

"""simple docstring"""




import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase =				logging.get_logger(__name__)


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

__UpperCAmelCase =				{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

__UpperCAmelCase =				{'facebook/blenderbot_small-90M': 512}
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					set()
 UpperCAmelCase__					:  Optional[int]					   =					word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)      )
  UpperCAmelCase__					:  Any					   =					char

 UpperCAmelCase__					:  Optional[int]					   =					set(__UpperCamelCase      )
 return pairs







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       VOCAB_FILES_NAMES
  snake_case_				       =       PRETRAINED_VOCAB_FILES_MAP
  snake_case_				       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_				       =       ["""input_ids""", """attention_mask"""]

  def __init__(  self		:			Optional[Any]					,A		:			Any					,A		:			Dict					,A		:			List[str]="__start__"					,A		:			str="__end__"					,A		:			List[str]="__unk__"					,A		:			Any="__null__"					,**A		:			Optional[Any]					,):






   '''simple docstring'''
   super().__init__(unk_token=A					,bos_token=A					,eos_token=A					,pad_token=A					,**A    )

   with open(A					,encoding="""utf-8"""    ) as vocab_handle:
    UpperCAmelCase__					:  List[str]					   =					json.load(A    )
   UpperCAmelCase__					:  str					   =					{v: k for k, v in self.encoder.items()}
   with open(A					,encoding="""utf-8"""    ) as merges_handle:
    UpperCAmelCase__					:  Optional[Any]					   =					merges_handle.read().split("""\n"""    )[1:-1]
   UpperCAmelCase__					:  List[str]					   =					[tuple(merge.split()    ) for merge in merges]
   UpperCAmelCase__					:  Union[str, Any]					   =					dict(zip(A					,range(len(A    )    )    )    )
   UpperCAmelCase__					:  str					   =					{}

  @property
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   return len(self.encoder    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   return dict(self.encoder					,**self.added_tokens_encoder    )

  def    __lowercase       (  self		:			int					,A		:			str    ):






   '''simple docstring'''
   if token in self.cache:
    return self.cache[token]
   UpperCAmelCase__					:  Optional[int]					   =					re.sub("""([.,!?()])"""					,R""" \1"""					,A    )
   UpperCAmelCase__					:  List[str]					   =					re.sub("""(')"""					,R""" \1 """					,A    )
   UpperCAmelCase__					:  List[str]					   =					re.sub(R"""\s{2,}"""					,""" """					,A    )
   if "\n" in token:
    UpperCAmelCase__					:  int					   =					token.replace("""\n"""					,""" __newln__"""    )

   UpperCAmelCase__					:  List[Any]					   =					token.split(""" """    )
   UpperCAmelCase__					:  Union[str, Any]					   =					[]
   for token in tokens:
    if not len(A    ):
     continue

    UpperCAmelCase__					:  Any					   =					token.lower()
    UpperCAmelCase__					:  int					   =					tuple(A    )
    UpperCAmelCase__					:  Optional[int]					   =					tuple(list(word[:-1]    ) + [word[-1] + """</w>"""]    )
    UpperCAmelCase__					:  Optional[Any]					   =					get_pairs(A    )

    if not pairs:
     words.append(A    )
     continue

    while True:
     UpperCAmelCase__					:  Any					   =					min(A					,key=lambda A    : self.bpe_ranks.get(A					,float("""inf"""    )    )    )
     if bigram not in self.bpe_ranks:
      break
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					bigram
     UpperCAmelCase__					:  Dict					   =					[]
     UpperCAmelCase__					:  Dict					   =					0

     while i < len(A    ):
      try:
       UpperCAmelCase__					:  int					   =					word.index(A					,A    )
       new_word.extend(word[i:j]    )
       UpperCAmelCase__					:  List[str]					   =					j
      except ValueError:
       new_word.extend(word[i:]    )
       break

      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__					:  int					   =					tuple(A    )
     UpperCAmelCase__					:  Tuple					   =					new_word
     if len(A    ) == 1:
      break
     else:
      UpperCAmelCase__					:  Optional[int]					   =					get_pairs(A    )
    UpperCAmelCase__					:  int					   =					"""@@ """.join(A    )
    UpperCAmelCase__					:  str					   =					word[:-4]

    UpperCAmelCase__					:  str					   =					word
    words.append(A    )
   return " ".join(A    )

  def    __lowercase       (  self		:			List[Any]					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					[]

   UpperCAmelCase__					:  List[Any]					   =					re.findall(R"""\S+\n?"""					,A    )

   for token in words:
    split_tokens.extend(list(self.bpe(A    ).split(""" """    )    )    )
   return split_tokens

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					token.lower()
   return self.encoder.get(A					,self.encoder.get(self.unk_token    )    )

  def    __lowercase       (  self		:			Dict					,A		:			int    ):






   '''simple docstring'''
   return self.decoder.get(A					,self.unk_token    )

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






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					""" """.join(A    ).replace("""@@ """					,""""""    ).strip()
   return out_string




  def    __lowercase       (  self		:			Any					,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
   UpperCAmelCase__					:  Dict					   =					os.path.join(
       A					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
   UpperCAmelCase__					:  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"""    )

   UpperCAmelCase__					:  Optional[int]					   =					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__					:  Dict					   =					token_index
     writer.write(""" """.join(A    ) + """\n"""    )
     index += 1

   return vocab_file, merge_file








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				['RobertaTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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





