code: 
  
  
  
  
  """simple docstring"""
  
  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 			lowerCamelCase__    :
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=13				, SCREAMING_SNAKE_CASE=7				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=99				, SCREAMING_SNAKE_CASE=[1, 1, 2]				, SCREAMING_SNAKE_CASE=1				, SCREAMING_SNAKE_CASE=32				, SCREAMING_SNAKE_CASE=4				, SCREAMING_SNAKE_CASE=8				, SCREAMING_SNAKE_CASE=37				, SCREAMING_SNAKE_CASE="gelu_new"				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=0.0				, SCREAMING_SNAKE_CASE=512				, SCREAMING_SNAKE_CASE=3				, SCREAMING_SNAKE_CASE=0.02				, SCREAMING_SNAKE_CASE=3				, SCREAMING_SNAKE_CASE=4				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=False				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				parent
  					snake_case		:						int								=				batch_size
  					snake_case		:						Tuple								=				seq_length
  					snake_case		:						Optional[int]								=				is_training
  					snake_case		:						Any								=				use_input_mask
  					snake_case		:						Any								=				use_token_type_ids
  					snake_case		:						Optional[Any]								=				use_labels
  					snake_case		:						List[str]								=				vocab_size
  					snake_case		:						List[str]								=				block_sizes
  					snake_case		:						Optional[int]								=				num_decoder_layers
  					snake_case		:						Optional[int]								=				d_model
  					snake_case		:						List[Any]								=				n_head
  					snake_case		:						List[str]								=				d_head
  					snake_case		:						List[str]								=				d_inner
  					snake_case		:						str								=				hidden_act
  					snake_case		:						Union[str, Any]								=				hidden_dropout
  					snake_case		:						List[str]								=				attention_dropout
  					snake_case		:						Optional[int]								=				activation_dropout
  					snake_case		:						Any								=				max_position_embeddings
  					snake_case		:						Optional[int]								=				type_vocab_size
  					snake_case		:						Union[str, Any]								=				2
  					snake_case		:						int								=				num_labels
  					snake_case		:						Tuple								=				num_choices
  					snake_case		:						Any								=				scope
  					snake_case		:						Any								=				initializer_std
  
  					# Used in the tests to check the size of the first attention layer
  					snake_case		:						int								=				n_head
  					# Used in the tests to check the size of the first hidden state
  					snake_case		:						str								=				self.d_model
  					# Used in the tests to check the number of output hidden states/attentions
  					snake_case		:						Optional[int]								=				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:
  							snake_case		:						Union[str, Any]								=				self.num_hidden_layers + 2
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size							)
  
  					snake_case		:						str								=				None
  					if self.use_input_mask:
  							snake_case		:						List[str]								=				ids_tensor([self.batch_size, self.seq_length]				, vocab_size=2							)
  
  					snake_case		:						Any								=				None
  					if self.use_token_type_ids:
  							snake_case		:						Dict								=				ids_tensor([self.batch_size, self.seq_length]				, self.type_vocab_size							)
  
  					snake_case		:						List[str]								=				None
  					snake_case		:						Tuple								=				None
  					snake_case		:						Optional[Any]								=				None
  					if self.use_labels:
  							snake_case		:						int								=				ids_tensor([self.batch_size]				, self.type_sequence_label_size							)
  							snake_case		:						int								=				ids_tensor([self.batch_size, self.seq_length]				, self.num_labels							)
  							snake_case		:						Any								=				ids_tensor([self.batch_size]				, self.num_choices							)
  							snake_case		:						Optional[int]								=				ids_tensor([self.batch_size, self.seq_length]				, 1							)
  
  					snake_case		:						Dict								=				self.get_config()
  
  					return (
  					    config,
  					    input_ids,
  					    token_type_ids,
  					    input_mask,
  					    sequence_labels,
  					    token_labels,
  					    choice_labels,
  					    fake_token_labels,
  					)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					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 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				FunnelModel(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						List[str]								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Union[str, Any]								=				model(SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE							)
  					snake_case		:						int								=				model(SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.d_model)							)
  
  					snake_case		:						Dict								=				False
  					snake_case		:						List[str]								=				model(SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.d_model)							)
  
  					snake_case		:						Optional[int]								=				False
  					snake_case		:						Dict								=				model(SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.d_model)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				FunnelBaseModel(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						Union[str, Any]								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[str]								=				model(SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE							)
  					snake_case		:						int								=				model(SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, 2, self.d_model)							)
  
  					snake_case		:						List[str]								=				False
  					snake_case		:						Tuple								=				model(SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, 3, self.d_model)							)
  
  					snake_case		:						List[str]								=				False
  					snake_case		:						Union[str, Any]								=				model(SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, 2, self.d_model)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.num_labels
  					snake_case		:						str								=				FunnelForPreTraining(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						Any								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE				, labels=SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				FunnelForMaskedLM(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						Union[str, Any]								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE				, labels=SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				self.num_labels
  					snake_case		:						Any								=				FunnelForSequenceClassification(SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						List[Any]								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE				, labels=SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				self.num_choices
  					snake_case		:						Optional[int]								=				FunnelForMultipleChoice(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						Dict								=				input_ids.unsqueeze(1							).expand(-1				, self.num_choices				, -1							).contiguous()
  					snake_case		:						Any								=				token_type_ids.unsqueeze(1							).expand(-1				, self.num_choices				, -1							).contiguous()
  					snake_case		:						Any								=				input_mask.unsqueeze(1							).expand(-1				, self.num_choices				, -1							).contiguous()
  					snake_case		:						Tuple								=				model(
  					    SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE				, labels=SCREAMING_SNAKE_CASE				, )
  					self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_choices)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				self.num_labels
  					snake_case		:						List[Any]								=				FunnelForTokenClassification(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						Union[str, Any]								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE				, labels=SCREAMING_SNAKE_CASE							)
  					self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				FunnelForQuestionAnswering(config=SCREAMING_SNAKE_CASE							)
  					model.to(SCREAMING_SNAKE_CASE							)
  					model.eval()
  					snake_case		:						Any								=				model(
  					    SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE				, start_positions=SCREAMING_SNAKE_CASE				, end_positions=SCREAMING_SNAKE_CASE				, )
  					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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				self.prepare_config_and_inputs()
  					(
  					    (
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				(
  					    snake_case 
  					)			,				 
  					)		:						List[str]								=				config_and_inputs
  					snake_case		:						Union[str, Any]								=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
  					return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 			lowerCamelCase__    (  snake_case_				,  snake_case_				,  unittest.TestCase			):
  			a__ :      Any								=     False
  			a__ :      List[Any]								=     False
  			a__ :      Dict								=     (
  			    (
  			        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 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=False							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				super()._prepare_for_class(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, return_labels=SCREAMING_SNAKE_CASE							)
  
  					if return_labels:
  							if model_class in get_values(SCREAMING_SNAKE_CASE							):
  									snake_case		:						List[Any]								=				torch.zeros(
  									    (self.model_tester.batch_size, self.model_tester.seq_length)				, dtype=torch.long				, device=SCREAMING_SNAKE_CASE							)
  					return inputs_dict
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				FunnelModelTester(self							)
  					snake_case		:						List[Any]								=				ConfigTester(self				, config_class=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					self.config_tester.run_common_tests()
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[str]								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE							)
  
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if hasattr(SCREAMING_SNAKE_CASE				, "weight"							) and module.weight is not None:
  							module.weight.data.fill_(3							)
  					if hasattr(SCREAMING_SNAKE_CASE				, "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(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) and getattr(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) is not None:
  									snake_case		:						int								=				getattr(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  									weight.data.fill_(3							)
  
  
  
  
  
  
  
  @require_torch
  class 			lowerCamelCase__    (  snake_case_				,  unittest.TestCase			):
  			a__ :      str								=     False
  			a__ :      Dict								=     False
  			a__ :      str								=     (
  			    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
  			)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				FunnelModelTester(self				, base=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				ConfigTester(self				, config_class=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					self.config_tester.run_common_tests()
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_base_model(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case			,				snake_case		:						Any								=				self.model_tester.prepare_config_and_inputs_for_common()
  					snake_case		:						Union[str, Any]								=				True
  
  					for model_class in self.all_model_classes:
  							if model_class.__name__ == "FunnelBaseModel":
  									continue
  							snake_case		:						Optional[Any]								=				model_class(SCREAMING_SNAKE_CASE							)
  							model.to(SCREAMING_SNAKE_CASE							)
  							model.train()
  							snake_case		:						List[str]								=				self._prepare_for_class(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, return_labels=SCREAMING_SNAKE_CASE							)
  							snake_case		:						List[Any]								=				model(**SCREAMING_SNAKE_CASE							).loss
  							loss.backward()
  
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if hasattr(SCREAMING_SNAKE_CASE				, "weight"							) and module.weight is not None:
  							module.weight.data.fill_(3							)
  					if hasattr(SCREAMING_SNAKE_CASE				, "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(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) and getattr(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) is not None:
  									snake_case		:						Optional[Any]								=				getattr(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  									weight.data.fill_(3							)
  
  
  
  
  
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				13
  					snake_case		:						Union[str, Any]								=				7
  					snake_case		:						Dict								=				torch.arange(0				, batch_size * sequence_length							).long().reshape(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[Any]								=				[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
  					snake_case		:						Optional[Any]								=				torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]							)
  
  					snake_case		:						Dict								=				FunnelModel.from_pretrained("sgugger/funnel-random-tiny"							)
  					snake_case		:						Union[str, Any]								=				model(SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE							)[0].abs()
  
  					snake_case		:						Any								=				torch.tensor(23_44.83_52							)
  					snake_case		:						Tuple								=				torch.tensor(0.80_52							)
  					self.assertTrue(torch.allclose(output.sum()				, SCREAMING_SNAKE_CASE				, atol=1E-4							)							)
  					self.assertTrue(torch.allclose(output.mean()				, SCREAMING_SNAKE_CASE				, atol=1E-4							)							)
  
  					snake_case		:						Dict								=				torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]							)
  					snake_case		:						Tuple								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, token_type_ids=SCREAMING_SNAKE_CASE							)[0].abs()
  
  					snake_case		:						int								=				torch.tensor(23_43.84_25							)
  					snake_case		:						Optional[int]								=				torch.tensor(0.80_49							)
  					self.assertTrue(torch.allclose(output.sum()				, SCREAMING_SNAKE_CASE				, atol=1E-4							)							)
  					self.assertTrue(torch.allclose(output.mean()				, SCREAMING_SNAKE_CASE				, atol=1E-4							)							)
  
  
  
  			@slow
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				FunnelTokenizer.from_pretrained("huggingface/funnel-small"							)
  					snake_case		:						Optional[Any]								=				FunnelModel.from_pretrained("huggingface/funnel-small"							)
  					snake_case		:						Dict								=				tokenizer("Hello! I am the Funnel Transformer model."				, return_tensors="pt"							)
  					snake_case		:						Any								=				model(**SCREAMING_SNAKE_CASE							)[0]
  
  					snake_case		:						Tuple								=				torch.tensor(2_35.72_46							)
  					snake_case		:						Dict								=				torch.tensor(0.02_56							)
  					self.assertTrue(torch.allclose(output.sum()				, SCREAMING_SNAKE_CASE				, atol=1E-4							)							)
  					self.assertTrue(torch.allclose(output.mean()				, SCREAMING_SNAKE_CASE				, atol=1E-4							)							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str   ):
  		snake_case		:						List[str]								=				tmp_path / "text.txt"
  		snake_case		:						Tuple								=				textwrap.dedent(
  		    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "   )
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     Dict   ):
  		snake_case		:						Any								=				tmp_path / "text_with_image.txt"
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.mark.parametrize("keep_linebreaks"       ,     [True, False]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Tuple   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						List[Any]								=				f.read().splitlines(keepends=lowercase__   )
  		snake_case		:						List[Any]								=				Text(keep_linebreaks=lowercase__       ,     encoding="utf-8"   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						int								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  
  
  @require_pil
  def   UpperCamelCase__     (		lowercase__  :     Optional[Any]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Union[str, Any]								=				f.read().splitlines()[0]
  		snake_case		:						List[Any]								=				Text(encoding="utf-8"       ,     features=Features({"image": Image()}   )   )
  		snake_case		:						Tuple								=				text._generate_tables([[text_file_with_image]]   )
  		snake_case		:						List[str]								=				pa.concat_tables([table for _, table in generator]   )
  		assert pa_table.schema.field("image"   ).type == Image()()
  		snake_case		:						Optional[Any]								=				pa_table.to_pydict()["image"]
  		assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  @pytest.mark.parametrize("sample_by"       ,     ["line", "paragraph", "document"]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Tuple								=				f.read()
  		if sample_by == "line":
  				snake_case		:						Optional[int]								=				expected_content.splitlines()
  		elif sample_by == "paragraph":
  				snake_case		:						str								=				expected_content.split("\n\n"   )
  		elif sample_by == "document":
  				snake_case		:						List[Any]								=				[expected_content]
  		snake_case		:						str								=				Text(sample_by=lowercase__       ,     encoding="utf-8"       ,     chunksize=100   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						Optional[Any]								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  