



'''simple docstring'''





import json
import os
from typing import Optional, Tuple

import regex as re

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


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_lowerCAmelCase      						=	{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

_lowerCAmelCase      						=	{
    '''ctrl''': 256,
}

_lowerCAmelCase      						=	{
    '''Pregnancy''': 16_8629,
    '''Christianity''': 7675,
    '''Explain''': 10_6423,
    '''Fitness''': 6_3440,
    '''Saving''': 6_3163,
    '''Ask''': 2_7171,
    '''Ass''': 9_5985,
    '''Joke''': 16_3509,
    '''Questions''': 4_5622,
    '''Thoughts''': 4_9605,
    '''Retail''': 5_2342,
    '''Feminism''': 16_4338,
    '''Writing''': 1_1992,
    '''Atheism''': 19_2263,
    '''Netflix''': 4_8616,
    '''Computing''': 3_9639,
    '''Opinion''': 4_3213,
    '''Alone''': 4_4967,
    '''Funny''': 5_8917,
    '''Gaming''': 4_0358,
    '''Human''': 4088,
    '''India''': 1331,
    '''Joker''': 7_7138,
    '''Diet''': 3_6206,
    '''Legal''': 1_1859,
    '''Norman''': 4939,
    '''Tip''': 7_2689,
    '''Weight''': 5_2343,
    '''Movies''': 4_6273,
    '''Running''': 2_3425,
    '''Science''': 2090,
    '''Horror''': 3_7793,
    '''Confession''': 6_0572,
    '''Finance''': 1_2250,
    '''Politics''': 1_6360,
    '''Scary''': 19_1985,
    '''Support''': 1_2654,
    '''Technologies''': 3_2516,
    '''Teenage''': 6_6160,
    '''Event''': 3_2769,
    '''Learned''': 6_7460,
    '''Notion''': 18_2770,
    '''Wikipedia''': 3_7583,
    '''Books''': 6665,
    '''Extract''': 7_6050,
    '''Confessions''': 10_2701,
    '''Conspiracy''': 7_5932,
    '''Links''': 6_3674,
    '''Narcissus''': 15_0425,
    '''Relationship''': 5_4766,
    '''Relationships''': 13_4796,
    '''Reviews''': 4_1671,
    '''News''': 4256,
    '''Translation''': 2_6820,
    '''multilingual''': 12_8406,
}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Optional[Any]							      =					set(UpperCamelCase			)
    return pairs






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      VOCAB_FILES_NAMES
  __lowercase							:      Dict				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Optional[Any]				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      Any				=      CONTROL_CODES




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase="<unk>"  ,**__UpperCAmelCase      )    ->		Optional[int]:
      super().__init__(unk_token=__UpperCAmelCase  ,**__UpperCAmelCase      )

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




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      return len(self.encoder      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      return dict(self.encoder  ,**self.added_tokens_encoder      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      if token in self.cache:
          return self.cache[token]
      lowerCAmelCase__ :  Optional[Any]							      =					tuple(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[str]							      =					tuple(list(word[:-1]      ) + [word[-1] + """</w>"""]      )
      lowerCAmelCase__ :  Any							      =					get_pairs(__UpperCAmelCase      )

      if not pairs:
          return token

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

              if word[i] == first and i < len(__UpperCAmelCase      ) - 1 and word[i + 1] == second:
                  new_word.append(first + second      )
                  i += 2
              else:
                  new_word.append(word[i]      )
                  i += 1
          lowerCAmelCase__ :  Tuple							      =					tuple(__UpperCAmelCase      )
          lowerCAmelCase__ :  Tuple							      =					new_word
          if len(__UpperCAmelCase      ) == 1:
              break
          else:
              lowerCAmelCase__ :  Dict							      =					get_pairs(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					"""@@ """.join(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					word[:-4]
      lowerCAmelCase__ :  Optional[Any]							      =					word
      return word




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Optional[int]							      =					[]

      lowerCAmelCase__ :  Any							      =					re.findall(R"""\S+\n?"""  ,__UpperCAmelCase      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      return self.encoder.get(__UpperCAmelCase  ,self.encoder.get(self.unk_token      )      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      return self.decoder.get(__UpperCAmelCase  ,self.unk_token      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Any							      =					""" """.join(__UpperCAmelCase      ).replace("""@@ """  ,""""""      ).strip()
      return out_string






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  List[Any]							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
      lowerCAmelCase__ :  Optional[int]							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]      )

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

      lowerCAmelCase__ :  int							      =					0
      with open(__UpperCAmelCase  ,"""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 __UpperCAmelCase      : 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!"""      )
                  lowerCAmelCase__ :  Dict							      =					token_index
              writer.write(""" """.join(__UpperCAmelCase      ) + """\n"""      )
              index += 1

      return vocab_file, merge_file

  # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
  #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
  #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
  #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
  #     return ''.join(tokens_generated_so_far)




from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)




