=UpperCamelCase__   )
				self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length)   )
		def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Optional[int]      ,							)      ->     List[Any]:







				"""simple docstring"""






				__magic_name__    					=	self.num_labels
				__magic_name__    					=	ElectraForSequenceClassification(UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							labels=UpperCamelCase__   )
				self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)   )
		def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			str      ,							)      ->     Any:







				"""simple docstring"""






				__magic_name__    					=	ElectraForQuestionAnswering(config=UpperCamelCase__   )
				model.to(UpperCamelCase__   )
				model.eval()
				__magic_name__    					=	model(
				    UpperCamelCase__      ,							attention_mask=UpperCamelCase__      ,							token_type_ids=UpperCamelCase__      ,							start_positions=UpperCamelCase__      ,							end_positions=UpperCamelCase__      ,							)
				self.parent.assertEqual(result.start_logits.shape      ,							(self.batch_size, self.seq_length)   )
				self.parent.assertEqual(result.end_logits.shape      ,							(self.batch_size, self.seq_length)   )
		def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			List[Any]      ,							)      ->     Dict:







				"""simple docstring"""






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





		def 							_lowercase							(			self      :			List[str]   )      ->     int:







				"""simple docstring"""






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







@require_torch
class 	UpperCAmelCase_      (			_A  ,	_A  ,	unittest.TestCase					):


		'''simple docstring'''







		a__   	=		(
		    (
		        ElectraModel,
		        ElectraForPreTraining,
		        ElectraForMaskedLM,
		        ElectraForCausalLM,
		        ElectraForMultipleChoice,
		        ElectraForTokenClassification,
		        ElectraForSequenceClassification,
		        ElectraForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		a__   	=		(
		    {
		        """feature-extraction""": ElectraModel,
		        """fill-mask""": ElectraForMaskedLM,
		        """question-answering""": ElectraForQuestionAnswering,
		        """text-classification""": ElectraForSequenceClassification,
		        """text-generation""": ElectraForCausalLM,
		        """token-classification""": ElectraForTokenClassification,
		        """zero-shot""": ElectraForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		a__   	=		True
		def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			List[str]=False   )      ->     List[str]:







				"""simple docstring"""






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

				if return_labels:
						if model_class in get_values(UpperCamelCase__   ):
								__magic_name__    					=	torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.seq_length)      ,							dtype=torch.long      ,							device=UpperCamelCase__   )
				return inputs_dict
		def 							_lowercase							(			self      :			Optional[Any]   )      ->     Dict:







				"""simple docstring"""






				__magic_name__    					=	ElectraModelTester(self   )
				__magic_name__    					=	ConfigTester(self      ,							config_class=UpperCamelCase__      ,							hidden_size=37   )
		def 							_lowercase							(			self      :			List[str]   )      ->     List[str]:







				"""simple docstring"""






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







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_model(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			str   )      ->     Union[str, Any]:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_electra_model_as_decoder(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			Optional[Any]   )      ->     List[Any]:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						__magic_name__    					=	type
						self.model_tester.create_and_check_electra_model(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			int   )      ->     Tuple:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_for_masked_lm(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			Tuple   )      ->     str:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_for_token_classification(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			List[str]   )      ->     Union[str, Any]:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_for_pretraining(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			List[Any]   )      ->     int:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_for_sequence_classification(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			Any   )      ->     Any:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_for_question_answering(*UpperCamelCase__   )
		def 							_lowercase							(			self      :			Any   )      ->     Any:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_electra_for_multiple_choice(*UpperCamelCase__   )
		@slow
		def 							_lowercase							(			self      :			Tuple   )      ->     Optional[int]:







				"""simple docstring"""






				for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__magic_name__    					=	ElectraModel.from_pretrained(UpperCamelCase__   )
						self.assertIsNotNone(UpperCamelCase__   )





		def 							_lowercase							(			self      :			str   )      ->     Optional[Any]:







				"""simple docstring"""






				__magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_electra_for_causal_lm(*UpperCamelCase__   )







@require_torch
class 	UpperCAmelCase_      (			unittest.TestCase					):


		'''simple docstring'''







		@slow
		def 							_lowercase							(			self      :			Union[str, Any]   )      ->     List[str]:







				"""simple docstring"""






				__magic_name__    					=	ElectraModel.from_pretrained("""google/electra-small-discriminator"""   )
				__magic_name__    					=	torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
				__magic_name__    					=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
				__magic_name__    					=	model(UpperCamelCase__      ,							attention_mask=UpperCamelCase__   )[0]
				__magic_name__    					=	torch.Size((1, 11, 256)   )
				self.assertEqual(output.shape      ,							UpperCamelCase__   )
				__magic_name__    					=	torch.tensor(
				    [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]   )

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



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 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""LayoutLMv2ImageProcessor"""
  a__   	=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  def __init__(			self      :			int      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			int=None      ,							**UpperCamelCase__      :			str   )      ->     Union[str, Any]:







    """simple docstring"""






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

    __magic_name__    					=	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__(UpperCamelCase__      ,							UpperCamelCase__   )
  def __call__(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__      :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							UpperCamelCase__      :			Union[List[List[int]], List[List[List[int]]]] = None      ,							UpperCamelCase__      :			Optional[Union[List[int], List[List[int]]]] = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			int = 0      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     BatchEncoding:







    """simple docstring"""






    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."""   )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

    # first, apply the image processor
    __magic_name__    					=	self.image_processor(images=UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__    					=	features["""words"""]

    __magic_name__    					=	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=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

    # add pixel values
    __magic_name__    					=	features.pop("""pixel_values"""   )
    if return_overflowing_tokens is True:
      __magic_name__    					=	self.get_overflowing_images(UpperCamelCase__      ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
    __magic_name__    					=	images

    return encoded_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]   )

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

    return images_with_overflow
  def 							_lowercase							(			self      :			Optional[int]      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






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





  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






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

