


'''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

__magic_name__			=      logging.get_logger(__name__)


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

__magic_name__			=      {
    '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'
    },
}

__magic_name__			=      {'facebook/blenderbot-3B': 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCamelCase					(      ):
      A_						:  int          =				(
          list(range(ord("""!""")							,						ord("""~""") + 1)) + list(range(ord("""¡""")							,						ord("""¬""") + 1)) + list(range(ord("""®""")							,						ord("""ÿ""") + 1))
      )
      A_						:  Dict          =				bs[:]
      A_						:  Tuple          =				0
      for b in range(2**8):
            if b not in bs:
                  bs.append(lowerCamelCase)
                  cs.append(2**8 + n)
                  n += 1
      A_						:  Optional[Any]          =				[chr(lowerCamelCase) for n in cs]
      return dict(zip(lowerCamelCase							,						lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()
      A_						:  Union[str, Any]          =				word[0]
      for char in word[1:]:
            pairs.add((prev_char, char))
            A_						:  Tuple          =				char
      return pairs






class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







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






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

        '''simple docstring'''




        A_						:  Any          =				AddedToken(_a	,lstrip=_a	,rstrip=_a ) if isinstance(_a	,_a ) else bos_token
        A_						:  Dict          =				AddedToken(_a	,lstrip=_a	,rstrip=_a ) if isinstance(_a	,_a ) else eos_token
        A_						:  List[Any]          =				AddedToken(_a	,lstrip=_a	,rstrip=_a ) if isinstance(_a	,_a ) else sep_token
        A_						:  Optional[int]          =				AddedToken(_a	,lstrip=_a	,rstrip=_a ) if isinstance(_a	,_a ) else cls_token
        A_						:  List[Any]          =				AddedToken(_a	,lstrip=_a	,rstrip=_a ) if isinstance(_a	,_a ) else unk_token
        A_						:  List[str]          =				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
        A_						:  Dict          =				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:
              A_						:  Any          =				json.load(_a )
        A_						:  str          =				{v: k for k, v in self.encoder.items()}
        A_						:  Union[str, Any]          =				errors  # how to handle errors in decoding
        A_						:  Any          =				bytes_to_unicode()
        A_						:  int          =				{v: k for k, v in self.byte_encoder.items()}
        with open(_a	,encoding="""utf-8""" ) as merges_handle:
              A_						:  int          =				merges_handle.read().split("""\n""" )[1:-1]
        A_						:  Optional[int]          =				[tuple(merge.split() ) for merge in bpe_merges]
        A_						:  Union[str, Any]          =				dict(zip(_a	,range(len(_a ) ) ) )
        A_						:  Any          =				{}
        A_						:  int          =				add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        A_						:  int          =				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 :       Tuple ):

        '''simple docstring'''




        return len(self.encoder )






  def        _a							(							self :       Dict ):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[Any]	,_a :       Any ):

        '''simple docstring'''




        if token in self.cache:
              return self.cache[token]
        A_						:  Optional[int]          =				tuple(_a )
        A_						:  int          =				get_pairs(_a )

        if not pairs:
              return token

        while True:
              A_						:  List[str]          =				min(_a	,key=lambda _a : self.bpe_ranks.get(_a	,float("""inf""" ) ) )
              if bigram not in self.bpe_ranks:
                    break
              A_   ,					A_						:  Dict          =				bigram
              A_						:  Optional[Any]          =				[]
              A_						:  Dict          =				0
              while i < len(_a ):
                    try:
                          A_						:  int          =				word.index(_a	,_a )
                    except ValueError:
                          new_word.extend(word[i:] )
                          break
                    else:
                          new_word.extend(word[i:j] )
                          A_						:  Any          =				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
              A_						:  Union[str, Any]          =				tuple(_a )
              A_						:  Optional[Any]          =				new_word
              if len(_a ) == 1:
                    break
              else:
                    A_						:  str          =				get_pairs(_a )
        A_						:  List[Any]          =				""" """.join(_a )
        A_						:  str          =				word
        return word






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

        '''simple docstring'''




        A_						:  Union[str, Any]          =				[]
        for token in re.findall(self.pat	,_a ):
              A_						:  str          =				"""""".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 :       str	,_a :       List[str] ):

        '''simple docstring'''




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






  def        _a							(							self :       Any	,_a :       List[Any] ):

        '''simple docstring'''




        return self.decoder.get(_a )






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

        '''simple docstring'''




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






  def        _a							(							self :       Tuple	,_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
        A_						:  Union[str, Any]          =				os.path.join(
            _a	,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
        A_						:  Dict          =				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""" )

        A_						:  List[Any]          =				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!""" )
                          A_						:  Tuple          =				token_index
                    writer.write(""" """.join(_a ) + """\n""" )
                    index += 1

        return vocab_file, merge_file






  def        _a							(							self :       int	,_a :       List[int]	,_a :       Optional[List[int]] = None	,_a :       bool = False ):

        '''simple docstring'''




        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 :       int	,_a :       List[int]	,_a :       Optional[List[int]] = None ):

        '''simple docstring'''




        A_						:  Optional[int]          =				[self.sep_token_id]
        A_						:  List[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        _a							(							self :       Optional[int]	,_a :       Dict	,_a :       Optional[Any]=False	,**_a :       Any ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				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()):
              A_						:  List[str]          =				""" """ + text
        return (text, kwargs)






  def        _a							(							self :       Optional[Any]	,_a :       List[int]	,_a :       Optional[List[int]] = None ):

        '''simple docstring'''




        return token_ids_a + [self.eos_token_id]






  def        _a							(							self :       List[str]	,_a :       "Conversation" ):

        '''simple docstring'''




        A_						:  Tuple          =				[]
        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 )

        A_						:  List[Any]          =				"""  """.join(_a )
        A_						:  List[str]          =				self.encode(_a )
        if len(_a ) > self.model_max_length:
              A_						:  List[Any]          =				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
