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: 
  
  
  
  from __future__ import annotations
  
  import os
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[str]	     =							(
  	    "High card",
  	    "One pair",
  	    "Two pairs",
  	    "Three of a kind",
  	    "Straight",
  	    "Flush",
  	    "Full house",
  	    "Four of a kind",
  	    "Straight flush",
  	    "Royal flush",
  	)
  
  	a__   :      Optional[Any]	     =							(
  	    "",  # placeholder as tuples are zero-indexed
  	    "One",
  	    "Two",
  	    "Three",
  	    "Four",
  	    "Five",
  	    "Six",
  	    "Seven",
  	    "Eight",
  	    "Nine",
  	    "Ten",
  	    "Jack",
  	    "Queen",
  	    "King",
  	    "Ace",
  	)
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		str  )    ->				None:
  							if not isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													UpperCAmelCase_=     F"""Hand should be of type 'str': {hand!r}"""
  													raise TypeError(__UpperCAmelCase  )
  							# split removes duplicate whitespaces so no need of strip
  							if len(hand.split(""" """  )  ) != 5:
  													UpperCAmelCase_=     F"""Hand should contain only 5 cards: {hand!r}"""
  													raise ValueError(__UpperCAmelCase  )
  							UpperCAmelCase_=     hand
  							UpperCAmelCase_=     0
  							UpperCAmelCase_=     0
  							UpperCAmelCase_,     UpperCAmelCase_=     self._internal_state()
  							UpperCAmelCase_=     self._get_hand_type()
  							UpperCAmelCase_=     self._card_values[0]
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Union[str, Any]:
  							return self._hand
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		PokerHand  )    ->				str:
  							# Breaking the tie works on the following order of precedence:
  							# 1. First pair (default 0)
  							# 2. Second pair (default 0)
  							# 3. Compare all cards in reverse order because they are sorted.
  
  							# First pair and second pair will only be a non-zero value if the card
  							# type is either from the following:
  							# 21: Four of a kind
  							# 20: Full house
  							# 17: Three of a kind
  							# 16: Two pairs
  							# 15: One pair
  							if self._hand_type > other._hand_type:
  													return "Win"
  							elif self._hand_type < other._hand_type:
  													return "Loss"
  							elif self._first_pair == other._first_pair:
  													if self._second_pair == other._second_pair:
  																			return self._compare_cards(__UpperCAmelCase  )
  													else:
  																			return "Win" if self._second_pair > other._second_pair else "Loss"
  							return "Win" if self._first_pair > other._first_pair else "Loss"
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				str:
  							UpperCAmelCase_=     PokerHand._HAND_NAME[self._hand_type - 14]
  							UpperCAmelCase_=     PokerHand._CARD_NAME[self._high_card]
  							UpperCAmelCase_=     PokerHand._CARD_NAME[self._first_pair]
  							UpperCAmelCase_=     PokerHand._CARD_NAME[self._second_pair]
  							if self._hand_type in [22, 19, 18]:
  													return name + F""", {high}-high"""
  							elif self._hand_type in [21, 17, 15]:
  													return name + F""", {paira}s"""
  							elif self._hand_type in [20, 16]:
  													UpperCAmelCase_=     """over""" if self._hand_type == 20 else """and"""
  													return name + F""", {paira}s {join} {paira}s"""
  							elif self._hand_type == 23:
  													return name
  							else:
  													return name + F""", {high}"""
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		PokerHand  )    ->				str:
  							# Enumerate gives us the index as well as the element of a list
  							for index, card_value in enumerate(self._card_values  ):
  													if card_value != other._card_values[index]:
  																			return "Win" if card_value > other._card_values[index] else "Loss"
  							return "Tie"
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				int:
  							# Number representing the type of hand internally:
  							# 23: Royal flush
  							# 22: Straight flush
  							# 21: Four of a kind
  							# 20: Full house
  							# 19: Flush
  							# 18: Straight
  							# 17: Three of a kind
  							# 16: Two pairs
  							# 15: One pair
  							# 14: High card
  							if self._is_flush():
  													if self._is_five_high_straight() or self._is_straight():
  																			return 23 if sum(self._card_values  ) == 60 else 22
  													return 19
  							elif self._is_five_high_straight() or self._is_straight():
  													return 18
  							return 14 + self._is_same_kind()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				bool:
  							return len(self._card_suit  ) == 1
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				bool:
  							# If a card is a five high straight (low ace) change the location of
  							# ace from the start of the list to the end. Check whether the first
  							# element is ace or not. (Don't want to change again)
  							# Five high straight (low ace): AH 2H 3S 4C 5D
  							# Why use sorted here? One call to this function will mutate the list to
  							# [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
  							# need to compare the sorted version.
  							# Refer test_multiple_calls_five_high_straight in test_poker_hand.py
  							if sorted(self._card_values  ) == [2, 3, 4, 5, 14]:
  													if self._card_values[0] == 14:
  																			# Remember, our list is sorted in reverse order
  																			UpperCAmelCase_=     self._card_values.pop(0  )
  																			self._card_values.append(__UpperCAmelCase  )
  													return True
  							return False
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				bool:
  							for i in range(4  ):
  													if self._card_values[i] - self._card_values[i + 1] != 1:
  																			return False
  							return True
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				int:
  							# Kind Values for internal use:
  							# 7: Four of a kind
  							# 6: Full house
  							# 3: Three of a kind
  							# 2: Two pairs
  							# 1: One pair
  							# 0: False
  							UpperCAmelCase_=     UpperCAmelCase_=     UpperCAmelCase_=     0
  							for i in range(4  ):
  													# Compare two cards at a time, if they are same increase 'kind',
  													# add the value of the card to val1, if it is repeating again we
  													# will add 2 to 'kind' as there are now 3 cards with same value.
  													# If we get card of different value than val1, we will do the same
  													# thing with val2
  													if self._card_values[i] == self._card_values[i + 1]:
  																			if not vala:
  																									UpperCAmelCase_=     self._card_values[i]
  																									kind += 1
  																			elif vala == self._card_values[i]:
  																									kind += 2
  																			elif not vala:
  																									UpperCAmelCase_=     self._card_values[i]
  																									kind += 1
  																			elif vala == self._card_values[i]:
  																									kind += 2
          # For consistency in hand type (look at note in _get_hand_type function)
  							UpperCAmelCase_=     kind + 2 if kind in [4, 5] else kind
  							# first meaning first pair to compare in 'compare_with'
  							UpperCAmelCase_=     max(__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     min(__UpperCAmelCase		,						__UpperCAmelCase  )
  							# If it's full house (three count pair + two count pair), make sure
  							# first pair is three count and if not then switch them both.
  							if kind == 6 and self._card_values.count(__UpperCAmelCase  ) != 3:
  													UpperCAmelCase_,     UpperCAmelCase_=     second, first
  							UpperCAmelCase_=     first
  							UpperCAmelCase_=     second
  							return kind
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				tuple[list[int], set[str]]:
  							# Internal representation of hand as a list of card values and
  							# a set of card suit
  							UpperCAmelCase_=     {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
  							UpperCAmelCase_=     self._hand.translate(str.maketrans(__UpperCAmelCase  )  ).split()
  							UpperCAmelCase_=     [int(card[:-1]  ) for card in new_hand]
  							UpperCAmelCase_=     {card[-1] for card in new_hand}
  							return sorted(__UpperCAmelCase		,						reverse=__UpperCAmelCase  ), card_suit
  
  
  
  
  	def __repr__(			self			:		Dict  )    ->				Dict:
  							return F"""{self.__class__}(\"{self._hand}\")"""
  
  
  
  
  	def __str__(			self			:		Tuple  )    ->				List[Any]:
  							return self._hand
  
  
  
  
  	def __eq__(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[Any]  )    ->				int:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return self.compare_with(__UpperCAmelCase  ) == "Tie"
  							return NotImplemented
  
  
  
  
  	def __lt__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[int]  )    ->				Union[str, Any]:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return self.compare_with(__UpperCAmelCase  ) == "Loss"
  							return NotImplemented
  
  
  
  
  	def __le__(			self			:		List[str]		,						__UpperCAmelCase			:		Optional[int]  )    ->				Optional[Any]:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return self < other or self == other
  							return NotImplemented
  
  
  
  
  	def __gt__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]  )    ->				int:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return not self < other and self != other
  							return NotImplemented
  
  
  
  
  	def __ge__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Tuple  )    ->				Any:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return not self < other
  							return NotImplemented
  
  
  
  
  	def __hash__(			self			:		Any  )    ->				int:
  							return object.__hash__(self  )
  
  
  
  def  __a  (					)   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     0
  						UpperCAmelCase_=     os.path.abspath(os.path.dirname(lowerCAmelCase_       )       )
  						UpperCAmelCase_=     os.path.join(lowerCAmelCase_    ,"""poker_hands.txt"""       )
  						with open(lowerCAmelCase_       ) as file_hand:
  												for line in file_hand:
  																		UpperCAmelCase_=     line[:14].strip()
  																		UpperCAmelCase_=     line[15:].strip()
  																		UpperCAmelCase_,     UpperCAmelCase_=     PokerHand(lowerCAmelCase_       ), PokerHand(lowerCAmelCase_       )
  																		UpperCAmelCase_=     player.compare_with(lowerCAmelCase_       )
  																		if output == "Win":
  																								answer += 1
  						return answer
  
  
  if __name__ == "__main__":
  	solution()
  
  
  
  
  