






"""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 						__UpperCamelCase     :
					def __init__(							self							:	Dict     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Any=13     , UpperCAmelCase							:	Union[str, Any]=7     , UpperCAmelCase							:	str=True     , UpperCAmelCase							:	int=True     , UpperCAmelCase							:	Any=True     , UpperCAmelCase							:	Optional[Any]=True     , UpperCAmelCase							:	List[str]=99     , UpperCAmelCase							:	Optional[int]=32     , UpperCAmelCase							:	str=5     , UpperCAmelCase							:	Tuple=4     , UpperCAmelCase							:	List[str]=37     , UpperCAmelCase							:	Any="gelu"     , UpperCAmelCase							:	str=0.1     , UpperCAmelCase							:	str=0.1     , UpperCAmelCase							:	int=512     , UpperCAmelCase							:	Tuple=16     , UpperCAmelCase							:	List[Any]=2     , UpperCAmelCase							:	str=0.0_2     , UpperCAmelCase							:	Any=3     , UpperCAmelCase							:	Optional[int]=4     , UpperCAmelCase							:	Union[str, Any]=None     , )	->       str:
											lowerCAmelCase   :Optional[int]						=			parent
											lowerCAmelCase   :Optional[int]						=			batch_size
											lowerCAmelCase   :Optional[Any]						=			seq_length
											lowerCAmelCase   :List[Any]						=			is_training
											lowerCAmelCase   :Any						=			use_input_mask
											lowerCAmelCase   :int						=			use_token_type_ids
											lowerCAmelCase   :Union[str, Any]						=			use_labels
											lowerCAmelCase   :Tuple						=			vocab_size
											lowerCAmelCase   :str						=			hidden_size
											lowerCAmelCase   :List[Any]						=			num_hidden_layers
											lowerCAmelCase   :str						=			num_attention_heads
											lowerCAmelCase   :List[Any]						=			intermediate_size
											lowerCAmelCase   :List[str]						=			hidden_act
											lowerCAmelCase   :Tuple						=			hidden_dropout_prob
											lowerCAmelCase   :List[str]						=			attention_probs_dropout_prob
											lowerCAmelCase   :Dict						=			max_position_embeddings
											lowerCAmelCase   :Dict						=			type_vocab_size
											lowerCAmelCase   :Union[str, Any]						=			type_sequence_label_size
											lowerCAmelCase   :Optional[Any]						=			initializer_range
											lowerCAmelCase   :Optional[Any]						=			num_labels
											lowerCAmelCase   :int						=			num_choices
											lowerCAmelCase   :str						=			scope
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											lowerCAmelCase   :Optional[int]						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )

											lowerCAmelCase   :str						=			None
											if self.use_input_mask:
																	lowerCAmelCase   :Optional[int]						=			random_attention_mask([self.batch_size, self.seq_length]   )

											lowerCAmelCase   :Optional[int]						=			None
											if self.use_token_type_ids:
																	lowerCAmelCase   :str						=			ids_tensor([self.batch_size, self.seq_length]     , self.type_vocab_size   )

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

											lowerCAmelCase   :Optional[int]						=			self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def        UpperCAmelCase__ (							self							:	Any   )	->       int:
											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=__lowerCamelCase     , initializer_range=self.initializer_range     , )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Optional[int]   )	->       Union[str, Any]:
											lowerCAmelCase   :str						=			NystromformerModel(config=__lowerCamelCase   )
											model.to(__lowerCamelCase   )
											model.eval()
											lowerCAmelCase   :Any						=			model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase   )
											lowerCAmelCase   :Dict						=			model(__lowerCamelCase     , token_type_ids=__lowerCamelCase   )
											lowerCAmelCase   :int						=			model(__lowerCamelCase   )
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)   )
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	int   )	->       Optional[int]:
											lowerCAmelCase   :List[Any]						=			NystromformerForMaskedLM(config=__lowerCamelCase   )
											model.to(__lowerCamelCase   )
											model.eval()
											lowerCAmelCase   :List[Any]						=			model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase   )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)   )
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	Dict   )	->       Tuple:
											lowerCAmelCase   :List[str]						=			NystromformerForQuestionAnswering(config=__lowerCamelCase   )
											model.to(__lowerCamelCase   )
											model.eval()
											lowerCAmelCase   :int						=			model(
											    __lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__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							:	Dict     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	int     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[Any]   )	->       str:
											lowerCAmelCase   :Optional[int]						=			self.num_labels
											lowerCAmelCase   :int						=			NystromformerForSequenceClassification(__lowerCamelCase   )
											model.to(__lowerCamelCase   )
											model.eval()
											lowerCAmelCase   :Union[str, Any]						=			model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase   )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Optional[int]   )	->       str:
											lowerCAmelCase   :List[str]						=			self.num_labels
											lowerCAmelCase   :Dict						=			NystromformerForTokenClassification(config=__lowerCamelCase   )
											model.to(__lowerCamelCase   )
											model.eval()
											lowerCAmelCase   :List[Any]						=			model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase   )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	int     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Optional[Any]   )	->       str:
											lowerCAmelCase   :Any						=			self.num_choices
											lowerCAmelCase   :Optional[Any]						=			NystromformerForMultipleChoice(config=__lowerCamelCase   )
											model.to(__lowerCamelCase   )
											model.eval()
											lowerCAmelCase   :List[str]						=			input_ids.unsqueeze(1   ).expand(-1     , self.num_choices     , -1   ).contiguous()
											lowerCAmelCase   :Tuple						=			token_type_ids.unsqueeze(1   ).expand(-1     , self.num_choices     , -1   ).contiguous()
											lowerCAmelCase   :Any						=			input_mask.unsqueeze(1   ).expand(-1     , self.num_choices     , -1   ).contiguous()
											lowerCAmelCase   :Tuple						=			model(
											    __lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase     , )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       str:
											lowerCAmelCase   :List[Any]						=			self.prepare_config_and_inputs()
											(
											    (
											    lowerCAmelCase     
											)	, (
											    lowerCAmelCase     
											)	, (
											    lowerCAmelCase     
											)	, (
											    lowerCAmelCase     
											)	, (
											    lowerCAmelCase     
											)	, (
											    lowerCAmelCase     
											)	, (
											    lowerCAmelCase     
											)	,      
											)   :Optional[int]						=			config_and_inputs
											lowerCAmelCase   :Optional[Any]						=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict




@require_torch
class 						__UpperCamelCase     (     __SCREAMING_SNAKE_CASE   ,     __SCREAMING_SNAKE_CASE   ,     unittest.TestCase						):
					lowercase_    :				Tuple      =  (
					    (
					        NystromformerModel,
					        NystromformerForMaskedLM,
					        NystromformerForMultipleChoice,
					        NystromformerForQuestionAnswering,
					        NystromformerForSequenceClassification,
					        NystromformerForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase_    :				List[str]      =  (
					    {
					        '''feature-extraction''': NystromformerModel,
					        '''fill-mask''': NystromformerForMaskedLM,
					        '''question-answering''': NystromformerForQuestionAnswering,
					        '''text-classification''': NystromformerForSequenceClassification,
					        '''token-classification''': NystromformerForTokenClassification,
					        '''zero-shot''': NystromformerForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase_    :				Optional[int]      =  False
					lowercase_    :				List[Any]      =  False
					def        UpperCAmelCase__ (							self							:	int   )	->       Tuple:
											lowerCAmelCase   :Union[str, Any]						=			NystromformerModelTester(self   )
											lowerCAmelCase   :Tuple						=			ConfigTester(self     , config_class=__lowerCamelCase     , hidden_size=37   )
					def        UpperCAmelCase__ (							self							:	int   )	->       int:
											self.config_tester.run_common_tests()
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[Any]:
											lowerCAmelCase   :Dict						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowerCamelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Optional[Any]:
											lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowerCAmelCase   :Optional[Any]						=			type
																	self.model_tester.create_and_check_model(*__lowerCamelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Tuple:
											lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       int:
											lowerCAmelCase   :Tuple						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[Any]:
											lowerCAmelCase   :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase   )
					def        UpperCAmelCase__ (							self							:	str   )	->       Optional[int]:
											lowerCAmelCase   :Optional[int]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Tuple:
											lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase   )
					@slow
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       str:
											for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCAmelCase   :List[str]						=			NystromformerModel.from_pretrained(__lowerCamelCase   )
																	self.assertIsNotNone(__lowerCamelCase   )




@require_torch
class 						__UpperCamelCase     (     unittest.TestCase						):
					@slow
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Union[str, Any]:
											lowerCAmelCase   :int						=			NystromformerModel.from_pretrained('uw-madison/nystromformer-512'   )
											lowerCAmelCase   :Union[str, Any]						=			torch.tensor([[0, 1, 2, 3, 4, 5]]   )

											with torch.no_grad():
																	lowerCAmelCase   :str						=			model(__lowerCamelCase   )[0]

											lowerCAmelCase   :int						=			torch.Size((1, 6, 768)   )
											self.assertEqual(output.shape     , __lowerCamelCase   )

											lowerCAmelCase   :List[str]						=			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]     , __lowerCamelCase     , atol=1e-4   )   )
					@slow
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict:
											lowerCAmelCase   :Any						=			'the [MASK] of Belgium is Brussels'

											lowerCAmelCase   :Tuple						=			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'   )
											lowerCAmelCase   :Any						=			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'   )

											lowerCAmelCase   :Union[str, Any]						=			tokenizer(__lowerCamelCase     , return_tensors='pt'   )

											with torch.no_grad():
																	lowerCAmelCase   :str						=			model(encoding.input_ids   ).logits

											lowerCAmelCase   :Union[str, Any]						=			token_logits[:, 2, :].argmax(-1   )[0]

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




"""simple docstring"""






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

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


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt'''}

snake_case   					=				{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

snake_case   					=				{
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  _snake_case   	=      collections.OrderedDict()
  with open(lowerCAmelCase_  ,						'''r'''  ,						encoding='''utf-8'''	) as reader:
    _snake_case   	=      reader.readlines()
  for index, token in enumerate(lowerCAmelCase_	):
    _snake_case   	=      token.rstrip('''\n'''	)
    _snake_case   	=      index
  return vocab



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	List[str]="<unk>"							,       __lowerCamelCase    :	Tuple=2_0_0				):



     """simple docstring"""
     _snake_case   	=      vocab
     _snake_case   	=      unk_token
     _snake_case   	=      max_input_chars_per_word





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      list(__lowerCamelCase				)
     if len(__lowerCamelCase				) > self.max_input_chars_per_word:
       return [self.unk_token]

     _snake_case   	=      0
     _snake_case   	=      []
     while start < len(__lowerCamelCase				):
       _snake_case   	=      len(__lowerCamelCase				)
       _snake_case   	=      None
       while start < end:
         _snake_case   	=      ''''''.join(chars[start:end]				)
         if substr in self.vocab:
           _snake_case   	=      substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token				)
         start += 1
       else:
         sub_tokens.append(__lowerCamelCase				)
         _snake_case   	=      end

     return sub_tokens



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	List[str]           =  VOCAB_FILES_NAMES
   A__		:	str           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	str           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Union[str, Any]           =  ['''input_ids''', '''attention_mask''']
   A__		:	Optional[int]           =  False



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	str="<d>"							,       __lowerCamelCase    :	Tuple="</d>"							,       __lowerCamelCase    :	Tuple="<s>"							,       __lowerCamelCase    :	int="</s>"							,       __lowerCamelCase    :	List[str]="<pad>"							,       __lowerCamelCase    :	int="<unk>"							,       __lowerCamelCase    :	int="</n>"							,       __lowerCamelCase    :	Tuple="</_>"							,       __lowerCamelCase    :	Optional[Any]="left"							,       **__lowerCamelCase    :	str							,       ):



     """simple docstring"""
     requires_backends(self							,       ['''jieba''']				)
     super().__init__(
         bod_token=__lowerCamelCase							,       eod_token=__lowerCamelCase							,       bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       line_token=__lowerCamelCase							,       space_token=__lowerCamelCase							,       padding_side=__lowerCamelCase							,       **__lowerCamelCase							,       )
     _snake_case   	=      bod_token
     _snake_case   	=      eod_token
     _snake_case   	=      load_vocab(__lowerCamelCase				)
     _snake_case   	=      self.encoder[space_token]
     _snake_case   	=      self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     _snake_case   	=      {v: k for k, v in self.encoder.items()}

     _snake_case   	=      WordpieceTokenizer(vocab=self.encoder							,       unk_token=self.unk_token				)



   @property
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return self.encoder[self.bod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return self.encoder[self.eod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     return self.encoder["\n"]



   @property
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     return len(self.encoder				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return dict(self.encoder							,       **self.added_tokens_encoder				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      []
     for x in jieba.cut(__lowerCamelCase							,       cut_all=__lowerCamelCase				):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCamelCase				)				)
     return output_tokens



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      [i for i in token_ids if i >= 0]
     _snake_case   	=      [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return token in self.encoder



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return "".join(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.encoder.get(__lowerCamelCase							,       self.encoder.get(self.unk_token				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.decoder.get(__lowerCamelCase							,       self.unk_token				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if os.path.isdir(__lowerCamelCase				):
       _snake_case   	=      os.path.join(
           __lowerCamelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
     else:
       _snake_case   	=      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
     _snake_case   	=      0
     if " " in self.encoder:
       _snake_case   	=      self.encoder[''' ''']
       del self.encoder[" "]
     if "\n" in self.encoder:
       _snake_case   	=      self.encoder['''\n''']
       del self.encoder["\n"]
     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               ''' Please check that the vocabulary is not corrupted!'''				)
           _snake_case   	=      token_index
         writer.write(token + '''\n'''				)
         index += 1
     return (vocab_file,)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	List[int] = None				):



     """simple docstring"""
     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None							,       __lowerCamelCase    :	bool = False				):



     """simple docstring"""

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCamelCase							,       token_ids_a=__lowerCamelCase							,       already_has_special_tokens=__lowerCamelCase				)

     if token_ids_a is not None:
       return [1] + ([0] * len(__lowerCamelCase				)) + [1] + ([0] * len(__lowerCamelCase				))
     return [1] + ([0] * len(__lowerCamelCase				))





