





"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{"vocab_file": "spiece.model"}

lowerCamelCase_       =		{
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

lowerCamelCase_       =		{"bert_for_seq_generation": 5_1_2}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_       :				List[int]     						=  []
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  ['''input_ids''', '''attention_mask''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="<s>"		,SCREAMING_SNAKE_CASE__="</s>"		,SCREAMING_SNAKE_CASE__="<unk>"		,SCREAMING_SNAKE_CASE__="<pad>"		,SCREAMING_SNAKE_CASE__="<::::>"		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

             # Add extra_ids to the special token list
             super().__init__(
                 bos_token=SCREAMING_SNAKE_CASE__		,eos_token=SCREAMING_SNAKE_CASE__		,unk_token=SCREAMING_SNAKE_CASE__		,pad_token=SCREAMING_SNAKE_CASE__		,sep_token=SCREAMING_SNAKE_CASE__		,sp_model_kwargs=self.sp_model_kwargs		,**SCREAMING_SNAKE_CASE__		,)

             __SCREAMING_SNAKE_CASE       :Dict		    =  vocab_file

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(SCREAMING_SNAKE_CASE__							)




       @property
       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             return self.sp_model.get_piece_size()




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab




       def __getstate__(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.__dict__.copy()
             __SCREAMING_SNAKE_CASE       :Dict		    =  None
             return state




       def __setstate__(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  d

             # for backward compatibility
             if not hasattr(self		,'''sp_model_kwargs'''							):
                   __SCREAMING_SNAKE_CASE       :str		    =  {}

             __SCREAMING_SNAKE_CASE       :str		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(self.vocab_file							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             return self.sp_model.encode(SCREAMING_SNAKE_CASE__		,out_type=SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__							)
             return token




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  []
             __SCREAMING_SNAKE_CASE       :List[str]		    =  ''''''
             for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__							) + token
                         __SCREAMING_SNAKE_CASE       :Dict		    =  []
                   else:
                         current_sub_tokens.append(SCREAMING_SNAKE_CASE__							)
             out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__							)
             return out_string.strip()




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)      ->				Tuple[str]:





             """simple docstring"""






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

             if os.path.abspath(self.vocab_file							) != os.path.abspath(SCREAMING_SNAKE_CASE__							) and os.path.isfile(self.vocab_file							):
                   copyfile(self.vocab_file		,SCREAMING_SNAKE_CASE__							)
             elif not os.path.isfile(self.vocab_file							):
                   with open(SCREAMING_SNAKE_CASE__		,'''wb'''							) as fi:
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  self.sp_model.serialized_model_proto()
                         fi.write(SCREAMING_SNAKE_CASE__							)

             return (out_vocab_file,)







"""simple docstring"""
import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 (
      NystromformerForMaskedLM,
      NystromformerForMultipleChoice,
      NystromformerForQuestionAnswering,
      NystromformerForSequenceClassification,
      NystromformerForTokenClassification,
      NystromformerModel,
  )
  from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=13		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=99		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=5		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=37		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=None		,)      ->				Dict:





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None
             if self.use_input_mask:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  random_attention_mask([self.batch_size, self.seq_length]							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None
             if self.use_token_type_ids:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  ids_tensor([self.batch_size, self.seq_length]		,self.type_vocab_size							)

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

             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_config()

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




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





             """simple docstring"""






             return NystromformerConfig(
                 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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  NystromformerModel(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Any		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  model(SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Any		    =  NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Dict		    =  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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Tuple		    =  NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Any		    =  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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.num_choices
             __SCREAMING_SNAKE_CASE       :Dict		    =  NystromformerForMultipleChoice(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :List[str]		    =  input_ids.unsqueeze(1							).expand(-1		,self.num_choices		,-1							).contiguous()
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  token_type_ids.unsqueeze(1							).expand(-1		,self.num_choices		,-1							).contiguous()
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  input_mask.unsqueeze(1							).expand(-1		,self.num_choices		,-1							).contiguous()
             __SCREAMING_SNAKE_CASE       :Dict		    =  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							)      ->				Any:





             """simple docstring"""






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






@require_torch
class  _SCREAMING_SNAKE_CASE(       A   ,     A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  (
           (
               NystromformerModel,
               NystromformerForMaskedLM,
               NystromformerForMultipleChoice,
               NystromformerForQuestionAnswering,
               NystromformerForSequenceClassification,
               NystromformerForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  (
           {
               '''feature-extraction''': NystromformerModel,
               '''fill-mask''': NystromformerForMaskedLM,
               '''question-answering''': NystromformerForQuestionAnswering,
               '''text-classification''': NystromformerForSequenceClassification,
               '''token-classification''': NystromformerForTokenClassification,
               '''zero-shot''': NystromformerForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_       :				str     						=  False
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  False




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  NystromformerModelTester(self							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ConfigTester(self		,config_class=SCREAMING_SNAKE_CASE__		,hidden_size=37							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   __SCREAMING_SNAKE_CASE       :Any		    =  type
                   self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__							)




       @slow
       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






@require_torch
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @slow
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  torch.tensor([[0, 1, 2, 3, 4, 5]]							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE       :str		    =  model(SCREAMING_SNAKE_CASE__							)[0]

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.Size((1, 6, 7_68)							)
             self.assertEqual(output.shape		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  torch.tensor(
                 [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]							)

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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  '''the [MASK] of Belgium is Brussels'''

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''							)
             __SCREAMING_SNAKE_CASE       :str		    =  NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model(encoding.input_ids							).logits

             __SCREAMING_SNAKE_CASE       :List[str]		    =  token_logits[:, 2, :].argmax(-1							)[0]

             self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE__							)		,'''capital'''							)