
'''simple docstring'''


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 						lowercase__	:






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=1_3 ,   lowerCamelCase__=7 ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=9_9 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=5 ,   lowerCamelCase__=4 ,   lowerCamelCase__=3_7 ,   lowerCamelCase__="gelu" ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=1_2_8 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=1_6 ,   lowerCamelCase__=2 ,   lowerCamelCase__=0.02 ,   lowerCamelCase__=3 ,   lowerCamelCase__=4 ,   lowerCamelCase__=None ,   ):

      '''simple docstring'''







      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       =  hidden_size
      UpperCamelCase       =  num_hidden_layers
      UpperCamelCase       =  num_attention_heads
      UpperCamelCase       =  intermediate_size
      UpperCamelCase       =  hidden_act
      UpperCamelCase       =  hidden_dropout_prob
      UpperCamelCase       =  attention_probs_dropout_prob
      UpperCamelCase       =  max_position_embeddings
      UpperCamelCase       =  type_vocab_size
      UpperCamelCase       =  type_sequence_label_size
      UpperCamelCase       =  initializer_range
      UpperCamelCase       =  num_labels
      UpperCamelCase       =  num_choices
      UpperCamelCase       =  scope







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size   )

      UpperCamelCase       =  None
      if self.use_input_mask:
         UpperCamelCase       =  random_attention_mask([self.batch_size, self.seq_length]   )

      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       =  self.get_config()

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







   def       UpperCAmelCase				(			self   ):

      '''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=SCREAMING_SNAKE_CASE_ ,   initializer_range=self.initializer_range ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      (
          (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     ,  
      )       =  self.prepare_config_and_inputs()

      UpperCamelCase       =  True
      UpperCamelCase       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
      UpperCamelCase       =  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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaModel(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  model(SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_   )
      UpperCamelCase       =  model(SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_   )
      UpperCamelCase       =  model(SCREAMING_SNAKE_CASE_   )
      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  True
      UpperCamelCase       =  NezhaModel(SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  model(
          SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_ ,   encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,   encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,   )
      UpperCamelCase       =  model(
          SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_ ,   encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,   )
      UpperCamelCase       =  model(SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_   )
      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaForMaskedLM(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaForNextSentencePrediction(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  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, 2)   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaForPreTraining(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  model(
          SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_ ,   labels=SCREAMING_SNAKE_CASE_ ,   next_sentence_label=SCREAMING_SNAKE_CASE_ ,   )
      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.num_labels
      UpperCamelCase       =  NezhaForSequenceClassification(SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.num_labels
      UpperCamelCase       =  NezhaForTokenClassification(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      model.eval()
      UpperCamelCase       =  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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.num_choices
      UpperCamelCase       =  NezhaForMultipleChoice(config=SCREAMING_SNAKE_CASE_   )
      model.to(SCREAMING_SNAKE_CASE_   )
      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(
          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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.prepare_config_and_inputs()
      (
          (
          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__	(  __lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					unittest.TestCase   ):






   '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      UpperCamelCase       =  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_   ):
            UpperCamelCase       =  torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length) ,   dtype=torch.long ,   device=SCREAMING_SNAKE_CASE_   )
            UpperCamelCase       =  torch.zeros(
                self.model_tester.batch_size ,   dtype=torch.long ,   device=SCREAMING_SNAKE_CASE_   )
      return inputs_dict







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaModelTester(self   )
      UpperCamelCase       =  ConfigTester(self ,   config_class=SCREAMING_SNAKE_CASE_ ,   hidden_size=3_7   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.config_tester.run_common_tests()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      (
          (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     , (
          UpperCamelCase 
      )     ,  
      )       =  self.model_tester.prepare_config_and_inputs_for_decoder()

      UpperCamelCase       =  None

      self.model_tester.create_and_check_model_as_decoder(
          SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_   )







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         UpperCamelCase       =  NezhaModel.from_pretrained(SCREAMING_SNAKE_CASE_   )
         self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )







   @slow
   @require_torch_gpu
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase     , UpperCamelCase       =  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

         UpperCamelCase       =  True
         UpperCamelCase       =  model_class(config=SCREAMING_SNAKE_CASE_   )

         UpperCamelCase       =  self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_   )
         UpperCamelCase       =  torch.jit.trace(
             SCREAMING_SNAKE_CASE_ ,   (inputs_dict['''input_ids'''].to('''cpu'''   ), inputs_dict['''attention_mask'''].to('''cpu'''   ))   )

         with tempfile.TemporaryDirectory() as tmp:
            torch.jit.save(SCREAMING_SNAKE_CASE_ ,   os.path.join(SCREAMING_SNAKE_CASE_ ,   '''bert.pt'''   )   )
            UpperCamelCase       =  torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE_ ,   '''bert.pt'''   ) ,   map_location=SCREAMING_SNAKE_CASE_   )
            loaded(inputs_dict['''input_ids'''].to(SCREAMING_SNAKE_CASE_   ) ,   inputs_dict['''attention_mask'''].to(SCREAMING_SNAKE_CASE_   )   )




@require_torch
class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''   )
      UpperCamelCase       =  torch.tensor([[0, 1, 2, 3, 4, 5]]   )
      UpperCamelCase       =  torch.tensor([[0, 1, 1, 1, 1, 1]]   )
      with torch.no_grad():
         UpperCamelCase       =  model(SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_   )[0]
      UpperCamelCase       =  torch.Size((1, 6, 7_6_8)   )
      self.assertEqual(output.shape ,   SCREAMING_SNAKE_CASE_   )
      UpperCamelCase       =  torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]   )

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







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''   )
      UpperCamelCase       =  torch.tensor([[0, 1, 2, 3, 4, 5]]   )
      UpperCamelCase       =  torch.tensor([[1, 1, 1, 1, 1, 1]]   )
      with torch.no_grad():
         UpperCamelCase       =  model(SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_   )[0]
      UpperCamelCase       =  torch.Size((1, 6, 2_1_1_2_8)   )
      self.assertEqual(output.shape ,   SCREAMING_SNAKE_CASE_   )
      UpperCamelCase       =  torch.tensor(
          [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]   )

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







'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_pegasus import PegasusTokenizer
else:
   snake_case_ :  Dict     =					None


snake_case_ :  int     =					logging.get_logger(__name__)


snake_case_ :  int     =					'▁'

snake_case_ :  Optional[int]     =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_ :  List[Any]     =					{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

snake_case_ :  Tuple     =					{
    'google/pegasus-xsum': 512,
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<mask_2>" ,   lowerCamelCase__="<mask_1>" ,   lowerCamelCase__=None ,   lowerCamelCase__=1_0_3 ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  offset

      if additional_special_tokens is not None:
         if not isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
            raise TypeError(
                f'additional_special_tokens should be of type {type(lowerCamelCase__   )}, but is'
                f' {type(lowerCamelCase__   )}'   )

         UpperCamelCase       =  (
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f'<unk_{i}>' for i in range(len(lowerCamelCase__   ) ,   self.offset - 1   )
         ]

         if len(set(lowerCamelCase__   )   ) != len(lowerCamelCase__   ):
            raise ValueError(
                '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'   )
         UpperCamelCase       =  additional_special_tokens_extended
      else:
         UpperCamelCase       =  [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'<unk_{i}>' for i in range(2 ,   self.offset   )]

      super().__init__(
          lowerCamelCase__ ,   tokenizer_file=lowerCamelCase__ ,   pad_token=lowerCamelCase__ ,   eos_token=lowerCamelCase__ ,   unk_token=lowerCamelCase__ ,   mask_token=lowerCamelCase__ ,   mask_token_sent=lowerCamelCase__ ,   offset=lowerCamelCase__ ,   additional_special_tokens=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  False if not self.vocab_file else True







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  set(self.all_special_ids   )  # call it once instead of inside list comp
      all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

      if all_special_ids != set(range(len(self.additional_special_tokens   ) + 3   )   ):
         raise ValueError(
             '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
             f' {len(self.additional_special_tokens   )} additional_special_tokens, but got {all_special_ids}'   )

      return [1 if x in all_special_ids else 0 for x in seq]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      if already_has_special_tokens:
         return self._special_token_mask(lowerCamelCase__   )
      elif token_ids_a is None:
         return self._special_token_mask(lowerCamelCase__   ) + [1]
      else:
         return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''   )

      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ):
         copyfile(self.vocab_file ,   lowerCamelCase__   )

      return (out_vocab_file,)





