
import os
import tempfile
import unittest

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

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


if is_torch_available():
					import torch

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





class 							UpperCAmelCase      :




	'''simple docstring'''





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






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size )

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

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

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

					__A   			= self.get_config()

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






	def 				UpperCamelCase_  (       self					: int ):
					return NezhaConfig(
					    vocab_size=self.vocab_size							,hidden_size=self.hidden_size							,num_hidden_layers=self.num_hidden_layers							,num_attention_heads=self.num_attention_heads							,intermediate_size=self.intermediate_size							,hidden_act=self.hidden_act							,hidden_dropout_prob=self.hidden_dropout_prob							,attention_probs_dropout_prob=self.attention_probs_dropout_prob							,max_position_embeddings=self.max_position_embeddings							,type_vocab_size=self.type_vocab_size							,is_decoder=A							,initializer_range=self.initializer_range							,)






	def 				UpperCamelCase_  (       self					: Tuple ):
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= self.prepare_config_and_inputs()

					__A   			= True
					__A   			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					__A   			= 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 				UpperCamelCase_  (       self					: str							,A					: Tuple							,A					: List[str]							,A					: List[Any]							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: Optional[int] ):
					__A   			= NezhaModel(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A							,token_type_ids=A )
					__A   			= model(A							,token_type_ids=A )
					__A   			= model(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					: Optional[int]							,A					: Optional[int]							,A					: Union[str, Any]							,A					: Any							,A					: Any							,A					: Dict							,A					: Union[str, Any]							,A					: List[str]							,A					: List[Any]							,A					: Union[str, Any]							,):
					__A   			= True
					__A   			= NezhaModel(A )
					model.to(A )
					model.eval()
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,encoder_hidden_states=A							,encoder_attention_mask=A							,)
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,encoder_hidden_states=A							,)
					__A   			= model(A							,attention_mask=A							,token_type_ids=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					: int							,A					: int							,A					: Any							,A					: List[str]							,A					: Tuple							,A					: Optional[int]							,A					: Any							,A					: Union[str, Any] ):
					__A   			= NezhaForMaskedLM(config=A )
					model.to(A )
					model.eval()
					__A   			= model(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					: Union[str, Any]							,A					: List[str]							,A					: List[Any]							,A					: Tuple							,A					: Union[str, Any]							,A					: Tuple							,A					: Union[str, Any]							,A					: List[Any] ):
					__A   			= NezhaForNextSentencePrediction(config=A )
					model.to(A )
					model.eval()
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,labels=A							,)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, 2) )






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: List[Any]							,A					: Optional[Any]							,A					: Union[str, Any]							,A					: Any							,A					: Tuple							,A					: Any ):
					__A   			= NezhaForPreTraining(config=A )
					model.to(A )
					model.eval()
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,labels=A							,next_sentence_label=A							,)
					self.parent.assertEqual(result.prediction_logits.shape							,(self.batch_size, self.seq_length, self.vocab_size) )
					self.parent.assertEqual(result.seq_relationship_logits.shape							,(self.batch_size, 2) )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: int							,A					: Any							,A					: Optional[Any]							,A					: Tuple							,A					: Optional[Any]							,A					: List[Any]							,A					: List[Any] ):
					__A   			= NezhaForQuestionAnswering(config=A )
					model.to(A )
					model.eval()
					__A   			= model(
					    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					: List[Any]							,A					: List[Any]							,A					: Optional[Any]							,A					: List[Any]							,A					: Union[str, Any]							,A					: Tuple							,A					: Tuple							,A					: List[str] ):
					__A   			= self.num_labels
					__A   			= NezhaForSequenceClassification(A )
					model.to(A )
					model.eval()
					__A   			= model(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					: str							,A					: List[Any]							,A					: Optional[Any]							,A					: Union[str, Any]							,A					: Tuple							,A					: List[str]							,A					: Union[str, Any]							,A					: Optional[Any] ):
					__A   			= self.num_labels
					__A   			= NezhaForTokenClassification(config=A )
					model.to(A )
					model.eval()
					__A   			= model(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					: Union[str, Any]							,A					: List[str]							,A					: Any							,A					: str							,A					: Optional[int]							,A					: Tuple							,A					: Union[str, Any]							,A					: Any ):
					__A   			= self.num_choices
					__A   			= NezhaForMultipleChoice(config=A )
					model.to(A )
					model.eval()
					__A   			= input_ids.unsqueeze(1 ).expand(-1							,self.num_choices							,-1 ).contiguous()
					__A   			= token_type_ids.unsqueeze(1 ).expand(-1							,self.num_choices							,-1 ).contiguous()
					__A   			= input_mask.unsqueeze(1 ).expand(-1							,self.num_choices							,-1 ).contiguous()
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,labels=A							,)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_choices) )







	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.prepare_config_and_inputs()
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= config_and_inputs
					__A   			= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (
	    (
	        NezhaModel,
	        NezhaForMaskedLM,
	        NezhaForMultipleChoice,
	        NezhaForNextSentencePrediction,
	        NezhaForPreTraining,
	        NezhaForQuestionAnswering,
	        NezhaForSequenceClassification,
	        NezhaForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_					    =     (
	    {
	        "feature-extraction": NezhaModel,
	        "fill-mask": NezhaForMaskedLM,
	        "question-answering": NezhaForQuestionAnswering,
	        "text-classification": NezhaForSequenceClassification,
	        "token-classification": NezhaForTokenClassification,
	        "zero-shot": NezhaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_					    =     True






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Any							,A					: Union[str, Any]							,A					: int=False ):
					__A   			= super()._prepare_for_class(A							,A							,return_labels=A )

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






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= NezhaModelTester(self )
					__A   			= ConfigTester(self							,config_class=A							,hidden_size=37 )






	def 				UpperCamelCase_  (       self					: str ):
					self.config_tester.run_common_tests()






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					# This regression test was failing with PyTorch < 1.3
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= self.model_tester.prepare_config_and_inputs_for_decoder()

					__A   			= None

					self.model_tester.create_and_check_model_as_decoder(
					    A							,A							,A							,A							,A							,A							,A							,A							,A							,)






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*A )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*A )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_next_sequence_prediction(*A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*A )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*A )






	@slow
	def 				UpperCamelCase_  (       self					: int ):
					for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= NezhaModel.from_pretrained(A )
									self.assertIsNotNone(A )







	@slow
	@require_torch_gpu
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
									# NezhaForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == NezhaForMultipleChoice:
													return

									__A   			= True
									__A   			= model_class(config=A )

									__A   			= self._prepare_for_class(A							,A )
									__A   			= torch.jit.trace(
									    A							,(inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )

									with tempfile.TemporaryDirectory() as tmp:
													torch.jit.save(A							,os.path.join(A							,"bert.pt" ) )
													__A   			= torch.jit.load(os.path.join(A							,"bert.pt" )							,map_location=A )
													loaded(inputs_dict["input_ids"].to(A )							,inputs_dict["attention_mask"].to(A ) )





@require_torch
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= NezhaModel.from_pretrained("sijunhe/nezha-cn-base" )
					__A   			= torch.tensor([[0, 1, 2, 3, 4, 5]] )
					__A   			= torch.tensor([[0, 1, 1, 1, 1, 1]] )
					with torch.no_grad():
									__A   			= model(A							,attention_mask=A )[0]
					__A   			= torch.Size((1, 6, 7_68) )
					self.assertEqual(output.shape							,A )
					__A   			= torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]] )

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







	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" )
					__A   			= torch.tensor([[0, 1, 2, 3, 4, 5]] )
					__A   			= torch.tensor([[1, 1, 1, 1, 1, 1]] )
					with torch.no_grad():
									__A   			= model(A							,attention_mask=A )[0]
					__A   			= torch.Size((1, 6, 2_11_28) )
					self.assertEqual(output.shape							,A )
					__A   			= torch.tensor(
					    [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]] )

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




'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					['image_processor', 'tokenizer']
    lowercase    =					'AutoImageProcessor'
    lowercase    =					'AutoTokenizer'

    def __init__(   self    :  int    ,						lowerCamelCase    :  List[str]=None    ,						lowerCamelCase    :  Union[str, Any]=None    ,						**lowerCamelCase    :  str		)   ->  Tuple:
      lowerCAmelCase_			:      str			=   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    ,						)
        lowerCAmelCase_			:      Tuple			=   kwargs.pop("""feature_extractor"""		)

      lowerCAmelCase_			:      Tuple			=   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		)
      lowerCAmelCase_			:      Optional[int]			=   self.image_processor
      lowerCAmelCase_			:      Any			=   False

    def __call__(   self    :  List[Any]    ,						*lowerCamelCase    :  str    ,						**lowerCamelCase    :  Tuple		)   ->  Union[str, Any]:
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*lowerCamelCase    ,						**lowerCamelCase		)

      lowerCAmelCase_			:      Any			=   kwargs.pop("""images"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""text"""    ,						lowerCamelCase		)
      if len(lowerCamelCase		) > 0:
        lowerCAmelCase_			:      str			=   args[0]
        lowerCAmelCase_			:      Dict			=   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:
        lowerCAmelCase_			:      Any			=   self.image_processor(lowerCamelCase    ,						*lowerCamelCase    ,						**lowerCamelCase		)
      if text is not None:
        lowerCAmelCase_			:      str			=   self.tokenizer(lowerCamelCase    ,						**lowerCamelCase		)

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

    def __lowercase   (   self    :  str    ,						*lowerCamelCase    :  Union[str, Any]    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.batch_decode(*lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  List[Any]    ,						*lowerCamelCase    :  Any    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.decode(*lowerCamelCase    ,						**lowerCamelCase		)

    @contextmanager
    def __lowercase   (   self    :  str		)   ->  Union[str, Any]:
      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."""		)
      lowerCAmelCase_			:      List[str]			=   True
      lowerCAmelCase_			:      Optional[Any]			=   self.tokenizer
      yield
      lowerCAmelCase_			:      List[Any]			=   self.image_processor
      lowerCAmelCase_			:      List[str]			=   False

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  str=False    ,						lowerCamelCase    :  List[Any]=None		)   ->  Optional[int]:
      if added_vocab is None:
        lowerCAmelCase_			:      str			=   self.tokenizer.get_added_vocab()

      lowerCAmelCase_			:      Union[str, Any]			=   {}

      while tokens:
        lowerCAmelCase_			:      Dict			=   re.search(R"""<s_(.*?)>"""    ,						lowerCamelCase    ,						re.IGNORECASE		)
        if start_token is None:
          break
        lowerCAmelCase_			:      Tuple			=   start_token.group(1		)
        lowerCAmelCase_			:      Tuple			=   re.search(RF'</s_{key}>'    ,						lowerCamelCase    ,						re.IGNORECASE		)
        lowerCAmelCase_			:      Tuple			=   start_token.group()
        if end_token is None:
          lowerCAmelCase_			:      str			=   tokens.replace(lowerCamelCase    ,						""""""		)
        else:
          lowerCAmelCase_			:      List[str]			=   end_token.group()
          lowerCAmelCase_			:      Dict			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      int			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      Dict			=   re.search(F'{start_token_escaped}(.*?){end_token_escaped}'    ,						lowerCamelCase    ,						re.IGNORECASE		)
          if content is not None:
            lowerCAmelCase_			:      str			=   content.group(1		).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowerCAmelCase_			:      str			=   self.tokenajson(lowerCamelCase    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)
              if value:
                if len(lowerCamelCase		) == 1:
                  lowerCAmelCase_			:      List[Any]			=   value[0]
                lowerCAmelCase_			:      Optional[Any]			=   value
            else:  # leaf nodes
              lowerCAmelCase_			:      List[str]			=   []
              for leaf in content.split(R"""<sep/>"""		):
                lowerCAmelCase_			:      Union[str, Any]			=   leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowerCAmelCase_			:      Any			=   leaf[1:-2]  # for categorical special tokens
                output[key].append(lowerCamelCase		)
              if len(output[key]		) == 1:
                lowerCAmelCase_			:      Optional[Any]			=   output[key][0]

          lowerCAmelCase_			:      List[Any]			=   tokens[tokens.find(lowerCamelCase		) + len(lowerCamelCase		) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)

      if len(lowerCamelCase		):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}

    @property
    def __lowercase   (   self    :  Dict		)   ->  int:
      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    :  Dict		)   ->  Optional[Any]:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor



