



'''simple docstring'''





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    if openai_config_file == "":
        lowerCAmelCase__			:  str											= OpenAIGPTConfig()
    else:
        lowerCAmelCase__			:  Union[str, Any]											= OpenAIGPTConfig.from_json_file(UpperCamelCase      )
    lowerCAmelCase__			:  Dict											= OpenAIGPTModel(UpperCamelCase      )

    # Load weights from numpy
    load_tf_weights_in_openai_gpt(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

    # Save pytorch-model
    lowerCAmelCase__			:  Dict											= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
    lowerCAmelCase__			:  Optional[Any]											= pytorch_dump_folder_path + """/""" + CONFIG_NAME
    print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""      )
    torch.save(model.state_dict()							,   UpperCamelCase      )
    print(f"""Save configuration file to {pytorch_config_dump_path}"""      )
    with open(UpperCamelCase							,   """w"""							,   encoding="""utf-8"""      ) as f:
        f.write(config.to_json_string()      )


if __name__ == "__main__":
 _lowerCAmelCase										=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--openai_checkpoint_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint path.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--openai_config_file''',
     default='''''',
     type=str,
     help=(
         '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 _lowerCAmelCase										=     parser.parse_args()
 convert_openai_checkpoint_to_pytorch(
     args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
 )







'''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__			:  Dict											= set()
    lowerCAmelCase__			:  List[str]											= word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)      )
        lowerCAmelCase__			:  str											= char

    lowerCAmelCase__			:  int											= set(UpperCamelCase      )
    return pairs






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




  __lowercase							:				Optional[int]           =				VOCAB_FILES_NAMES
  __lowercase							:				Union[str, Any]           =				PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:				List[Any]           =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:				str           =				CONTROL_CODES




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

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




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




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




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

      if not pairs:
          return token

      while True:
          lowerCAmelCase__			:  Tuple											= min(__UpperCAmelCase  ,key=lambda __UpperCAmelCase				: self.bpe_ranks.get(__UpperCAmelCase  ,float("""inf"""				)				)				)
          if bigram not in self.bpe_ranks:
              break
          lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[Any]											= bigram
          lowerCAmelCase__			:  Dict											= []
          lowerCAmelCase__			:  int											= 0
          while i < len(__UpperCAmelCase				):
              try:
                  lowerCAmelCase__			:  Any											= word.index(__UpperCAmelCase  ,__UpperCAmelCase				)
              except ValueError:
                  new_word.extend(word[i:]				)
                  break
              else:
                  new_word.extend(word[i:j]				)
                  lowerCAmelCase__			:  Any											= 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__			:  Any											= tuple(__UpperCAmelCase				)
          lowerCAmelCase__			:  Tuple											= new_word
          if len(__UpperCAmelCase				) == 1:
              break
          else:
              lowerCAmelCase__			:  List[str]											= get_pairs(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= """@@ """.join(__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= word[:-4]
      lowerCAmelCase__			:  Optional[Any]											= word
      return word




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[Any]											= []

      lowerCAmelCase__			:  Optional[int]											= 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				)   ->		Tuple:
      return self.encoder.get(__UpperCAmelCase  ,self.encoder.get(self.unk_token				)				)




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




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Dict:
      lowerCAmelCase__			:  Optional[int]											= """ """.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__			:  Tuple											= os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
      lowerCAmelCase__			:  str											= 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__			:  Optional[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__			:  List[str]											= 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)


