
"""simple docstring"""






from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )





class 				a__					:



     def __init__(							self :Union[str, Any]      ,  _lowerCamelCase :int      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=parent
      UpperCamelCase_     :					str   	=13
      UpperCamelCase_     :					List[str]   	=7
      UpperCamelCase_     :					str   	=30
      UpperCamelCase_     :					Dict   	=self.seq_length + self.mem_len
      UpperCamelCase_     :					List[Any]   	=15
      UpperCamelCase_     :					Tuple   	=True
      UpperCamelCase_     :					Union[str, Any]   	=True
      UpperCamelCase_     :					str   	=99
      UpperCamelCase_     :					List[str]   	=[10, 50, 80]
      UpperCamelCase_     :					List[str]   	=32
      UpperCamelCase_     :					int   	=32
      UpperCamelCase_     :					Dict   	=4
      UpperCamelCase_     :					Union[str, Any]   	=8
      UpperCamelCase_     :					Dict   	=128
      UpperCamelCase_     :					List[Any]   	=2
      UpperCamelCase_     :					Dict   	=2
      UpperCamelCase_     :					List[Any]   	=None
      UpperCamelCase_     :					List[str]   	=1
      UpperCamelCase_     :					Optional[int]   	=0
      UpperCamelCase_     :					List[Any]   	=3
      UpperCamelCase_     :					List[Any]   	=self.vocab_size - 1
      UpperCamelCase_     :					int   	=0.01



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size   )
      UpperCamelCase_     :					List[str]   	=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size   )

      UpperCamelCase_     :					Optional[Any]   	=None
      if self.use_labels:
       UpperCamelCase_     :					List[str]   	=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size   )

      UpperCamelCase_     :					Dict   	=TransfoXLConfig(
          vocab_size=self.vocab_size      ,  mem_len=self.mem_len      ,  clamp_len=self.clamp_len      ,  cutoffs=self.cutoffs      ,  d_model=self.hidden_size      ,  d_embed=self.d_embed      ,  n_head=self.num_attention_heads      ,  d_head=self.d_head      ,  d_inner=self.d_inner      ,  div_val=self.div_val      ,  n_layer=self.num_hidden_layers      ,  eos_token_id=self.eos_token_id      ,  pad_token_id=self.vocab_size - 1      ,  init_range=self.init_range      ,  num_labels=self.num_labels      ,  )

      return (config, input_ids_a, input_ids_a, lm_labels)



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      random.seed(self.seed   )
      tf.random.set_seed(self.seed   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=TFTransfoXLModel(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=model(_lowerCamelCase   ).to_tuple()

      UpperCamelCase_     :					List[str]   	={'input_ids': input_ids_a, 'mems': mems_a}

      UpperCamelCase_	,     UpperCamelCase_     :					int   	=model(_lowerCamelCase   ).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)   )
      self.parent.assertEqual(hidden_states_a.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)   )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,  )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,  )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=TFTransfoXLLMHeadModel(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					str   	=model(_lowerCamelCase   ).to_tuple()

      UpperCamelCase_     :					Union[str, Any]   	={'input_ids': input_ids_a, 'labels': lm_labels}
      UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=model(_lowerCamelCase   ).to_tuple()

      UpperCamelCase_	,     UpperCamelCase_     :					Any   	=model([input_ids_a, mems_a]   ).to_tuple()

      UpperCamelCase_     :					List[str]   	={'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

      UpperCamelCase_	,     UpperCamelCase_     :					Any   	=model(_lowerCamelCase   ).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)   )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,  )

      self.parent.assertEqual(lm_logits_a.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)   )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]      ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,  )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :int      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=TFTransfoXLForSequenceClassification(_lowerCamelCase   )
      UpperCamelCase_     :					Dict   	=model(_lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)   )




     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=self.prepare_config_and_inputs()
      ((UpperCamelCase_)	,     (UpperCamelCase_)	,     (UpperCamelCase_)	,     (UpperCamelCase_))     :					Union[str, Any]   	=config_and_inputs
      UpperCamelCase_     :					Dict   	={'input_ids': input_ids_a}
      return config, inputs_dict





@require_tf
class 				a__					(						A__   ,							A__   ,							unittest.TestCase       ):
     UpperCAmelCase__         =      (
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     UpperCAmelCase__         =      () if is_tf_available() else ()
     UpperCAmelCase__         =      (
         {
             '''feature-extraction''': TFTransfoXLModel,
             '''text-classification''': TFTransfoXLForSequenceClassification,
             '''text-generation''': TFTransfoXLLMHeadModel,
             '''zero-shot''': TFTransfoXLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      False



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if pipeline_test_casse_name == "TextGenerationPipelineTests":
       # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
       # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
       # tokenizer.
       return True

      return False



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=TFTransfoXLModelTester(self   )
      UpperCamelCase_     :					Union[str, Any]   	=ConfigTester(self      ,  config_class=_lowerCamelCase      ,  d_embed=37   )



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      self.config_tester.run_common_tests()



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      self.model_tester.set_seed()
      UpperCamelCase_     :					Tuple   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      self.model_tester.set_seed()
      UpperCamelCase_     :					Optional[int]   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase_     :					str   	=[TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
       UpperCamelCase_     :					List[Any]   	=model_class(_lowerCamelCase   )
       assert isinstance(model.get_input_embeddings()      ,  tf.keras.layers.Layer   )
       if model_class in list_other_models_with_output_ebd:
        UpperCamelCase_     :					List[Any]   	=model.get_output_embeddings()
        assert isinstance(_lowerCamelCase      ,  tf.keras.layers.Layer   )
        UpperCamelCase_     :					Tuple   	=model.get_bias()
        assert name is None
       else:
        UpperCamelCase_     :					Tuple   	=model.get_output_embeddings()
        assert x is None
        UpperCamelCase_     :					List[Any]   	=model.get_bias()
        assert name is None



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      pass



     @slow
     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase_     :					Optional[Any]   	=TFTransfoXLModel.from_pretrained(_lowerCamelCase   )
       self.assertIsNotNone(_lowerCamelCase   )




     @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'   )
     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      pass





@require_tf
class 				a__					(						unittest.TestCase       ):



     @unittest.skip('Skip test until #12651 is resolved.'   )
     @slow
     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'   )
      # fmt: off
      UpperCamelCase_     :					str   	=tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]      ,  dtype=tf.intaa   )  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      UpperCamelCase_     :					int   	=[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      UpperCamelCase_     :					Any   	=model.generate(_lowerCamelCase      ,  max_length=200      ,  do_sample=_lowerCamelCase   )
      self.assertListEqual(output_ids[0].numpy().tolist()      ,  _lowerCamelCase   )


"""simple docstring"""






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							=     {
    'nielsr/canine-s': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
__SCREAMING_SNAKE_CASE							=     1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__SCREAMING_SNAKE_CASE							=     0

__SCREAMING_SNAKE_CASE							=     0XE_0_0_0
__SCREAMING_SNAKE_CASE							=     0XE_0_0_1
__SCREAMING_SNAKE_CASE							=     0XE_0_0_2
__SCREAMING_SNAKE_CASE							=     0XE_0_0_3
__SCREAMING_SNAKE_CASE							=     0XE_0_0_4

# Maps special codepoints to human-readable names.
__SCREAMING_SNAKE_CASE							=     {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__SCREAMING_SNAKE_CASE							=     {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(							self :List[str]      ,  _lowerCamelCase :Optional[Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :List[str]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Any=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Optional[int]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Tuple=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Union[str, Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Union[str, Any]=2_048      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else bos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else eos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else sep_token
      UpperCamelCase_     :					Tuple   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else cls_token
      UpperCamelCase_     :					Optional[int]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCamelCase_     :					Dict   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  add_prefix_space=_lowerCamelCase      ,  model_max_length=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

      # Creates a mapping for looking up the IDs of special symbols.
      UpperCamelCase_     :					Dict[str, int]   	={}
      for codepoint, name in SPECIAL_CODEPOINTS.items():
       UpperCamelCase_     :					List[str]   	=codepoint

      # Creates a mapping for looking up the string forms of special symbol IDs.
      UpperCamelCase_     :					Dict[int, str]   	={
          codepoint: name for name, codepoint in self._special_codepoints.items()
      }

      UpperCamelCase_     :					str   	=UNICODE_VOCAB_SIZE
      UpperCamelCase_     :					Any   	=len(self._special_codepoints   )



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







      '''simple docstring'''



      return self._unicode_vocab_size



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return list(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      try:
       return ord(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid token: \'{token}\''''   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      try:
       if index in SPECIAL_CODEPOINTS:
        return SPECIAL_CODEPOINTS[index]
       return chr(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid id: {index}'''   )



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return "".join(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=[self.sep_token_id]
      UpperCamelCase_     :					Tuple   	=[self.cls_token_id]

      UpperCamelCase_     :					List[str]   	=cls + token_ids_a + sep
      if token_ids_a is not None:
       result += token_ids_a + sep
      return result



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _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   )

      UpperCamelCase_     :					int   	=[1] + ([0] * len(_lowerCamelCase   )) + [1]
      if token_ids_a is not None:
       result += ([0] * len(_lowerCamelCase   )) + [1]
      return result



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=[self.sep_token_id]
      UpperCamelCase_     :					Optional[int]   	=[self.cls_token_id]

      UpperCamelCase_     :					Dict   	=len(cls + token_ids_a + sep   ) * [0]
      if token_ids_a is not None:
       result += len(token_ids_a + sep   ) * [1]
      return result




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      return ()
