code: 
  
  
  
  import unittest
  
  from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  	import torch
  
  	from transformers import (
  	    MODEL_FOR_PRETRAINING_MAPPING,
  	    FunnelBaseModel,
  	    FunnelForMaskedLM,
  	    FunnelForMultipleChoice,
  	    FunnelForPreTraining,
  	    FunnelForQuestionAnswering,
  	    FunnelForSequenceClassification,
  	    FunnelForTokenClassification,
  	    FunnelModel,
  	)
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		Any		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Dict=13		,						__UpperCAmelCase			:		Tuple=7		,						__UpperCAmelCase			:		Optional[int]=True		,						__UpperCAmelCase			:		List[str]=True		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[str]=99		,						__UpperCAmelCase			:		List[str]=[1, 1, 2]		,						__UpperCAmelCase			:		Union[str, Any]=1		,						__UpperCAmelCase			:		Any=32		,						__UpperCAmelCase			:		str=4		,						__UpperCAmelCase			:		List[str]=8		,						__UpperCAmelCase			:		Any=37		,						__UpperCAmelCase			:		Tuple="gelu_new"		,						__UpperCAmelCase			:		List[Any]=0.1		,						__UpperCAmelCase			:		int=0.1		,						__UpperCAmelCase			:		List[str]=0.0		,						__UpperCAmelCase			:		Dict=512		,						__UpperCAmelCase			:		Any=3		,						__UpperCAmelCase			:		List[str]=0.02		,						__UpperCAmelCase			:		List[Any]=3		,						__UpperCAmelCase			:		Tuple=4		,						__UpperCAmelCase			:		List[Any]=None		,						__UpperCAmelCase			:		int=False		,						)    ->				Optional[Any]:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     seq_length
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     use_input_mask
  							UpperCAmelCase_=     use_token_type_ids
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     vocab_size
  							UpperCAmelCase_=     block_sizes
  							UpperCAmelCase_=     num_decoder_layers
  							UpperCAmelCase_=     d_model
  							UpperCAmelCase_=     n_head
  							UpperCAmelCase_=     d_head
  							UpperCAmelCase_=     d_inner
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout
  							UpperCAmelCase_=     attention_dropout
  							UpperCAmelCase_=     activation_dropout
  							UpperCAmelCase_=     max_position_embeddings
  							UpperCAmelCase_=     type_vocab_size
  							UpperCAmelCase_=     2
  							UpperCAmelCase_=     num_labels
  							UpperCAmelCase_=     num_choices
  							UpperCAmelCase_=     scope
  							UpperCAmelCase_=     initializer_std
  
  							# Used in the tests to check the size of the first attention layer
  							UpperCAmelCase_=     n_head
  							# Used in the tests to check the size of the first hidden state
  							UpperCAmelCase_=     self.d_model
  							# Used in the tests to check the number of output hidden states/attentions
  							UpperCAmelCase_=     sum(self.block_sizes  ) + (0 if base else self.num_decoder_layers)
  							# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
  							# the last hidden state of the first block (which is the first hidden state of the decoder).
  							if not base:
  													UpperCAmelCase_=     self.num_hidden_layers + 2
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size  )
  
  							UpperCAmelCase_=     None
  							if self.use_input_mask:
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						vocab_size=2  )
  
  							UpperCAmelCase_=     None
  							if self.use_token_type_ids:
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size  )
  
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     None
  							if self.use_labels:
  													UpperCAmelCase_=     ids_tensor([self.batch_size]		,						self.type_sequence_label_size  )
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.num_labels  )
  													UpperCAmelCase_=     ids_tensor([self.batch_size]		,						self.num_choices  )
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						1  )
  
  							UpperCAmelCase_=     self.get_config()
  
  							return (
  							    config,
  							    input_ids,
  							    token_type_ids,
  							    input_mask,
  							    sequence_labels,
  							    token_labels,
  							    choice_labels,
  							    fake_token_labels,
  							)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				int:
  							return FunnelConfig(
  							    vocab_size=self.vocab_size		,						block_sizes=self.block_sizes		,						num_decoder_layers=self.num_decoder_layers		,						d_model=self.d_model		,						n_head=self.n_head		,						d_head=self.d_head		,						d_inner=self.d_inner		,						hidden_act=self.hidden_act		,						hidden_dropout=self.hidden_dropout		,						attention_dropout=self.attention_dropout		,						activation_dropout=self.activation_dropout		,						max_position_embeddings=self.max_position_embeddings		,						type_vocab_size=self.type_vocab_size		,						initializer_std=self.initializer_std		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						)    ->				Dict:
  							UpperCAmelCase_=     FunnelModel(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.d_model)  )
  
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.d_model)  )
  
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.d_model)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Dict		,						)    ->				Any:
  							UpperCAmelCase_=     FunnelBaseModel(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, 2, self.d_model)  )
  
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, 3, self.d_model)  )
  
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, 2, self.d_model)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		str		,						)    ->				Optional[int]:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     FunnelForPreTraining(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase  )
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[int]		,						)    ->				str:
  							UpperCAmelCase_=     FunnelForMaskedLM(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		List[Any]		,						)    ->				Dict:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     FunnelForSequenceClassification(__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase  )
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		int		,						)    ->				Tuple:
  							UpperCAmelCase_=     self.num_choices
  							UpperCAmelCase_=     FunnelForMultipleChoice(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     input_ids.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     token_type_ids.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     input_mask.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase		,						)
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						)    ->				Tuple:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     FunnelForTokenClassification(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Dict		,						)    ->				Optional[Any]:
  							UpperCAmelCase_=     FunnelForQuestionAnswering(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Optional[int]:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							(
  							    (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     
  							)=     config_and_inputs
  							UpperCAmelCase_=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
  							return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      str	     =							False
  	a__   :      int	     =							False
  	a__   :      Union[str, Any]	     =							(
  	    (
  	        FunnelModel,
  	        FunnelForMaskedLM,
  	        FunnelForPreTraining,
  	        FunnelForQuestionAnswering,
  	        FunnelForTokenClassification,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	a__   :      str	     =							(
  	    {
  	        "feature-extraction": (FunnelBaseModel, FunnelModel),
  	        "fill-mask": FunnelForMaskedLM,
  	        "question-answering": FunnelForQuestionAnswering,
  	        "text-classification": FunnelForSequenceClassification,
  	        "token-classification": FunnelForTokenClassification,
  	        "zero-shot": FunnelForSequenceClassification,
  	    }
  	    if is_torch_available()
  	    else {}
  	)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Union[str, Any]=False  )    ->				List[str]:
  							UpperCAmelCase_=     super()._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase		,						return_labels=__UpperCAmelCase  )
  
  							if return_labels:
  													if model_class in get_values(__UpperCAmelCase  ):
  																			UpperCAmelCase_=     torch.zeros(
  																			    (self.model_tester.batch_size, self.model_tester.seq_length)		,						dtype=torch.long		,						device=__UpperCAmelCase  )
  							return inputs_dict
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Dict:
  							UpperCAmelCase_=     FunnelModelTester(self  )
  							UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Any:
  							self.config_tester.run_common_tests()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				int:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Optional[Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				List[Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Any:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		List[str]  )    ->				Optional[int]:
  							if hasattr(__UpperCAmelCase		,						"""weight"""  ) and module.weight is not None:
  													module.weight.data.fill_(3  )
  							if hasattr(__UpperCAmelCase		,						"""bias"""  ) and module.bias is not None:
  													module.bias.data.fill_(3  )
  
  							for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
  													if hasattr(__UpperCAmelCase		,						__UpperCAmelCase  ) and getattr(__UpperCAmelCase		,						__UpperCAmelCase  ) is not None:
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			weight.data.fill_(3  )
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      str	     =							False
  	a__   :      List[str]	     =							False
  	a__   :      Optional[Any]	     =							(
  	    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
  	)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				List[Any]:
  							UpperCAmelCase_=     FunnelModelTester(self		,						base=__UpperCAmelCase  )
  							UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				int:
  							self.config_tester.run_common_tests()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				int:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_base_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				List[str]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				str:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  							UpperCAmelCase_=     True
  
  							for model_class in self.all_model_classes:
  													if model_class.__name__ == "FunnelBaseModel":
  																			continue
  													UpperCAmelCase_=     model_class(__UpperCAmelCase  )
  													model.to(__UpperCAmelCase  )
  													model.train()
  													UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase		,						return_labels=__UpperCAmelCase  )
  													UpperCAmelCase_=     model(**__UpperCAmelCase  ).loss
  													loss.backward()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		List[str]  )    ->				str:
  							if hasattr(__UpperCAmelCase		,						"""weight"""  ) and module.weight is not None:
  													module.weight.data.fill_(3  )
  							if hasattr(__UpperCAmelCase		,						"""bias"""  ) and module.bias is not None:
  													module.bias.data.fill_(3  )
  
  							for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
  													if hasattr(__UpperCAmelCase		,						__UpperCAmelCase  ) and getattr(__UpperCAmelCase		,						__UpperCAmelCase  ) is not None:
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			weight.data.fill_(3  )
  
  
  
  
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				Dict:
  							UpperCAmelCase_=     13
  							UpperCAmelCase_=     7
  							UpperCAmelCase_=     torch.arange(0		,						batch_size * sequence_length  ).long().reshape(__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
  							UpperCAmelCase_=     torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]  )
  
  							UpperCAmelCase_=     FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""  )
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )[0].abs()
  
  							UpperCAmelCase_=     torch.tensor(2_344.8_352  )
  							UpperCAmelCase_=     torch.tensor(0.8_052  )
  							self.assertTrue(torch.allclose(output.sum()		,						__UpperCAmelCase		,						atol=1E-4  )  )
  							self.assertTrue(torch.allclose(output.mean()		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  							UpperCAmelCase_=     torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]  )
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )[0].abs()
  
  							UpperCAmelCase_=     torch.tensor(2_343.8_425  )
  							UpperCAmelCase_=     torch.tensor(0.8_049  )
  							self.assertTrue(torch.allclose(output.sum()		,						__UpperCAmelCase		,						atol=1E-4  )  )
  							self.assertTrue(torch.allclose(output.mean()		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Tuple:
  							UpperCAmelCase_=     FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""  )
  							UpperCAmelCase_=     FunnelModel.from_pretrained("""huggingface/funnel-small"""  )
  							UpperCAmelCase_=     tokenizer("""Hello! I am the Funnel Transformer model."""		,						return_tensors="""pt"""  )
  							UpperCAmelCase_=     model(**__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     torch.tensor(235.7_246  )
  							UpperCAmelCase_=     torch.tensor(0.0_256  )
  							self.assertTrue(torch.allclose(output.sum()		,						__UpperCAmelCase		,						atol=1E-4  )  )
  							self.assertTrue(torch.allclose(output.mean()		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  
style_context: 
  
  
  
  import copy
  from typing import Dict
  
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Optional[int]	     =							"semantic"
  
  
  
  
  	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     text_encoding_offset
  							UpperCAmelCase_=     text_pad_token
  							UpperCAmelCase_=     eos_token_id
  							UpperCAmelCase_=     semantic_infer_token
  							UpperCAmelCase_=     semantic_vocab_size
  							UpperCAmelCase_=     max_input_semantic_length
  							UpperCAmelCase_=     semantic_rate_hz
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"coarse_acoustics"
  
  
  
  
  	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     coarse_semantic_pad_token
  							UpperCAmelCase_=     coarse_rate_hz
  							UpperCAmelCase_=     n_coarse_codebooks
  							UpperCAmelCase_=     coarse_infer_token
  							UpperCAmelCase_=     max_coarse_input_length
  							UpperCAmelCase_=     max_coarse_history
  							UpperCAmelCase_=     sliding_window_len
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"fine_acoustics"
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
  							super().__init__(temperature=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     max_fine_history_length
  							UpperCAmelCase_=     max_fine_input_length
  							UpperCAmelCase_=     n_fine_codebooks
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"bark"
  	a__   :      List[str]	     =							True
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
  							if semantic_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )
  
  							if coarse_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )
  
  							if fine_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )
  
  							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )
  
  							UpperCAmelCase_=     sample_rate
  							UpperCAmelCase_=     codebook_size
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				Any:
  							return cls(
  							    semantic_config=semantic_config.to_dict()		,						coarse_acoustics_config=coarse_acoustics_config.to_dict()		,						fine_acoustics_config=fine_acoustics_config.to_dict()		,						**__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
  							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )
  
  							UpperCAmelCase_=     self.semantic_config.to_dict()
  							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
  							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()
  
  							UpperCAmelCase_=     self.__class__.model_type
  							return output
  
  
  
  
  