




"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

A_      			=       logging.get_logger(__name__)


A_      			=       {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A_      			=       {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

A_      			=       {'''facebook/blenderbot-3B''': 1_28}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Tuple			=						(
      list(range(ord("""!"""					)	,     ord("""~"""					) + 1					)					) + list(range(ord("""¡"""					)	,     ord("""¬"""					) + 1					)					) + list(range(ord("""®"""					)	,     ord("""ÿ"""					) + 1					)					)
  )
  _snake_case							:	Optional[Any]			=						bs[:]
  _snake_case							:	int			=						0
  for b in range(2**8					):
    if b not in bs:
      bs.append(__UpperCamelCase					)
      cs.append(2**8 + n					)
      n += 1
  _snake_case							:	List[Any]			=						[chr(__UpperCamelCase					) for n in cs]
  return dict(zip(__UpperCamelCase	,     __UpperCamelCase					)					)


def       UpperCAmelCase__ (snake_case__							:      List[str]					):
  """simple docstring"""

  _snake_case							:	Any			=						set()
  _snake_case							:	Dict			=						word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)					)
    _snake_case							:	Optional[Any]			=						char
  return pairs






class       lowercase(  SCREAMING_SNAKE_CASE__				):







 '''simple docstring'''





 lowercase__				      =      VOCAB_FILES_NAMES
 lowercase__				      =      PRETRAINED_VOCAB_FILES_MAP
 lowercase__				      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__				      =      ["input_ids", "attention_mask"]


 def __init__(       self:	Tuple,   a_:	Union[str, Any],   a_:	Tuple,   a_:	Union[str, Any]="replace",   a_:	Optional[int]="<s>",   a_:	List[str]="</s>",   a_:	List[str]="</s>",   a_:	Union[str, Any]="<s>",   a_:	Tuple="<unk>",   a_:	List[Any]="<pad>",   a_:	Tuple="<mask>",   a_:	Optional[Any]=False,   **a_:	Optional[int],   ):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						AddedToken(_lowercase,   lstrip=_lowercase,   rstrip=_lowercase	) if isinstance(_lowercase,   _lowercase	) else bos_token
   _snake_case							:	Union[str, Any]			=						AddedToken(_lowercase,   lstrip=_lowercase,   rstrip=_lowercase	) if isinstance(_lowercase,   _lowercase	) else eos_token
   _snake_case							:	List[Any]			=						AddedToken(_lowercase,   lstrip=_lowercase,   rstrip=_lowercase	) if isinstance(_lowercase,   _lowercase	) else sep_token
   _snake_case							:	List[str]			=						AddedToken(_lowercase,   lstrip=_lowercase,   rstrip=_lowercase	) if isinstance(_lowercase,   _lowercase	) else cls_token
   _snake_case							:	Tuple			=						AddedToken(_lowercase,   lstrip=_lowercase,   rstrip=_lowercase	) if isinstance(_lowercase,   _lowercase	) else unk_token
   _snake_case							:	Dict			=						AddedToken(_lowercase,   lstrip=_lowercase,   rstrip=_lowercase	) if isinstance(_lowercase,   _lowercase	) else pad_token

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

   super().__init__(
       errors=_lowercase,   bos_token=_lowercase,   eos_token=_lowercase,   unk_token=_lowercase,   sep_token=_lowercase,   cls_token=_lowercase,   pad_token=_lowercase,   mask_token=_lowercase,   add_prefix_space=_lowercase,   **_lowercase,   )

   with open(_lowercase,   encoding="""utf-8"""	) as vocab_handle:
     _snake_case							:	str			=						json.load(_lowercase	)
   _snake_case							:	Optional[int]			=						{v: k for k, v in self.encoder.items()}
   _snake_case							:	List[str]			=						errors  # how to handle errors in decoding
   _snake_case							:	Any			=						bytes_to_unicode()
   _snake_case							:	Union[str, Any]			=						{v: k for k, v in self.byte_encoder.items()}
   with open(_lowercase,   encoding="""utf-8"""	) as merges_handle:
     _snake_case							:	Union[str, Any]			=						merges_handle.read().split("""\n"""	)[1:-1]
   _snake_case							:	Optional[Any]			=						[tuple(merge.split()	) for merge in bpe_merges]
   _snake_case							:	int			=						dict(zip(_lowercase,   range(len(_lowercase	)	)	)	)
   _snake_case							:	Any			=						{}
   _snake_case							:	Union[str, Any]			=						add_prefix_space

   # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
   _snake_case							:	List[str]			=						re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""	)


 @property
 # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   return len(self.encoder	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   return dict(self.encoder,   **self.added_tokens_encoder	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	Optional[Any]	):




   '''simple docstring'''



   if token in self.cache:
     return self.cache[token]
   _snake_case							:	str			=						tuple(_lowercase	)
   _snake_case							:	Optional[int]			=						get_pairs(_lowercase	)

   if not pairs:
     return token

   while True:
     _snake_case							:	str			=						min(_lowercase,   key=lambda a_	: self.bpe_ranks.get(_lowercase,   float("""inf"""	)	)	)
     if bigram not in self.bpe_ranks:
       break
     _snake_case							:	Tuple			=						bigram
     _snake_case							:	Any			=						[]
     _snake_case							:	List[str]			=						0
     while i < len(_lowercase	):
       try:
         _snake_case							:	Any			=						word.index(_lowercase,   _lowercase	)
       except ValueError:
         new_word.extend(word[i:]	)
         break
       else:
         new_word.extend(word[i:j]	)
         _snake_case							:	Optional[int]			=						j

       if word[i] == first and i < len(_lowercase	) - 1 and word[i + 1] == second:
         new_word.append(first + second	)
         i += 2
       else:
         new_word.append(word[i]	)
         i += 1
     _snake_case							:	Optional[int]			=						tuple(_lowercase	)
     _snake_case							:	int			=						new_word
     if len(_lowercase	) == 1:
       break
     else:
       _snake_case							:	List[Any]			=						get_pairs(_lowercase	)
   _snake_case							:	Optional[int]			=						""" """.join(_lowercase	)
   _snake_case							:	List[str]			=						word
   return word


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						[]
   for token in re.findall(self.pat,   _lowercase	):
     _snake_case							:	Dict			=						"""""".join(
         self.byte_encoder[b] for b in token.encode("""utf-8"""	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase	).split(""" """	)	)
   return bpe_tokens


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Tuple	):




   '''simple docstring'''



   return self.encoder.get(_lowercase,   self.encoder.get(self.unk_token	)	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	Optional[Any]	):




   '''simple docstring'''



   return self.decoder.get(_lowercase	)


 def 				UpperCamelCase_     (       self:	str,   a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Any			=						"""""".join(_lowercase	)
   _snake_case							:	Dict			=						bytearray([self.byte_decoder[c] for c in text]	).decode("""utf-8""",   errors=self.errors	)
   return text


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[Any],   a_:	Optional[int] = None	):




   '''simple docstring'''



   if not os.path.isdir(_lowercase	):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"	)
     return
   _snake_case							:	int			=						os.path.join(
       _lowercase,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
   _snake_case							:	Dict			=						os.path.join(
       _lowercase,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

   with open(_lowercase,   """w""",   encoding="""utf-8"""	) as f:
     f.write(json.dumps(self.encoder,   indent=2,   sort_keys=_lowercase,   ensure_ascii=_lowercase	) + """\n"""	)

   _snake_case							:	List[Any]			=						0
   with open(_lowercase,   """w""",   encoding="""utf-8"""	) as writer:
     writer.write("""#version: 0.2\n"""	)
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),   key=lambda a_	: kv[1]	):
       if index != token_index:
         logger.warning(
             f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
             """ Please check that the tokenizer is not corrupted!"""	)
         _snake_case							:	Dict			=						token_index
       writer.write(""" """.join(_lowercase	) + """\n"""	)
       index += 1

   return vocab_file, merge_file


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Optional[int],   a_:	List[Any] = None,   a_:	Any = False	):




   '''simple docstring'''



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

   if token_ids_a is None:
     return [1] + ([0] * len(_lowercase	)) + [1]
   return [1] + ([0] * len(_lowercase	)) + [1, 1] + ([0] * len(_lowercase	)) + [1]


 def 				UpperCamelCase_     (       self:	Dict,   a_:	List[Any],   a_:	str = None	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						[self.sep_token_id]
   _snake_case							:	Optional[Any]			=						[self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep	) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any],   a_:	Tuple=False,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						kwargs.pop("""add_prefix_space""",   self.add_prefix_space	)
   if (is_split_into_words or add_prefix_space) and (len(_lowercase	) > 0 and not text[0].isspace()):
     _snake_case							:	int			=						""" """ + text
   return (text, kwargs)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Union[str, Any],   a_:	Any = None	):




   '''simple docstring'''



   return token_ids_a + [self.eos_token_id]


 def 				UpperCamelCase_     (       self:	str,   a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	str			=						[]
   for is_user, text in conversation.iter_texts():
     if is_user:
       # We need to space prefix as it's being done within blenderbot
       inputs.append(""" """ + text	)
     else:
       # Generated responses should contain them already.
       inputs.append(_lowercase	)

   _snake_case							:	Tuple			=						"""  """.join(_lowercase	)
   _snake_case							:	Optional[int]			=						self.encode(_lowercase	)
   if len(_lowercase	) > self.model_max_length:
     _snake_case							:	str			=						input_ids[-self.model_max_length :]
     logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."	)
   return input_ids








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_      			=       logging.get_logger(__name__)

A_      			=       {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "roformer"


 def __init__(       self:	List[str],   a_:	Tuple=50_000,   a_:	Optional[Any]=None,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	Optional[int]=12,   a_:	Optional[Any]=3_072,   a_:	List[str]="gelu",   a_:	List[str]=0.1,   a_:	Tuple=0.1,   a_:	Optional[int]=1_536,   a_:	Any=2,   a_:	Optional[int]=0.02,   a_:	Tuple=1E-12,   a_:	Dict=0,   a_:	str=False,   a_:	Dict=True,   **a_:	Dict,   ):




   '''simple docstring'''



   super().__init__(pad_token_id=a_,   **a_	)

   _snake_case							:	int			=						vocab_size
   _snake_case							:	int			=						hidden_size if embedding_size is None else embedding_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	Any			=						num_attention_heads
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[Any]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	List[Any]			=						initializer_range
   _snake_case							:	List[Any]			=						layer_norm_eps
   _snake_case							:	Optional[Any]			=						rotary_value
   _snake_case							:	List[str]			=						use_cache







class       lowercase(  __a				):







 '''simple docstring'''





 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   if self.task == "multiple-choice":
     _snake_case							:	str			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     _snake_case							:	List[str]			=						{0: """batch""", 1: """sequence"""}
   _snake_case							:	List[Any]			=						{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ]	)


