



'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			RobertaTokenizer
 __lowerCamelCase       :			List[Any]		=			RobertaTokenizerFast
 __lowerCamelCase       :			Union[str, Any]		=			True
 __lowerCamelCase       :			Dict		=			{'cls_token': '<s>'}



 def    a__    (self			)       ->	int:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    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(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	Dict:


      """simple docstring"""



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



 def    a__    (self					,  **A			)       ->	str:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    '''lower newer'''
      _a           =    '''lower newer'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''lower newer'''
      _a           =    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      _a           =    tokenizer.tokenize(A			)  # , add_prefix_space=True)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]
      _a           =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      self.assertListEqual(tokenizer.encode('''Hello world!'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 2]			)
      self.assertListEqual(
          tokenizer.encode('''Hello world! cécé herlolip 418'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]					,  )



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


      """simple docstring"""



      _a           =    self.tokenizer_class.from_pretrained('''roberta-base'''			)

      _a           =    tokenizer.encode('''sequence builders'''					,  add_special_tokens=A			)
      _a           =    tokenizer.encode('''multi-sequence build'''					,  add_special_tokens=A			)

      _a           =    tokenizer.encode(
          '''sequence builders'''					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.encode(
          '''sequence builders'''					,  '''multi-sequence build'''					,  add_special_tokens=A					,  add_prefix_space=A			)

      _a           =    tokenizer.build_inputs_with_special_tokens(A			)
      _a           =    tokenizer.build_inputs_with_special_tokens(A					,  A			)

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      _a           =    '''Encode this sequence.'''
      _a           =    tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

      # Testing encoder arguments
      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertNotEqual(A					,  A			)

      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertEqual(A					,  A			)

      tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
      _a           =    tokenizer.encode(A					,  add_special_tokens=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
      self.assertNotEqual(A					,  A			)

      # Testing spaces after special tokens
      _a           =    '''<mask>'''
      tokenizer.add_special_tokens(
          {'''mask_token''': AddedToken(A					,  lstrip=A					,  rstrip=A			)}			)  # mask token has a left space
      _a           =    tokenizer.convert_tokens_to_ids(A			)

      _a           =    '''Encode <mask> sequence'''
      _a           =    '''Encode <mask>sequence'''

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertEqual(A					,  A			)

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertNotEqual(A					,  A			)



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


      """simple docstring"""



      pass



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    self.rust_tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    self.tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    '''A, <mask> AllenNLP sentence.'''
                _a           =    tokenizer_r.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)
                _a           =    tokenizer_p.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)

                # 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''']			)					,  )

                _a           =    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
                _a           =    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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)
                self.assertSequenceEqual(tokens_r['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)

                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      for trim_offsets, add_prefix_space in itertools.product([True, False]					,  repeat=2			):
           _a           =    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)

           _a           =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
           _a           =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

           self.assertEqual(pre_tokenizer_state['''add_prefix_space''']					,  A			)

           self.assertEqual(post_processor_state['''add_prefix_space''']					,  A			)
           self.assertEqual(post_processor_state['''trim_offsets''']					,  A			)



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


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                _a           =    f'''{text_of_1_token} {text_of_1_token}'''

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    f''' {text}'''

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (1, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			) + 1, 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )





'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
     import torch


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


snake_case									=    logging.get_logger(__name__)
snake_case									=    {"""vocab_file""": """spiece.model"""}

snake_case									=    {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

snake_case									=    {
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Dict	=			VOCAB_FILES_NAMES
 A_				:				Optional[int]	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Optional[Any]	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				Union[str, Any]	=			["""input_ids""", """attention_mask"""]


 def __init__(    self  : List[str]			,							a__  : Any			,							a__  : Dict=False			,							a__  : List[Any]=False			,							a__  : Dict=False			,							a__  : Any=None			,							a__  : Tuple=None			,							a__  : str=None			,							a__  : Optional[int]=None			,							a__  : Optional[Dict[str, Any]] = None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	{} if sp_model_kwargs is None else sp_model_kwargs

  lowerCAmelCase__     :   List[str]     		=	kwargs.get("name_or_path"    )
  if name_or_path is None:
   logger.warning(
       "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
       " you are testing the model, this can safely be ignored"    )
   lowerCAmelCase__     :   Dict     		=	"None"

  # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
  lowerCAmelCase__     :   Tuple     		=	"<|endoftext|>" if eos_token is None else eos_token
  lowerCAmelCase__     :   List[str]     		=	"<unk>" if unk_token is None else unk_token
  if "gpt-sw3-7b" in name_or_path:
   lowerCAmelCase__     :   List[str]     		=	unk_token if pad_token is None else pad_token
   lowerCAmelCase__     :   Any     		=	eos_token if bos_token is None else bos_token
  else:
   lowerCAmelCase__     :   str     		=	"<pad>" if pad_token is None else pad_token
   lowerCAmelCase__     :   Union[str, Any]     		=	"<s>" if bos_token is None else bos_token

  super().__init__(
      do_lower_case=a__			,							remove_space=a__			,							keep_accents=a__			,							bos_token=a__			,							eos_token=a__			,							unk_token=a__			,							pad_token=a__			,							sp_model_kwargs=self.sp_model_kwargs			,							**a__			,							)

  lowerCAmelCase__     :   Dict     		=	do_lower_case
  lowerCAmelCase__     :   Union[str, Any]     		=	remove_space
  lowerCAmelCase__     :   Any     		=	keep_accents
  lowerCAmelCase__     :   List[str]     		=	vocab_file

  lowerCAmelCase__     :   Dict     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(a__    )

  # Used for whitespace normalization in input texts
  # fmt : off
  lowerCAmelCase__     :   List[str]     		=	{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
  # fmt : on

  # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
  lowerCAmelCase__     :   Dict     		=	re.compile(
      F'''[{''.join(map(a__			,							list(range(0			,							9    )    ) + list(range(11			,							32    )    ) + list(range(127			,							160    )    ) + [160, 173, 8203]    )    )}]'''    )


 def __getstate__(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.__dict__.copy()
  lowerCAmelCase__     :   List[str]     		=	None
  return state


 def __setstate__(    self  : List[Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	d

  # for backward compatibility
  if not hasattr(self			,							"sp_model_kwargs"    ):
   lowerCAmelCase__     :   int     		=	{}

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


 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def    _A	(    self  : str    ):
  '''simple docstring'''

  return len(self.sp_model    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.non_printing_characters_re.sub(""			,							a__    )

  # Normalize whitespaces
  lowerCAmelCase__     :   int     		=	"".join([char if char not in self.whitespaces else " " for char in text]    )

  # NFC Unicode normalization
  lowerCAmelCase__     :   Tuple     		=	unicodedata.normalize("NFC"			,							a__    )
  return text


 def    _A	(    self  : str			,							a__  : str			,							**a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.preprocess_text(a__    )
  return self.sp_model.encode(a__			,							out_type=a__    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  return self.sp_model.PieceToId(a__    )


 def    _A	(    self  : Optional[int]			,							a__  : int    ):
  '''simple docstring'''

  return self.sp_model.IdToPiece(a__    )


 @staticmethod
 def    _A	(    a__  : str    ):
  '''simple docstring'''

  return out_string


 def    _A	(    self  : int			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	[]
  lowerCAmelCase__     :   Tuple     		=	""
  lowerCAmelCase__     :   str     		=	False
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
    if not prev_is_special:
     out_string += " "

    out_string += self.sp_model.decode(a__    ) + token
    lowerCAmelCase__     :   Union[str, Any]     		=	True
    lowerCAmelCase__     :   Dict     		=	[]
   else:
    current_sub_tokens.append(a__    )
    lowerCAmelCase__     :   Dict     		=	False
  out_string += self.sp_model.decode(a__    )

  return out_string


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def    _A	(    self  : str			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   List[str]     		=	os.path.join(
      a__			,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

  if os.path.abspath(self.vocab_file    ) != os.path.abspath(a__    ) and os.path.isfile(self.vocab_file    ):
   copyfile(self.vocab_file			,							a__    )
  elif not os.path.isfile(self.vocab_file    ):
   with open(a__			,							"wb"    ) as fi:
    lowerCAmelCase__     :   int     		=	self.sp_model.serialized_model_proto()
    fi.write(a__    )

  return (out_vocab_file,)


 def    _A	(    self  : List[Any]			,							a__  : Union[str, List[str]]			,							a__  : Union[str, bool] = False    ):
  '''simple docstring'''

  if isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   int     		=	self.preprocess_text(a__    )
   lowerCAmelCase__     :   Dict     		=	self.sp_model.encode(a__    )
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	[self.preprocess_text(a__    ) for t in text]
   lowerCAmelCase__     :   Optional[int]     		=	self.sp_model.encode(a__    )

  if return_tensors is True or return_tensors == "pt":
   lowerCAmelCase__     :   Union[str, Any]     		=	torch.tensor(a__    )

  return token_ids


 def    _A	(    self  : Dict			,							a__  : Union[int, List[int]]    ):
  '''simple docstring'''

  return self.sp_model.decode(a__    )


 def    _A	(    self  : int			,							a__  : "Conversation"    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
  lowerCAmelCase__     :   List[str]     		=	(
      F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(a__    ) + F'''{self.bos_token}Bot:'''
  )
  return self.encode(text=a__    )





