





import os
import tempfile
import unittest

from transformers import DistilBertConfig, 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 (
       DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       DistilBertForMaskedLM,
       DistilBertForMultipleChoice,
       DistilBertForQuestionAnswering,
       DistilBertForSequenceClassification,
       DistilBertForTokenClassification,
       DistilBertModel,
   )




class        _a    (    __A):

      """simple docstring"""


      def __init__(		self:			List[str]	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Tuple=13	,    __lowerCamelCase:			Dict=7	,    __lowerCamelCase:			int=True	,    __lowerCamelCase:			Union[str, Any]=True	,    __lowerCamelCase:			Dict=False	,    __lowerCamelCase:			List[Any]=True	,    __lowerCamelCase:			Dict=99	,    __lowerCamelCase:			Union[str, Any]=32	,    __lowerCamelCase:			Dict=5	,    __lowerCamelCase:			Tuple=4	,    __lowerCamelCase:			List[str]=37	,    __lowerCamelCase:			Tuple="gelu"	,    __lowerCamelCase:			List[str]=0.1	,    __lowerCamelCase:			Tuple=0.1	,    __lowerCamelCase:			int=512	,    __lowerCamelCase:			Optional[Any]=16	,    __lowerCamelCase:			Tuple=2	,    __lowerCamelCase:			Optional[int]=0.02	,    __lowerCamelCase:			Optional[int]=3	,    __lowerCamelCase:			Optional[int]=4	,    __lowerCamelCase:			List[Any]=None	,    ):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


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

          UpperCamelCase__:	Dict					=     None
          if self.use_input_mask:
              UpperCamelCase__:	Optional[Any]					=     random_attention_mask([self.batch_size, self.seq_length]					)

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

          UpperCamelCase__:	Tuple					=     self.get_config()

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






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return DistilBertConfig(
              vocab_size=self.vocab_size	,    dim=self.hidden_size	,    n_layers=self.num_hidden_layers	,    n_heads=self.num_attention_heads	,    hidden_dim=self.intermediate_size	,    hidden_act=self.hidden_act	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    initializer_range=self.initializer_range	,    )






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     DistilBertModel(config=__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	List[str]					=     model(__lowerCamelCase	,    __lowerCamelCase					)
          UpperCamelCase__:	Dict					=     model(__lowerCamelCase					)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)					)






      def 			UpperCAmelCase_       (		self:			Optional[int]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			List[Any]	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     DistilBertForMaskedLM(config=__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	Optional[Any]					=     model(__lowerCamelCase	,    attention_mask=__lowerCamelCase	,    labels=__lowerCamelCase					)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)					)






      def 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			Optional[int]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			int	,    __lowerCamelCase:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     DistilBertForQuestionAnswering(config=__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	Any					=     model(
              __lowerCamelCase	,    attention_mask=__lowerCamelCase	,    start_positions=__lowerCamelCase	,    end_positions=__lowerCamelCase					)
          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:			Any	,    __lowerCamelCase:			Optional[int]	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.num_labels
          UpperCamelCase__:	int					=     DistilBertForSequenceClassification(__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	Any					=     model(__lowerCamelCase	,    attention_mask=__lowerCamelCase	,    labels=__lowerCamelCase					)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)					)






      def 			UpperCAmelCase_       (		self:			Optional[int]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			str	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.num_labels
          UpperCamelCase__:	List[str]					=     DistilBertForTokenClassification(config=__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()

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






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.num_choices
          UpperCamelCase__:	Any					=     DistilBertForMultipleChoice(config=__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	Union[str, Any]					=     input_ids.unsqueeze(1					).expand(-1	,    self.num_choices	,    -1					).contiguous()
          UpperCamelCase__:	str					=     input_mask.unsqueeze(1					).expand(-1	,    self.num_choices	,    -1					).contiguous()
          UpperCamelCase__:	str					=     model(
              __lowerCamelCase	,    attention_mask=__lowerCamelCase	,    labels=__lowerCamelCase	,    )
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_choices)					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     self.prepare_config_and_inputs()
          (UpperCamelCase__):	List[str]					=     config_and_inputs
          UpperCamelCase__:	Union[str, Any]					=     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




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

      """simple docstring"""


      UpperCamelCase__  		=       (
          (
              DistilBertModel,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      UpperCamelCase__  		=       (
          {
              """feature-extraction""": DistilBertModel,
              """fill-mask""": DistilBertForMaskedLM,
              """question-answering""": DistilBertForQuestionAnswering,
              """text-classification""": DistilBertForSequenceClassification,
              """token-classification""": DistilBertForTokenClassification,
              """zero-shot""": DistilBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase__  		=       True
      UpperCamelCase__  		=       True
      UpperCamelCase__  		=       True
      UpperCamelCase__  		=       True






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     DistilBertModelTester(self					)
          UpperCamelCase__:	Union[str, Any]					=     ConfigTester(self	,    config_class=__lowerCamelCase	,    dim=37					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          self.config_tester.run_common_tests()






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_model(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_masked_lm(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_question_answering(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_sequence_classification(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_token_classification(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_multiple_choice(*__lowerCamelCase					)






      @slow
      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase__:	List[str]					=     DistilBertModel.from_pretrained(__lowerCamelCase					)
              self.assertIsNotNone(__lowerCamelCase					)






      @slow
      @require_torch_gpu
      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              # BertForMultipleChoice behaves incorrectly in JIT environments.
              if model_class == DistilBertForMultipleChoice:
                  return

              UpperCamelCase__:	Optional[Any]					=     True
              UpperCamelCase__:	Tuple					=     model_class(config=__lowerCamelCase					)

              UpperCamelCase__:	Tuple					=     self._prepare_for_class(__lowerCamelCase	,    __lowerCamelCase					)
              UpperCamelCase__:	Tuple					=     torch.jit.trace(
                  __lowerCamelCase	,    (inputs_dict["input_ids"].to("cpu"					), inputs_dict["attention_mask"].to("cpu"					))					)

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




@require_torch
class        _a    (    unittest.TestCase):

      """simple docstring"""


      @slow
      def 			UpperCAmelCase_       (		self:			str					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     DistilBertModel.from_pretrained("distilbert-base-uncased"					)
          UpperCamelCase__:	List[str]					=     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
          UpperCamelCase__:	List[Any]					=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
          with torch.no_grad():
              UpperCamelCase__:	Any					=     model(__lowerCamelCase	,    attention_mask=__lowerCamelCase					)[0]
          UpperCamelCase__:	Union[str, Any]					=     torch.Size((1, 11, 768)					)
          self.assertEqual(output.shape	,    __lowerCamelCase					)
          UpperCamelCase__:	Union[str, Any]					=     torch.tensor(
              [[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]					)

          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,    __lowerCamelCase	,    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>''']		)



