



import os
import tempfile
import unittest

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

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


if is_torch_available():
		import torch

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

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=1_3 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=9_9 ,       __lowercase=3_2 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=3_7 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=1_2_8 ,       __lowercase=3_2 ,       __lowercase=1_6 ,       __lowercase=2 ,       __lowercase=0.0_2 ,       __lowercase=3 ,       __lowercase=4 ,       __lowercase=None ,       ) ->				Dict:
									"""simple docstring"""




									a__       :						List[str]							=  parent
									a__       :						Tuple							=  batch_size
									a__       :						List[Any]							=  seq_length
									a__       :						Tuple							=  is_training
									a__       :						Dict							=  use_input_mask
									a__       :						str							=  use_token_type_ids
									a__       :						List[Any]							=  use_labels
									a__       :						str							=  vocab_size
									a__       :						int							=  hidden_size
									a__       :						List[Any]							=  num_hidden_layers
									a__       :						str							=  num_attention_heads
									a__       :						Optional[int]							=  intermediate_size
									a__       :						Optional[Any]							=  hidden_act
									a__       :						Optional[Any]							=  hidden_dropout_prob
									a__       :						List[str]							=  attention_probs_dropout_prob
									a__       :						List[Any]							=  max_position_embeddings
									a__       :						List[str]							=  type_vocab_size
									a__       :						Any							=  type_sequence_label_size
									a__       :						List[str]							=  initializer_range
									a__       :						Any							=  num_labels
									a__       :						str							=  num_choices
									a__       :						int							=  scope

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Tuple							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )

									a__       :						Optional[int]							=  None
									if self.use_input_mask:
												a__       :						Optional[int]							=  random_attention_mask([self.batch_size, self.seq_length]  )

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

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

									a__       :						Optional[Any]							=  self.get_config()

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									return NezhaConfig(
									    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       type_vocab_size=self.type_vocab_size ,       is_decoder=__lowercase ,       initializer_range=self.initializer_range ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									(
									    (
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						
									)       :						Optional[int]							=  self.prepare_config_and_inputs()

									a__       :						Dict							=  True
									a__       :						Optional[int]							=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									a__       :						Any							=  ids_tensor([self.batch_size, self.seq_length] ,       vocab_size=2  )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_mask,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Any							=  NezhaModel(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						str							=  model(__lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase  )
									a__       :						Optional[Any]							=  model(__lowercase ,       token_type_ids=__lowercase  )
									a__       :						Optional[Any]							=  model(__lowercase  )
									self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)  )
									self.parent.assertEqual(result.pooler_output.shape ,       (self.batch_size, self.hidden_size)  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       ) ->				str:
									"""simple docstring"""




									a__       :						Any							=  True
									a__       :						Optional[Any]							=  NezhaModel(__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Union[str, Any]							=  model(
									    __lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase ,       encoder_hidden_states=__lowercase ,       encoder_attention_mask=__lowercase ,       )
									a__       :						str							=  model(
									    __lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase ,       encoder_hidden_states=__lowercase ,       )
									a__       :						Tuple							=  model(__lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase  )
									self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)  )
									self.parent.assertEqual(result.pooler_output.shape ,       (self.batch_size, self.hidden_size)  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						List[Any]							=  NezhaForMaskedLM(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						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.vocab_size)  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						List[str]							=  NezhaForNextSentencePrediction(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Dict							=  model(
									    __lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase ,       labels=__lowercase ,       )
									self.parent.assertEqual(result.logits.shape ,       (self.batch_size, 2)  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Dict:
									"""simple docstring"""




									a__       :						int							=  NezhaForPreTraining(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						str							=  model(
									    __lowercase ,       attention_mask=__lowercase ,       token_type_ids=__lowercase ,       labels=__lowercase ,       next_sentence_label=__lowercase ,       )
									self.parent.assertEqual(result.prediction_logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size)  )
									self.parent.assertEqual(result.seq_relationship_logits.shape ,       (self.batch_size, 2)  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Dict:
									"""simple docstring"""




									a__       :						Optional[Any]							=  NezhaForQuestionAnswering(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						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 ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.num_labels
									a__       :						str							=  NezhaForSequenceClassification(__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						List[str]							=  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 ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.num_labels
									a__       :						Tuple							=  NezhaForTokenClassification(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Optional[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 ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.num_choices
									a__       :						Dict							=  NezhaForMultipleChoice(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Optional[int]							=  input_ids.unsqueeze(1  ).expand(-1 ,       self.num_choices ,       -1  ).contiguous()
									a__       :						Dict							=  token_type_ids.unsqueeze(1  ).expand(-1 ,       self.num_choices ,       -1  ).contiguous()
									a__       :						Optional[Any]							=  input_mask.unsqueeze(1  ).expand(-1 ,       self.num_choices ,       -1  ).contiguous()
									a__       :						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  ) ->				Tuple:
									"""simple docstring"""




									a__       :						str							=  self.prepare_config_and_inputs()
									(
									    (
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						
									)       :						Tuple							=  config_and_inputs
									a__       :						Optional[int]							=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
									return config, inputs_dict




@require_torch
class        snake_case__    (A__ ,     A__ ,     A__ ,     unittest.TestCase		):







						"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase=False  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Dict							=  super()._prepare_for_class(__lowercase ,       __lowercase ,       return_labels=__lowercase  )

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  NezhaModelTester(self  )
									a__       :						Tuple							=  ConfigTester(self ,       config_class=__lowercase ,       hidden_size=3_7  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									self.config_tester.run_common_tests()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Dict							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Dict							=  self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									(
									    (
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						(
									    a__
									)	,						
									)       :						List[Any]							=  self.model_tester.prepare_config_and_inputs_for_decoder()

									a__       :						List[Any]							=  None

									self.model_tester.create_and_check_model_as_decoder(
									    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						List[str]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Any							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_next_sequence_prediction(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Tuple							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						str							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*__lowercase  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a__       :						Tuple							=  NezhaModel.from_pretrained(__lowercase  )
												self.assertIsNotNone(__lowercase  )

						@slow
						@require_torch_gpu
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__	,						a__       :						Optional[int]							=  self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												# NezhaForMultipleChoice behaves incorrectly in JIT environments.
												if model_class == NezhaForMultipleChoice:
															return

												a__       :						List[Any]							=  True
												a__       :						Tuple							=  model_class(config=__lowercase  )

												a__       :						Optional[Any]							=  self._prepare_for_class(__lowercase ,       __lowercase  )
												a__       :						str							=  torch.jit.trace(
												    __lowercase ,       (inputs_dict["""input_ids"""].to("""cpu"""  ), inputs_dict["""attention_mask"""].to("""cpu"""  ))  )

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




@require_torch
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[str]							=  NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""  )
									a__       :						List[Any]							=  torch.tensor([[0, 1, 2, 3, 4, 5]]  )
									a__       :						Dict							=  torch.tensor([[0, 1, 1, 1, 1, 1]]  )
									with torch.no_grad():
												a__       :						int							=  model(__lowercase ,       attention_mask=__lowercase  )[0]
									a__       :						List[Any]							=  torch.Size((1, 6, 7_6_8)  )
									self.assertEqual(output.shape ,       __lowercase  )
									a__       :						Dict							=  torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]  )

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

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						List[Any]							=  NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""  )
									a__       :						List[Any]							=  torch.tensor([[0, 1, 2, 3, 4, 5]]  )
									a__       :						Any							=  torch.tensor([[1, 1, 1, 1, 1, 1]]  )
									with torch.no_grad():
												a__       :						List[str]							=  model(__lowercase ,       attention_mask=__lowercase  )[0]
									a__       :						Any							=  torch.Size((1, 6, 2_1_1_2_8)  )
									self.assertEqual(output.shape ,       __lowercase  )
									a__       :						Dict							=  torch.tensor(
									    [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]  )

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









import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Tuple							=  params

									a__       :						str							=  np.array(__lowercase  )
									a__       :						List[Any]							=  np.array([len(__lowercase  ) for t in data]  )

									self.check()
									self.remove_long_sequences()
									self.remove_empty_sequences()
									self.remove_unknown_sequences()
									self.check()
									self.print_statistics()

						def __getitem__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									return (self.token_ids[index], self.lengths[index])

						def __len__(       self  ) ->				Dict:
									"""simple docstring"""




									return len(self.lengths  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									assert len(self.token_ids  ) == len(self.lengths  )
									assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						int							=  self.params.max_model_input_size
									a__       :						int							=  self.lengths > max_len
									logger.info(F'''Splitting {sum(__lowercase  )} too long sequences.'''  )

									def divide_chunks(__lowercase ,       __lowercase  ):
												return [l[i : i + n] for i in range(0 ,       len(__lowercase  ) ,       __lowercase  )]

									a__       :						Any							=  []
									a__       :						Optional[int]							=  []
									if self.params.mlm:
												a__	,						a__       :						Any							=  self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
									else:
												a__	,						a__       :						Dict							=  self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

									for seq_, len_ in zip(self.token_ids ,       self.lengths  ):
												assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
												if len_ <= max_len:
															new_tok_ids.append(seq_  )
															new_lengths.append(len_  )
												else:
															a__       :						int							=  []
															for sub_s in divide_chunks(seq_ ,       max_len - 2  ):
																		if sub_s[0] != cls_id:
																					a__       :						str							=  np.insert(__lowercase ,       0 ,       __lowercase  )
																		if sub_s[-1] != sep_id:
																					a__       :						List[str]							=  np.insert(__lowercase ,       len(__lowercase  ) ,       __lowercase  )
																		assert len(__lowercase  ) <= max_len
																		assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																		sub_seqs.append(__lowercase  )

															new_tok_ids.extend(__lowercase  )
															new_lengths.extend([len(__lowercase  ) for l in sub_seqs]  )

									a__       :						Optional[int]							=  np.array(__lowercase  )
									a__       :						Any							=  np.array(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  len(self  )
									a__       :						List[str]							=  self.lengths > 1_1
									a__       :						Dict							=  self.token_ids[indices]
									a__       :						List[str]							=  self.lengths[indices]
									a__       :						int							=  len(self  )
									logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									if "unk_token" not in self.params.special_tok_ids:
												return
									else:
												a__       :						Union[str, Any]							=  self.params.special_tok_ids["""unk_token"""]
									a__       :						List[Any]							=  len(self  )
									a__       :						Optional[int]							=  np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
									a__       :						Optional[Any]							=  (unk_occs / self.lengths) < 0.5
									a__       :						Tuple							=  self.token_ids[indices]
									a__       :						Union[str, Any]							=  self.lengths[indices]
									a__       :						Tuple							=  len(self  )
									logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									if not self.params.is_master:
												return
									logger.info(F'''{len(self  )} sequences'''  )
									# data_len = sum(self.lengths)
									# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
									# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

									# unk_idx = self.params.special_tok_ids['unk_token']
									# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
									# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[int]							=  [t[0] for t in batch]
									a__       :						Any							=  [t[1] for t in batch]
									assert len(__lowercase  ) == len(__lowercase  )

									# Max for paddings
									a__       :						List[Any]							=  max(__lowercase  )

									# Pad token ids
									if self.params.mlm:
												a__       :						int							=  self.params.special_tok_ids["""pad_token"""]
									else:
												a__       :						List[str]							=  self.params.special_tok_ids["""unk_token"""]
									a__       :						int							=  [list(t.astype(__lowercase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowercase  )) for t in token_ids]
									assert len(tk_  ) == len(__lowercase  )
									assert all(len(__lowercase  ) == max_seq_len_ for t in tk_  )

									a__       :						List[Any]							=  torch.tensor(tk_  )  # (bs, max_seq_len_)
									a__       :						Optional[int]							=  torch.tensor(__lowercase  )  # (bs)
									return tk_t, lg_t




