





'''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						:	List[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	Tuple=13							,  __snake_case						:	Dict=7							,  __snake_case						:	List[Any]=True							,  __snake_case						:	int=True							,  __snake_case						:	Optional[Any]=True							,  __snake_case						:	Dict=True							,  __snake_case						:	Union[str, Any]=99							,  __snake_case						:	str=[1, 1, 2]							,  __snake_case						:	Any=1							,  __snake_case						:	Optional[Any]=32							,  __snake_case						:	Tuple=4							,  __snake_case						:	List[Any]=8							,  __snake_case						:	Union[str, Any]=37							,  __snake_case						:	int="gelu_new"							,  __snake_case						:	Optional[int]=0.1							,  __snake_case						:	Optional[int]=0.1							,  __snake_case						:	Optional[int]=0.0							,  __snake_case						:	Any=5_12							,  __snake_case						:	Any=3							,  __snake_case						:	List[Any]=0.02							,  __snake_case						:	Optional[Any]=3							,  __snake_case						:	Dict=4							,  __snake_case						:	Union[str, Any]=None							,  __snake_case						:	Any=False							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		parent
											_snake_case: str	       =		batch_size
											_snake_case: str	       =		seq_length
											_snake_case: Dict	       =		is_training
											_snake_case: Union[str, Any]	       =		use_input_mask
											_snake_case: str	       =		use_token_type_ids
											_snake_case: Union[str, Any]	       =		use_labels
											_snake_case: Tuple	       =		vocab_size
											_snake_case: List[Any]	       =		block_sizes
											_snake_case: Dict	       =		num_decoder_layers
											_snake_case: Union[str, Any]	       =		d_model
											_snake_case: Tuple	       =		n_head
											_snake_case: Dict	       =		d_head
											_snake_case: Union[str, Any]	       =		d_inner
											_snake_case: Optional[Any]	       =		hidden_act
											_snake_case: Union[str, Any]	       =		hidden_dropout
											_snake_case: Dict	       =		attention_dropout
											_snake_case: str	       =		activation_dropout
											_snake_case: Tuple	       =		max_position_embeddings
											_snake_case: Optional[int]	       =		type_vocab_size
											_snake_case: List[Any]	       =		2
											_snake_case: Tuple	       =		num_labels
											_snake_case: Union[str, Any]	       =		num_choices
											_snake_case: Optional[int]	       =		scope
											_snake_case: str	       =		initializer_std

											# Used in the tests to check the size of the first attention layer
											_snake_case: str	       =		n_head
											# Used in the tests to check the size of the first hidden state
											_snake_case: Any	       =		self.d_model
											# Used in the tests to check the number of output hidden states/attentions
											_snake_case: 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: Dict	       =		self.num_hidden_layers + 2


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size							)

											_snake_case: Dict	       =		None
											if self.use_input_mask:
																	_snake_case: Tuple	       =		ids_tensor([self.batch_size, self.seq_length]							,  vocab_size=2							)

											_snake_case: Optional[int]	       =		None
											if self.use_token_type_ids:
																	_snake_case: List[str]	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.type_vocab_size							)

											_snake_case: Optional[Any]	       =		None
											_snake_case: int	       =		None
											_snake_case: Union[str, Any]	       =		None
											if self.use_labels:
																	_snake_case: Any	       =		ids_tensor([self.batch_size]							,  self.type_sequence_label_size							)
																	_snake_case: Any	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.num_labels							)
																	_snake_case: List[str]	       =		ids_tensor([self.batch_size]							,  self.num_choices							)
																	_snake_case: List[Any]	       =		ids_tensor([self.batch_size, self.seq_length]							,  1							)

											_snake_case: List[str]	       =		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						:	Any							):





											'''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  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[int]							,  __snake_case						:	Any							,  __snake_case						:	List[str]							,  __snake_case						:	Optional[Any]							,  ):





											'''simple docstring'''





											_snake_case: Tuple	       =		FunnelModel(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: Optional[Any]	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							)
											_snake_case: str	       =		model(__lowercase							,  token_type_ids=__lowercase							)
											_snake_case: Union[str, Any]	       =		model(__lowercase							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.d_model)							)

											_snake_case: Union[str, Any]	       =		False
											_snake_case: Optional[Any]	       =		model(__lowercase							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.d_model)							)

											_snake_case: str	       =		False
											_snake_case: List[str]	       =		model(__lowercase							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.d_model)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Any							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	Tuple							,  __snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		FunnelBaseModel(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: int	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							)
											_snake_case: List[str]	       =		model(__lowercase							,  token_type_ids=__lowercase							)
											_snake_case: Union[str, Any]	       =		model(__lowercase							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, 2, self.d_model)							)

											_snake_case: Optional[Any]	       =		False
											_snake_case: Tuple	       =		model(__lowercase							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, 3, self.d_model)							)

											_snake_case: Optional[int]	       =		False
											_snake_case: List[str]	       =		model(__lowercase							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, 2, self.d_model)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	List[str]							,  __snake_case						:	Tuple							,  __snake_case						:	int							,  __snake_case						:	List[str]							,  __snake_case						:	str							,  __snake_case						:	Tuple							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Any							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.num_labels
											_snake_case: List[str]	       =		FunnelForPreTraining(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: Union[str, Any]	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							,  labels=__lowercase							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	List[str]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	int							,  __snake_case						:	List[str]							,  __snake_case						:	List[Any]							,  __snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: Any	       =		FunnelForMaskedLM(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: Optional[int]	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							,  labels=__lowercase							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.vocab_size)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[str]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Optional[int]							,  __snake_case						:	int							,  __snake_case						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	str							,  __snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: str	       =		self.num_labels
											_snake_case: str	       =		FunnelForSequenceClassification(__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: Any	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							,  labels=__lowercase							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Dict							,  __snake_case						:	int							,  __snake_case						:	int							,  __snake_case						:	List[Any]							,  __snake_case						:	str							,  __snake_case						:	Optional[Any]							,  __snake_case						:	str							,  ):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.num_choices
											_snake_case: List[Any]	       =		FunnelForMultipleChoice(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: int	       =		input_ids.unsqueeze(1							).expand(-1							,  self.num_choices							,  -1							).contiguous()
											_snake_case: Optional[Any]	       =		token_type_ids.unsqueeze(1							).expand(-1							,  self.num_choices							,  -1							).contiguous()
											_snake_case: List[str]	       =		input_mask.unsqueeze(1							).expand(-1							,  self.num_choices							,  -1							).contiguous()
											_snake_case: Any	       =		model(
											    __lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							,  labels=__lowercase							,  )
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_choices)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Any							,  __snake_case						:	List[str]							,  __snake_case						:	Tuple							,  __snake_case						:	Any							,  __snake_case						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[Any]							,  __snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.num_labels
											_snake_case: Tuple	       =		FunnelForTokenClassification(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: List[Any]	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							,  labels=__lowercase							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	str							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[str]							,  __snake_case						:	str							,  __snake_case						:	Dict							,  __snake_case						:	Optional[int]							,  ):





											'''simple docstring'''





											_snake_case: str	       =		FunnelForQuestionAnswering(config=__lowercase							)
											model.to(__lowercase							)
											model.eval()
											_snake_case: Dict	       =		model(
											    __lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							,  start_positions=__lowercase							,  end_positions=__lowercase							,  )
											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						:	int							):





											'''simple docstring'''





											_snake_case: Any	       =		self.prepare_config_and_inputs()
											(
											    _snake_case 
											): List[Any]	       =		config_and_inputs
											_snake_case: Tuple	       =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
											return config, inputs_dict







@require_torch
class       lowerCamelCase       (    _A  ,				_A  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     (
					    (
					        FunnelModel,
					        FunnelForMaskedLM,
					        FunnelForPreTraining,
					        FunnelForQuestionAnswering,
					        FunnelForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE				 =     (
					    {
					        "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						:	List[str]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	str=False							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		super()._prepare_for_class(__lowercase							,  __lowercase							,  return_labels=__lowercase							)

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		FunnelModelTester(self							)
											_snake_case: List[str]	       =		ConfigTester(self							,  config_class=__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: str	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: int	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__lowercase							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





											if hasattr(__lowercase							,  'weight'							) and module.weight is not None:
																	module.weight.data.fill_(3							)
											if hasattr(__lowercase							,  '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(__lowercase							,  __lowercase							) and getattr(__lowercase							,  __lowercase							) is not None:
																							_snake_case: Tuple	       =		getattr(__lowercase							,  __lowercase							)
																							weight.data.fill_(3							)







@require_torch
class       lowerCamelCase       (    _A  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     (
					    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
					)


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case: str	       =		FunnelModelTester(self							,  base=__lowercase							)
											_snake_case: Dict	       =		ConfigTester(self							,  config_class=__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Tuple	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_base_model(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: Any	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Any	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case: str	       =		True

											for model_class in self.all_model_classes:
																	if model_class.__name__ == "FunnelBaseModel":
																							continue
																	_snake_case: Dict	       =		model_class(__lowercase							)
																	model.to(__lowercase							)
																	model.train()
																	_snake_case: str	       =		self._prepare_for_class(__lowercase							,  __lowercase							,  return_labels=__lowercase							)
																	_snake_case: Optional[Any]	       =		model(**__lowercase							).loss
																	loss.backward()




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	int							):





											'''simple docstring'''





											if hasattr(__lowercase							,  'weight'							) and module.weight is not None:
																	module.weight.data.fill_(3							)
											if hasattr(__lowercase							,  '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(__lowercase							,  __lowercase							) and getattr(__lowercase							,  __lowercase							) is not None:
																							_snake_case: Dict	       =		getattr(__lowercase							,  __lowercase							)
																							weight.data.fill_(3							)







@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case: Tuple	       =		13
											_snake_case: List[Any]	       =		7
											_snake_case: Optional[int]	       =		torch.arange(0							,  batch_size * sequence_length							).long().reshape(__lowercase							,  __lowercase							)
											_snake_case: Optional[int]	       =		[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
											_snake_case: str	       =		torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]							)

											_snake_case: int	       =		FunnelModel.from_pretrained('sgugger/funnel-random-tiny'							)
											_snake_case: Any	       =		model(__lowercase							,  token_type_ids=__lowercase							)[0].abs()

											_snake_case: Any	       =		torch.tensor(23_44.83_52							)
											_snake_case: Tuple	       =		torch.tensor(0.8_052							)
											self.assertTrue(torch.allclose(output.sum()							,  __lowercase							,  atol=1e-4							)							)
											self.assertTrue(torch.allclose(output.mean()							,  __lowercase							,  atol=1e-4							)							)

											_snake_case: str	       =		torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]							)
											_snake_case: Union[str, Any]	       =		model(__lowercase							,  attention_mask=__lowercase							,  token_type_ids=__lowercase							)[0].abs()

											_snake_case: Optional[Any]	       =		torch.tensor(23_43.84_25							)
											_snake_case: Union[str, Any]	       =		torch.tensor(0.8_049							)
											self.assertTrue(torch.allclose(output.sum()							,  __lowercase							,  atol=1e-4							)							)
											self.assertTrue(torch.allclose(output.mean()							,  __lowercase							,  atol=1e-4							)							)




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: Any	       =		FunnelTokenizer.from_pretrained('huggingface/funnel-small'							)
											_snake_case: Any	       =		FunnelModel.from_pretrained('huggingface/funnel-small'							)
											_snake_case: List[str]	       =		tokenizer('Hello! I am the Funnel Transformer model.'							,  return_tensors='pt'							)
											_snake_case: Optional[Any]	       =		model(**__lowercase							)[0]

											_snake_case: Union[str, Any]	       =		torch.tensor(2_35.72_46							)
											_snake_case: Dict	       =		torch.tensor(0.0_256							)
											self.assertTrue(torch.allclose(output.sum()							,  __lowercase							,  atol=1e-4							)							)
											self.assertTrue(torch.allclose(output.mean()							,  __lowercase							,  atol=1e-4							)							)












import sys

from transformers import AutoTokenizer


snake_case_				=      sys.argv[1]
snake_case_				=      sys.argv[2]
snake_case_				=      int(sys.argv[3])

snake_case_				=      0

snake_case_				=      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
				for line in f_p:
								snake_case_				=      line.rstrip()

								if not line:
												print(line)
												snake_case_				=      0
												continue

								snake_case_				=      line.split()[0]

								snake_case_				=      len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('''''')
												print(line)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

