






'''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 UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		str=13  ,   UpperCAmelCase      :		Union[str, Any]=7  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Optional[int]=True  ,   UpperCAmelCase      :		Optional[Any]=True  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Any=False  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		Dict=2  ,   UpperCAmelCase      :		Dict=99  ,   UpperCAmelCase      :		Union[str, Any]=0  ,   UpperCAmelCase      :		Any=32  ,   UpperCAmelCase      :		Any=5  ,   UpperCAmelCase      :		str=4  ,   UpperCAmelCase      :		List[Any]=0.1  ,   UpperCAmelCase      :		Optional[Any]=0.1  ,   UpperCAmelCase      :		Dict=512  ,   UpperCAmelCase      :		Tuple=12  ,   UpperCAmelCase      :		str=2  ,   UpperCAmelCase      :		Optional[Any]=0.0_2  ,   UpperCAmelCase      :		Optional[Any]=3  ,   UpperCAmelCase      :		Optional[int]=4  ,   UpperCAmelCase      :		Any="last"  ,   UpperCAmelCase      :		Optional[Any]=None  ,   UpperCAmelCase      :		str=None  ,   )	->	str:


       '''simple docstring'''

       lowercase       : str						=parent
       lowercase       : List[Any]						=batch_size
       lowercase       : Dict						=seq_length
       lowercase       : Dict						=is_training
       lowercase       : Dict						=use_input_lengths
       lowercase       : Tuple						=use_token_type_ids
       lowercase       : Union[str, Any]						=use_labels
       lowercase       : int						=gelu_activation
       lowercase       : Dict						=sinusoidal_embeddings
       lowercase       : Tuple						=causal
       lowercase       : Optional[Any]						=asm
       lowercase       : Optional[int]						=n_langs
       lowercase       : List[str]						=vocab_size
       lowercase       : str						=n_special
       lowercase       : Tuple						=hidden_size
       lowercase       : Any						=num_hidden_layers
       lowercase       : str						=num_attention_heads
       lowercase       : Optional[int]						=hidden_dropout_prob
       lowercase       : Dict						=attention_probs_dropout_prob
       lowercase       : Optional[int]						=max_position_embeddings
       lowercase       : List[Any]						=type_vocab_size
       lowercase       : str						=type_sequence_label_size
       lowercase       : List[str]						=initializer_range
       lowercase       : str						=num_labels
       lowercase       : Tuple						=num_choices
       lowercase       : Optional[int]						=summary_type
       lowercase       : Optional[Any]						=use_proj
       lowercase       : Optional[int]						=scope






      def 					A__   (			self      :		Tuple		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : int						=ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)
       lowercase       : str						=random_attention_mask([self.batch_size, self.seq_length]		)

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

       lowercase       : Optional[Any]						=None
       if self.use_token_type_ids:
        lowercase       : Dict						=ids_tensor([self.batch_size, self.seq_length]  ,   self.n_langs		)

       lowercase       : int						=None
       lowercase       : Any						=None
       lowercase       : Dict						=None
       if self.use_labels:
        lowercase       : int						=ids_tensor([self.batch_size]  ,   self.type_sequence_label_size		)
        lowercase       : Dict						=ids_tensor([self.batch_size, self.seq_length]  ,   self.num_labels		)
        lowercase       : Dict						=ids_tensor([self.batch_size]  ,   2		).float()
        lowercase       : Optional[int]						=ids_tensor([self.batch_size]  ,   self.num_choices		)

       lowercase       : Dict						=self.get_config()

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






      def 					A__   (			self      :		int		)	->	int:


       '''simple docstring'''

       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 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[int]  ,   )	->	Tuple:


       '''simple docstring'''

       lowercase       : Optional[int]						=FlaubertModel(config=UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()
       lowercase       : int						=model(UpperCAmelCase  ,   lengths=UpperCAmelCase  ,   langs=UpperCAmelCase		)
       lowercase       : Union[str, Any]						=model(UpperCAmelCase  ,   langs=UpperCAmelCase		)
       lowercase       : Dict						=model(UpperCAmelCase		)
       self.parent.assertEqual(result.last_hidden_state.shape  ,   (self.batch_size, self.seq_length, self.hidden_size)		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		Optional[int]  ,   )	->	Tuple:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=FlaubertWithLMHeadModel(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       lowercase       : List[Any]						=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 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Optional[Any]  ,   )	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Any						=FlaubertForQuestionAnsweringSimple(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       lowercase       : str						=model(UpperCAmelCase		)

       lowercase       : int						=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 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[Any]  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=FlaubertForQuestionAnswering(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       lowercase       : List[Any]						=model(UpperCAmelCase		)

       lowercase       : List[str]						=model(
           UpperCAmelCase  ,   start_positions=UpperCAmelCase  ,   end_positions=UpperCAmelCase  ,   cls_index=UpperCAmelCase  ,   is_impossible=UpperCAmelCase  ,   p_mask=UpperCAmelCase  ,   )

       lowercase       : Optional[int]						=model(
           UpperCAmelCase  ,   start_positions=UpperCAmelCase  ,   end_positions=UpperCAmelCase  ,   cls_index=UpperCAmelCase  ,   is_impossible=UpperCAmelCase  ,   )

       ((lowercase)	,		)       : Tuple						=result_with_labels.to_tuple()

       lowercase       : int						=model(UpperCAmelCase  ,   start_positions=UpperCAmelCase  ,   end_positions=UpperCAmelCase		)

       ((lowercase)	,		)       : Union[str, Any]						=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 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		str  ,   )	->	Tuple:


       '''simple docstring'''

       lowercase       : Dict						=FlaubertForSequenceClassification(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       lowercase       : str						=model(UpperCAmelCase		)
       lowercase       : str						=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 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		str  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : List[str]						=self.num_labels
       lowercase       : Any						=FlaubertForTokenClassification(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       lowercase       : Optional[int]						=model(UpperCAmelCase  ,   attention_mask=UpperCAmelCase  ,   labels=UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.num_labels)		)






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		str  ,   )	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Tuple						=self.num_choices
       lowercase       : Any						=FlaubertForMultipleChoice(config=UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()
       lowercase       : List[str]						=input_ids.unsqueeze(1		).expand(-1  ,   self.num_choices  ,   -1		).contiguous()
       lowercase       : str						=token_type_ids.unsqueeze(1		).expand(-1  ,   self.num_choices  ,   -1		).contiguous()
       lowercase       : Tuple						=input_mask.unsqueeze(1		).expand(-1  ,   self.num_choices  ,   -1		).contiguous()
       lowercase       : Dict						=model(
           UpperCAmelCase  ,   attention_mask=UpperCAmelCase  ,   token_type_ids=UpperCAmelCase  ,   labels=UpperCAmelCase  ,   )
       self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_choices)		)






      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : List[str]						=self.prepare_config_and_inputs()
       (
           (
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		(
           lowercase
       )	,		
       )       : Optional[int]						=config_and_inputs
       lowercase       : Tuple						={
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''lengths''': input_lengths,
           '''attention_mask''': input_mask,
       }
       return config, inputs_dict





@require_torch
class UpperCAmelCase_       (     __A					,							__A					,							unittest.TestCase					):




      """simple docstring"""




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






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Any		)	->	int:


       '''simple docstring'''

       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 					A__   (			self      :		Any  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Optional[int]=False		)	->	int:


       '''simple docstring'''

       lowercase       : List[str]						=super()._prepare_for_class(UpperCAmelCase  ,   UpperCAmelCase  ,   return_labels=UpperCAmelCase		)

       if return_labels:
        if model_class.__name__ == "FlaubertForQuestionAnswering":
         lowercase       : Union[str, Any]						=torch.zeros(
             self.model_tester.batch_size  ,   dtype=torch.long  ,   device=UpperCAmelCase		)
         lowercase       : Union[str, Any]						=torch.zeros(
             self.model_tester.batch_size  ,   dtype=torch.long  ,   device=UpperCAmelCase		)

       return inputs_dict






      def 					A__   (			self      :		Union[str, Any]		)	->	Any:


       '''simple docstring'''

       lowercase       : Tuple						=FlaubertModelTester(self		)
       lowercase       : Tuple						=ConfigTester(self  ,   config_class=UpperCAmelCase  ,   emb_dim=37		)






      def 					A__   (			self      :		Optional[int]		)	->	int:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 					A__   (			self      :		Optional[int]		)	->	int:


       '''simple docstring'''

       lowercase       : List[str]						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*UpperCAmelCase		)






      def 					A__   (			self      :		Dict		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : List[Any]						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*UpperCAmelCase		)






      def 					A__   (			self      :		Tuple		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Tuple						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_simple_qa(*UpperCAmelCase		)






      def 					A__   (			self      :		Optional[int]		)	->	str:


       '''simple docstring'''

       lowercase       : Any						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*UpperCAmelCase		)






      def 					A__   (			self      :		Any		)	->	int:


       '''simple docstring'''

       lowercase       : Optional[int]						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCAmelCase		)






      def 					A__   (			self      :		List[str]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_token_classif(*UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase       : Optional[int]						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_multiple_choice(*UpperCAmelCase		)






      @slow
      def 					A__   (			self      :		Dict		)	->	Optional[int]:


       '''simple docstring'''

       for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase       : Optional[Any]						=FlaubertModel.from_pretrained(UpperCAmelCase		)
        self.assertIsNotNone(UpperCAmelCase		)






      @slow
      @require_torch_gpu
      def 					A__   (			self      :		Optional[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       lowercase	,		lowercase       : Dict						=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

        lowercase       : List[str]						=True
        lowercase       : Optional[Any]						=model_class(config=UpperCAmelCase		)

        lowercase       : Tuple						=self._prepare_for_class(UpperCAmelCase  ,   UpperCAmelCase		)
        lowercase       : str						=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'''		)		)
         lowercase       : Optional[Any]						=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 UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      @slow
      def 					A__   (			self      :		List[str]		)	->	Dict:


       '''simple docstring'''

       lowercase       : str						=FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''		)
       lowercase       : Tuple						=torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
       with torch.no_grad():
        lowercase       : Optional[int]						=model(UpperCAmelCase		)[0]
       lowercase       : Dict						=torch.Size((1, 11, 768)		)
       self.assertEqual(output.shape  ,   UpperCAmelCase		)
       lowercase       : Any						=torch.tensor(
           [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]		)

       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 UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      MvpTokenizer
      UpperCamelCase_             =      MvpTokenizerFast
      UpperCamelCase_             =      True
      UpperCamelCase_             =      filter_roberta_detectors






      def 					A__   (			self      :		Union[str, Any]		)	->	List[str]:


       '''simple docstring'''

       super().setUp()
       lowercase       : Dict						=[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       lowercase       : Tuple						=dict(zip(UpperCAmelCase  ,   range(len(UpperCAmelCase		)		)		)		)
       lowercase       : Optional[Any]						=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       lowercase       : List[Any]						={'''unk_token''': '''<unk>'''}

       lowercase       : Dict						=os.path.join(self.tmpdirname  ,   VOCAB_FILES_NAMES['''vocab_file''']		)
       lowercase       : Dict						=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 					A__   (			self      :		Union[str, Any]  ,   **UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       kwargs.update(self.special_tokens_map		)
       return self.tokenizer_class.from_pretrained(self.tmpdirname  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		List[str]  ,   **UpperCAmelCase      :		Any		)	->	Tuple:


       '''simple docstring'''

       kwargs.update(self.special_tokens_map		)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Any		)	->	Union[str, Any]:


       '''simple docstring'''

       return "lower newer", "lower newer"






      @cached_property
      def 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

       return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''		)






      @cached_property
      def 					A__   (			self      :		Any		)	->	int:


       '''simple docstring'''

       return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''		)






      @require_torch
      def 					A__   (			self      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       lowercase       : List[str]						=[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Union[str, Any]						=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		)
        lowercase       : Union[str, Any]						=batch.input_ids.tolist()[0]
        self.assertListEqual(UpperCAmelCase  ,   UpperCAmelCase		)
        # Test that special tokens are reset






      @require_torch
      def 					A__   (			self      :		Tuple		)	->	Any:


       '''simple docstring'''

       lowercase       : Any						=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Dict						=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 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Optional[Any]						=tokenizer(text_target=UpperCAmelCase  ,   max_length=32  ,   padding='''max_length'''  ,   return_tensors='''pt'''		)
        self.assertEqual(32  ,   targets['''input_ids'''].shape[1]		)






      @require_torch
      def 					A__   (			self      :		Union[str, Any]		)	->	Any:


       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Union[str, Any]						=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 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=['''A long paragraph for summarization.''']
       lowercase       : List[Any]						=[
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : List[str]						=tokenizer(UpperCAmelCase  ,   text_target=UpperCAmelCase  ,   return_tensors='''pt'''		)
        lowercase       : Optional[int]						=inputs['''input_ids''']
        lowercase       : Optional[Any]						=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 					A__   (			self      :		List[Any]		)	->	List[Any]:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
         lowercase       : Union[str, Any]						=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
         lowercase       : Tuple						=self.tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
         lowercase       : Optional[Any]						='''A, <mask> AllenNLP sentence.'''
         lowercase       : int						=tokenizer_r.encode_plus(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase  ,   return_token_type_ids=UpperCAmelCase		)
         lowercase       : List[Any]						=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''']		)  ,   )

         lowercase       : Any						=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
         lowercase       : str						=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>''']		)



