


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': 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=(
         list(range(ord('!'				)			,     ord('~'				) + 1				)				) + list(range(ord('¡'				)			,     ord('¬'				) + 1				)				) + list(range(ord('®'				)			,     ord('ÿ'				) + 1				)				)
     )
     __UpperCamelCase    							=bs[:]
     __UpperCamelCase    							=0
     for b in range(2**8				):
          if b not in bs:
               bs.append(SCREAMING_SNAKE_CASE__				)
               cs.append(2**8 + n				)
               n += 1
     __UpperCamelCase    							=[chr(SCREAMING_SNAKE_CASE__				) for n in cs]
     return dict(zip(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple				):
     __UpperCamelCase    							=set()
     __UpperCamelCase    							=word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          __UpperCamelCase    							=char
     return pairs






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[str]      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		List[Any]      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Dict      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Dict      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   A_					,   A_="replace"					,   A_="<s>"					,   A_="</s>"					,   A_="</s>"					,   A_="<s>"					,   A_="<unk>"					,   A_="<pad>"					,   A_="<mask>"					,   A_=False					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else bos_token
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else eos_token
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else sep_token
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else cls_token
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else unk_token
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else pad_token

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

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

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

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         __UpperCamelCase    							=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 _a					(			self    )  ->       Union[str, Any]:
         return len(self.encoder    )






    def _a					(			self    )  ->       Union[str, Any]:
         return dict(self.encoder					,   **self.added_tokens_encoder    )






    def _a					(			self					,   A_    )  ->       List[str]:
         if token in self.cache:
              return self.cache[token]
         __UpperCamelCase    							=tuple(A_    )
         __UpperCamelCase    							=get_pairs(A_    )

         if not pairs:
              return token

         while True:
              __UpperCamelCase    							=min(A_					,   key=lambda A_    : self.bpe_ranks.get(A_					,   float('inf'    )    )    )
              if bigram not in self.bpe_ranks:
                   break
              __UpperCamelCase							,      __UpperCamelCase    							=bigram
              __UpperCamelCase    							=[]
              __UpperCamelCase    							=0
              while i < len(A_    ):
                   try:
                        __UpperCamelCase    							=word.index(A_					,   A_    )
                   except ValueError:
                        new_word.extend(word[i:]    )
                        break
                   else:
                        new_word.extend(word[i:j]    )
                        __UpperCamelCase    							=j

                   if word[i] == first and i < len(A_    ) - 1 and word[i + 1] == second:
                        new_word.append(first + second    )
                        i += 2
                   else:
                        new_word.append(word[i]    )
                        i += 1
              __UpperCamelCase    							=tuple(A_    )
              __UpperCamelCase    							=new_word
              if len(A_    ) == 1:
                   break
              else:
                   __UpperCamelCase    							=get_pairs(A_    )
         __UpperCamelCase    							=' '.join(A_    )
         __UpperCamelCase    							=word
         return word






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							=[]
         for token in re.findall(self.pat					,   A_    ):
              __UpperCamelCase    							=''.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(A_    ).split(' '    )    )
         return bpe_tokens






    def _a					(			self					,   A_    )  ->       Dict:
         return self.encoder.get(A_					,   self.encoder.get(self.unk_token    )    )






    def _a					(			self					,   A_    )  ->       Optional[Any]:
         return self.decoder.get(A_    )






    def _a					(			self					,   A_    )  ->       int:
         __UpperCamelCase    							=''.join(A_    )
         __UpperCamelCase    							=bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'					,   errors=self.errors    )
         return text






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
              return
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

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

         __UpperCamelCase    							=0
         with open(A_					,   '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!'    )
                        __UpperCamelCase    							=token_index
                   writer.write(' '.join(A_    ) + '\n'    )
                   index += 1

         return vocab_file, merge_file






    def _a					(			self					,   A_					,   A_ = None					,   A_ = False    )  ->       List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_					,   token_ids_a=A_					,   already_has_special_tokens=A_    )

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






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:
         __UpperCamelCase    							=[self.sep_token_id]
         __UpperCamelCase    							=[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 _a					(			self					,   A_					,   A_=False					,   **A_    )  ->       Tuple:
         __UpperCamelCase    							=kwargs.pop('add_prefix_space'					,   self.add_prefix_space    )
         if (is_split_into_words or add_prefix_space) and (len(A_    ) > 0 and not text[0].isspace()):
              __UpperCamelCase    							=' ' + text
         return (text, kwargs)






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple:
         return token_ids_a + [self.eos_token_id]






    def _a					(			self					,   A_    )  ->       List[int]:
         __UpperCamelCase    							=[]
         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(A_    )

         __UpperCamelCase    							='  '.join(A_    )
         __UpperCamelCase    							=self.encode(A_    )
         if len(A_    ) > self.model_max_length:
              __UpperCamelCase    							=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









from typing import TYPE_CHECKING

from ...utils import _LazyModule


_A		       =						{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
   from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





