


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

SCREAMING_SNAKE_CASE       :		Optional[Any]         =				logging.get_logger(__name__)


SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE       :		Dict         =				{
    "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"
    },
}

SCREAMING_SNAKE_CASE       :		Tuple         =				{"facebook/blenderbot-3B": 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     UpperCamelCase    (      )					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Any     =       (
         list(range(ord('''!'''		)       ,		ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)       ,		ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)       ,		ord('''ÿ'''		) + 1		)		)
     )
     lowercase_       :List[Any]     =       bs[:]
     lowercase_       :Dict     =       0
     for b in range(2**8		):
          if b not in bs:
               bs.append(_a		)
               cs.append(2**8 + n		)
               n += 1
     lowercase_       :Union[str, Any]     =       [chr(_a		) for n in cs]
     return dict(zip(_a       ,		_a		)		)




def     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





     lowercase_       :Dict     =       set()
     lowercase_       :Optional[int]     =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)		)
          lowercase_       :Union[str, Any]     =       char
     return pairs






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[str]									=VOCAB_FILES_NAMES
   lowercase					:	List[str]									=PRETRAINED_VOCAB_FILES_MAP
   lowercase					:	int									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase					:	Tuple									=["""input_ids""", """attention_mask"""]
   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_="replace"   ,							UpperCamelCase_="<s>"   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="<s>"   ,							UpperCamelCase_="<unk>"   ,							UpperCamelCase_="<pad>"   ,							UpperCamelCase_="<mask>"   ,							UpperCamelCase_=False   ,							**UpperCamelCase_   ,							):
        lowercase_       :Tuple     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else bos_token
        lowercase_       :str     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else eos_token
        lowercase_       :Optional[Any]     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else sep_token
        lowercase_       :str     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else cls_token
        lowercase_       :Tuple     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else unk_token
        lowercase_       :Union[str, Any]     =       AddedToken(UpperCamelCase_   ,							lstrip=UpperCamelCase_   ,							rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_   ,							UpperCamelCase_		) else pad_token

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

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

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        lowercase_       :Optional[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 		UpperCamelCase  (     self		):
        return len(self.encoder		)
   def 		UpperCamelCase  (     self		):
        return dict(self.encoder   ,							**self.added_tokens_encoder		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if token in self.cache:
             return self.cache[token]
        lowercase_       :int     =       tuple(UpperCamelCase_		)
        lowercase_       :int     =       get_pairs(UpperCamelCase_		)

        if not pairs:
             return token

        while True:
             lowercase_       :str     =       min(UpperCamelCase_   ,							key=lambda UpperCamelCase_		: self.bpe_ranks.get(UpperCamelCase_   ,							float('''inf'''		)		)		)
             if bigram not in self.bpe_ranks:
                  break
             lowercase_    ,      lowercase_       :List[Any]     =       bigram
             lowercase_       :Dict     =       []
             lowercase_       :int     =       0
             while i < len(UpperCamelCase_		):
                  try:
                       lowercase_       :Dict     =       word.index(UpperCamelCase_   ,							UpperCamelCase_		)
                  except ValueError:
                       new_word.extend(word[i:]		)
                       break
                  else:
                       new_word.extend(word[i:j]		)
                       lowercase_       :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
             lowercase_       :Tuple     =       tuple(UpperCamelCase_		)
             lowercase_       :int     =       new_word
             if len(UpperCamelCase_		) == 1:
                  break
             else:
                  lowercase_       :str     =       get_pairs(UpperCamelCase_		)
        lowercase_       :List[str]     =       ''' '''.join(UpperCamelCase_		)
        lowercase_       :str     =       word
        return word
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[int]     =       []
        for token in re.findall(self.pat   ,							UpperCamelCase_		):
             lowercase_       :List[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(UpperCamelCase_		).split(''' '''		)		)
        return bpe_tokens
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        return self.encoder.get(UpperCamelCase_   ,							self.encoder.get(self.unk_token		)		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        return self.decoder.get(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :str     =       ''''''.join(UpperCamelCase_		)
        lowercase_       :int     =       bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''   ,							errors=self.errors		)
        return text
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        if not os.path.isdir(UpperCamelCase_		):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
             return
        lowercase_       :str     =       os.path.join(
            UpperCamelCase_   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
        lowercase_       :Any     =       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'''		)

        lowercase_       :Tuple     =       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!'''		)
                       lowercase_       :Dict     =       token_index
                  writer.write(''' '''.join(UpperCamelCase_		) + '''\n'''		)
                  index += 1

        return vocab_file, merge_file
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = False		):
        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=UpperCamelCase_   ,							token_ids_a=UpperCamelCase_   ,							already_has_special_tokens=UpperCamelCase_		)

        if token_ids_a is None:
             return [1] + ([0] * len(UpperCamelCase_		)) + [1]
        return [1] + ([0] * len(UpperCamelCase_		)) + [1, 1] + ([0] * len(UpperCamelCase_		)) + [1]
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        lowercase_       :List[Any]     =       [self.sep_token_id]
        lowercase_       :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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=False   ,							**UpperCamelCase_		):
        lowercase_       :Tuple     =       kwargs.pop('''add_prefix_space'''   ,							self.add_prefix_space		)
        if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_		) > 0 and not text[0].isspace()):
             lowercase_       :Any     =       ''' ''' + text
        return (text, kwargs)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        return token_ids_a + [self.eos_token_id]




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Dict     =       []
        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(UpperCamelCase_		)

        lowercase_       :Dict     =       '''  '''.join(UpperCamelCase_		)
        lowercase_       :List[Any]     =       self.encode(UpperCamelCase_		)
        if len(UpperCamelCase_		) > self.model_max_length:
             lowercase_       :int     =       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





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     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     if openai_config_file == "":
          lowercase_       :str     =       OpenAIGPTConfig()
     else:
          lowercase_       :int     =       OpenAIGPTConfig.from_json_file(_a		)
     lowercase_       :int     =       OpenAIGPTModel(_a		)

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		List[str]         =				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."
       ),
   )
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				parser.parse_args()
   convert_openai_checkpoint_to_pytorch(
       args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
   )

