'''simple docstring'''
import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertWithLMHeadModel,
      )
      from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       List[Any]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       List[str]=13  ,							UpperCAmelCase__			:       List[Any]=7  ,							UpperCAmelCase__			:       Dict=True  ,							UpperCAmelCase__			:       Any=True  ,							UpperCAmelCase__			:       str=True  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							UpperCAmelCase__			:       List[str]=True  ,							UpperCAmelCase__			:       str=False  ,							UpperCAmelCase__			:       Tuple=False  ,							UpperCAmelCase__			:       List[str]=False  ,							UpperCAmelCase__			:       str=2  ,							UpperCAmelCase__			:       Optional[Any]=99  ,							UpperCAmelCase__			:       Optional[Any]=0  ,							UpperCAmelCase__			:       List[Any]=32  ,							UpperCAmelCase__			:       Union[str, Any]=5  ,							UpperCAmelCase__			:       Any=4  ,							UpperCAmelCase__			:       Optional[Any]=0.1  ,							UpperCAmelCase__			:       List[Any]=0.1  ,							UpperCAmelCase__			:       Union[str, Any]=512  ,							UpperCAmelCase__			:       str=12  ,							UpperCAmelCase__			:       int=2  ,							UpperCAmelCase__			:       List[str]=0.02  ,							UpperCAmelCase__			:       List[Any]=3  ,							UpperCAmelCase__			:       List[str]=4  ,							UpperCAmelCase__			:       List[Any]="last"  ,							UpperCAmelCase__			:       int=None  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							)				->Optional[Any]:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  seq_length
        UpperCAmelCase_            =  is_training
        UpperCAmelCase_            =  use_input_lengths
        UpperCAmelCase_            =  use_token_type_ids
        UpperCAmelCase_            =  use_labels
        UpperCAmelCase_            =  gelu_activation
        UpperCAmelCase_            =  sinusoidal_embeddings
        UpperCAmelCase_            =  causal
        UpperCAmelCase_            =  asm
        UpperCAmelCase_            =  n_langs
        UpperCAmelCase_            =  vocab_size
        UpperCAmelCase_            =  n_special
        UpperCAmelCase_            =  hidden_size
        UpperCAmelCase_            =  num_hidden_layers
        UpperCAmelCase_            =  num_attention_heads
        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_            =  summary_type
        UpperCAmelCase_            =  use_proj
        UpperCAmelCase_            =  scope






  def  lowerCAmelCase__ (      self			:       Dict  )				->Dict:
        UpperCAmelCase_            =  ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )
        UpperCAmelCase_            =  random_attention_mask([self.batch_size, self.seq_length]  )

        UpperCAmelCase_            =  None
        if self.use_input_lengths:
              UpperCAmelCase_            =  (
                  ids_tensor([self.batch_size]  ,							vocab_size=2  ) + self.seq_length - 2
              )  # small variation of seq_length

        UpperCAmelCase_            =  None
        if self.use_token_type_ids:
              UpperCAmelCase_            =  ids_tensor([self.batch_size, self.seq_length]  ,							self.n_langs  )

        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]  ,							2  ).float()
              UpperCAmelCase_            =  ids_tensor([self.batch_size]  ,							self.num_choices  )

        UpperCAmelCase_            =  self.get_config()

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )






  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        return FlaubertConfig(
            vocab_size=self.vocab_size  ,							n_special=self.n_special  ,							emb_dim=self.hidden_size  ,							n_layers=self.num_hidden_layers  ,							n_heads=self.num_attention_heads  ,							dropout=self.hidden_dropout_prob  ,							attention_dropout=self.attention_probs_dropout_prob  ,							gelu_activation=self.gelu_activation  ,							sinusoidal_embeddings=self.sinusoidal_embeddings  ,							asm=self.asm  ,							causal=self.causal  ,							n_langs=self.n_langs  ,							max_position_embeddings=self.max_position_embeddings  ,							initializer_range=self.initializer_range  ,							summary_type=self.summary_type  ,							use_proj=self.use_proj  ,							)






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Any  ,							)				->List[str]:
        UpperCAmelCase_            =  FlaubertModel(config=UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							lengths=UpperCAmelCase__  ,							langs=UpperCAmelCase__  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							langs=UpperCAmelCase__  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple  ,							)				->List[Any]:
        UpperCAmelCase_            =  FlaubertWithLMHeadModel(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							token_type_ids=UpperCAmelCase__  ,							labels=UpperCAmelCase__  )
        self.parent.assertEqual(result.loss.shape  ,							()  )
        self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.vocab_size)  )






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Tuple  ,							)				->List[Any]:
        UpperCAmelCase_            =  FlaubertForQuestionAnsweringSimple(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        UpperCAmelCase_            =  model(UpperCAmelCase__  )

        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							start_positions=UpperCAmelCase__  ,							end_positions=UpperCAmelCase__  )
        self.parent.assertEqual(result.start_logits.shape  ,							(self.batch_size, self.seq_length)  )
        self.parent.assertEqual(result.end_logits.shape  ,							(self.batch_size, self.seq_length)  )






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[Any]  ,							)				->str:
        UpperCAmelCase_            =  FlaubertForQuestionAnswering(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        UpperCAmelCase_            =  model(UpperCAmelCase__  )

        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							start_positions=UpperCAmelCase__  ,							end_positions=UpperCAmelCase__  ,							cls_index=UpperCAmelCase__  ,							is_impossible=UpperCAmelCase__  ,							p_mask=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							start_positions=UpperCAmelCase__  ,							end_positions=UpperCAmelCase__  ,							cls_index=UpperCAmelCase__  ,							is_impossible=UpperCAmelCase__  ,							)

        ((UpperCAmelCase_)      ,		)            =  result_with_labels.to_tuple()

        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							start_positions=UpperCAmelCase__  ,							end_positions=UpperCAmelCase__  )

        ((UpperCAmelCase_)      ,		)            =  result_with_labels.to_tuple()

        self.parent.assertEqual(result_with_labels.loss.shape  ,							()  )
        self.parent.assertEqual(result.start_top_log_probs.shape  ,							(self.batch_size, model.config.start_n_top)  )
        self.parent.assertEqual(result.start_top_index.shape  ,							(self.batch_size, model.config.start_n_top)  )
        self.parent.assertEqual(
            result.end_top_log_probs.shape  ,							(self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
        self.parent.assertEqual(
            result.end_top_index.shape  ,							(self.batch_size, model.config.start_n_top * model.config.end_n_top)  )
        self.parent.assertEqual(result.cls_logits.shape  ,							(self.batch_size,)  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Union[str, Any]  ,							)				->List[Any]:
        UpperCAmelCase_            =  FlaubertForSequenceClassification(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        UpperCAmelCase_            =  model(UpperCAmelCase__  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							labels=UpperCAmelCase__  )

        self.parent.assertEqual(result.loss.shape  ,							()  )
        self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.type_sequence_label_size)  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Tuple  ,							)				->Optional[Any]:
        UpperCAmelCase_            =  self.num_labels
        UpperCAmelCase_            =  FlaubertForTokenClassification(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							labels=UpperCAmelCase__  )
        self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.num_labels)  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Any  ,							)				->Optional[int]:
        UpperCAmelCase_            =  self.num_choices
        UpperCAmelCase_            =  FlaubertForMultipleChoice(config=UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  input_ids.unsqueeze(1  ).expand(-1  ,							self.num_choices  ,							-1  ).contiguous()
        UpperCAmelCase_            =  token_type_ids.unsqueeze(1  ).expand(-1  ,							self.num_choices  ,							-1  ).contiguous()
        UpperCAmelCase_            =  input_mask.unsqueeze(1  ).expand(-1  ,							self.num_choices  ,							-1  ).contiguous()
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							token_type_ids=UpperCAmelCase__  ,							labels=UpperCAmelCase__  ,							)
        self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_choices)  )






  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->List[Any]:
        UpperCAmelCase_            =  self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		
        )            =  config_and_inputs
        UpperCAmelCase_            =  {
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''lengths''': input_lengths,
            '''attention_mask''': input_mask,
        }
        return config, inputs_dict







@require_torch
class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	(
      (
          FlaubertModel,
          FlaubertWithLMHeadModel,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  lowerCAmelCase__										=	(
      {
          '''feature-extraction''': FlaubertModel,
          '''fill-mask''': FlaubertWithLMHeadModel,
          '''question-answering''': FlaubertForQuestionAnsweringSimple,
          '''text-classification''': FlaubertForSequenceClassification,
          '''token-classification''': FlaubertForTokenClassification,
          '''zero-shot''': FlaubertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple  )				->Any:
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith('''Fast'''  )
        ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True

        return False






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       str=False  )				->int:
        UpperCAmelCase_            =  super()._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  ,							return_labels=UpperCAmelCase__  )

        if return_labels:
              if model_class.__name__ == "FlaubertForQuestionAnswering":
                    UpperCAmelCase_            =  torch.zeros(
                        self.model_tester.batch_size  ,							dtype=torch.long  ,							device=UpperCAmelCase__  )
                    UpperCAmelCase_            =  torch.zeros(
                        self.model_tester.batch_size  ,							dtype=torch.long  ,							device=UpperCAmelCase__  )

        return inputs_dict






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->int:
        UpperCAmelCase_            =  FlaubertModelTester(self  )
        UpperCAmelCase_            =  ConfigTester(self  ,							config_class=UpperCAmelCase__  ,							emb_dim=37  )






  def  lowerCAmelCase__ (      self			:       Dict  )				->Union[str, Any]:
        self.config_tester.run_common_tests()






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Optional[int]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Dict  )				->List[str]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_simple_qa(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Any:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[int]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Optional[Any]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_token_classif(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->List[str]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_multiple_choice(*UpperCAmelCase__  )






  @slow
  def  lowerCAmelCase__ (      self			:       List[str]  )				->str:
        for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_            =  FlaubertModel.from_pretrained(UpperCAmelCase__  )
              self.assertIsNotNone(UpperCAmelCase__  )






  @slow
  @require_torch_gpu
  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->Dict:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
              # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
              if model_class == FlaubertForMultipleChoice:
                    return

              UpperCAmelCase_            =  True
              UpperCAmelCase_            =  model_class(config=UpperCAmelCase__  )

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

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







@require_torch
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  @slow
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->str:
        UpperCAmelCase_            =  FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''  )
        UpperCAmelCase_            =  torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
        with torch.no_grad():
              UpperCAmelCase_            =  model(UpperCAmelCase__  )[0]
        UpperCAmelCase_            =  torch.Size((1, 11, 768)  )
        self.assertEqual(output.shape  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  torch.tensor(
            [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]  )

        self.assertTrue(torch.allclose(output[:, :3, :3]  ,							UpperCAmelCase__  ,							atol=1e-4  )  )






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['''token_type_ids''']  )  ,							sum(tokens_p['''token_type_ids''']  )  )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )  ,							sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )





