



'''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


__a          =		logging.get_logger(__name__)


__a          =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__a          =		{
    "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"
        )
    },
}

__a          =		{"facebook/blenderbot_small-90M": 512}





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Any    						=      set()
      snake_case__  :    Tuple    						=      word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)		)
            snake_case__  :    str    						=      char

      snake_case__  :    Dict    						=      set(_lowerCAmelCase		)
      return pairs







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







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




    def __init__(    self					:     str ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[int] ,     snake_case_					:     int="__start__" ,     snake_case_					:     Optional[int]="__end__" ,     snake_case_					:     str="__unk__" ,     snake_case_					:     List[Any]="__null__" ,     **snake_case_					:     int ,     ):
          super().__init__(unk_token=snake_case_ ,     bos_token=snake_case_ ,     eos_token=snake_case_ ,     pad_token=snake_case_ ,     **snake_case_						)

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




    @property
    def lowerCamelCase       (    self					:     str						):
          return len(self.encoder						)




    def lowerCamelCase       (    self					:     List[Any]						):
          return dict(self.encoder ,     **self.added_tokens_encoder						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     str						):
          if token in self.cache:
                return self.cache[token]
          snake_case__  :    Tuple    						=      re.sub("""([.,!?()])""" ,     r""" \1""" ,     snake_case_						)
          snake_case__  :    Optional[int]    						=      re.sub("""(')""" ,     r""" \1 """ ,     snake_case_						)
          snake_case__  :    int    						=      re.sub(r"""\s{2,}""" ,     """ """ ,     snake_case_						)
          if "\n" in token:
                snake_case__  :    int    						=      token.replace("""\n""" ,     """ __newln__"""						)

          snake_case__  :    Dict    						=      token.split(""" """						)
          snake_case__  :    Union[str, Any]    						=      []
          for token in tokens:
                if not len(snake_case_						):
                      continue

                snake_case__  :    List[str]    						=      token.lower()
                snake_case__  :    Dict    						=      tuple(snake_case_						)
                snake_case__  :    Optional[Any]    						=      tuple(list(word[:-1]						) + [word[-1] + """</w>"""]						)
                snake_case__  :    int    						=      get_pairs(snake_case_						)

                if not pairs:
                      words.append(snake_case_						)
                      continue

                while True:
                      snake_case__  :    Tuple    						=      min(snake_case_ ,     key=lambda snake_case_						: self.bpe_ranks.get(snake_case_ ,     float("""inf"""						)						)						)
                      if bigram not in self.bpe_ranks:
                            break
                      snake_case__						,				snake_case__  :    List[Any]    						=      bigram
                      snake_case__  :    str    						=      []
                      snake_case__  :    Tuple    						=      0

                      while i < len(snake_case_						):
                            try:
                                  snake_case__  :    Union[str, Any]    						=      word.index(snake_case_ ,     snake_case_						)
                                  new_word.extend(word[i:j]						)
                                  snake_case__  :    List[Any]    						=      j
                            except ValueError:
                                  new_word.extend(word[i:]						)
                                  break

                            if word[i] == first and i < len(snake_case_						) - 1 and word[i + 1] == second:
                                  new_word.append(first + second						)
                                  i += 2
                            else:
                                  new_word.append(word[i]						)
                                  i += 1
                      snake_case__  :    int    						=      tuple(snake_case_						)
                      snake_case__  :    Optional[Any]    						=      new_word
                      if len(snake_case_						) == 1:
                            break
                      else:
                            snake_case__  :    str    						=      get_pairs(snake_case_						)
                snake_case__  :    Optional[int]    						=      """@@ """.join(snake_case_						)
                snake_case__  :    Any    						=      word[:-4]

                snake_case__  :    str    						=      word
                words.append(snake_case_						)
          return " ".join(snake_case_						)




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     str						):
          snake_case__  :    Tuple    						=      []

          snake_case__  :    str    						=      re.findall(r"""\S+\n?""" ,     snake_case_						)

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




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     str						):
          snake_case__  :    Dict    						=      token.lower()
          return self.encoder.get(snake_case_ ,     self.encoder.get(self.unk_token						)						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     int						):
          return self.decoder.get(snake_case_ ,     self.unk_token						)




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[str]						):
          snake_case__  :    List[str]    						=      """ """.join(snake_case_						).replace("""@@ """ ,     """"""						).strip()
          return out_string




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     str ,     snake_case_					:     Optional[str] = None						):
          if not os.path.isdir(snake_case_						):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
                return
          snake_case__  :    Union[str, Any]    						=      os.path.join(
              snake_case_ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
          snake_case__  :    List[Any]    						=      os.path.join(
              snake_case_ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

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

          snake_case__  :    Optional[Any]    						=      0
          with open(snake_case_ ,     """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 snake_case_						: 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!"""						)
                            snake_case__  :    int    						=      token_index
                      writer.write(""" """.join(snake_case_						) + """\n"""						)
                      index += 1

          return vocab_file, merge_file




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



