


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

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


lowerCAmelCase__      		= logging.get_logger(__name__)


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

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

lowerCAmelCase__      		= '</w>'
lowerCAmelCase__      		= '@@ '
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]						):
       _A      :       Optional[int]				    = set()
       _A      :       Optional[Any]				    = word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              _A      :       List[Any]				    = char
       return pairs


# Speech2Text2 has no max input length
lowerCAmelCase__      		= {'facebook/s2t-wav2vec2-large-en-de': 10_24}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase=False ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[Any]:
            super().__init__(
                unk_token=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						do_lower_case=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Dict				    = do_lower_case

            with open(__lowerCamelCase ,						encoding="utf-8") as vocab_handle:
                   _A      :       Optional[int]				    = json.load(__lowerCamelCase)
            _A      :       Optional[Any]				    = {v: k for k, v in self.encoder.items()}

            if merges_file is None:
                   logger.info(F"No merges files provided. {self.__class__.__name__} can only be used for decoding.")

                   _A      :       Optional[Any]				    = None
                   _A      :       Tuple				    = None
            else:
                   with open(__lowerCamelCase ,						encoding="utf-8") as merges_handle:
                          _A      :       Optional[int]				    = merges_handle.read().split("\n")[:-1]

                   _A      :       Union[str, Any]				    = [tuple(merge.split()[:2]) for merge in merges]
                   _A      :       Optional[int]				    = dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase))))
                   _A      :       List[Any]				    = {}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return len(self.decoder)


     def  _lowerCamelCase (						self)			->				Dict:
            return dict(self.encoder ,						**self.added_tokens_encoder)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            _A      :       Tuple				    = tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
            if token in self.cache:
                   return self.cache[token]
            _A      :       int				    = get_pairs(__lowerCamelCase)

            if not pairs:
                   return token

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

                          if word[i] == first and i < len(__lowerCamelCase) - 1 and word[i + 1] == second:
                                 new_word.append(first + second)
                                 i += 2
                          else:
                                 new_word.append(word[i])
                                 i += 1
                   _A      :       List[str]				    = tuple(__lowerCamelCase)
                   _A      :       List[str]				    = new_word
                   if len(__lowerCamelCase) == 1:
                          break
                   else:
                          _A      :       List[Any]				    = get_pairs(__lowerCamelCase)
            _A      :       Tuple				    = " ".join(__lowerCamelCase)
            if word == "\n  " + BPE_TOKEN_MERGES:
                   _A      :       List[str]				    = "\n" + BPE_TOKEN_MERGES

            if word.endswith(__lowerCamelCase):
                   _A      :       int				    = word.replace(__lowerCamelCase ,						"")

            _A      :       int				    = word.replace(" " ,						__lowerCamelCase)
            _A      :       Union[str, Any]				    = word
            return word


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:

            if self.bpe_ranks is None:
                   raise ValueError(
                       "This tokenizer was instantiated without a `merges.txt` file, so"
                       " that it can only be used for decoding, not for encoding."
                       "Make sure to provide `merges.txt` file at instantiation to enable "
                       "encoding.")

            if self.do_lower_case:
                   _A      :       List[Any]				    = text.lower()

            _A      :       Optional[int]				    = text.split()

            _A      :       List[str]				    = []
            for token in text:
                   if token:
                          split_tokens.extend(list(self.bpe(__lowerCamelCase).split(" ")))

            return split_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            return self.encoder.get(__lowerCamelCase ,						self.encoder.get(self.unk_token))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       List[str]				    = self.decoder.get(__lowerCamelCase ,						self.unk_token)
            return result


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       str				    = " ".join(__lowerCamelCase)

            # make sure @@ tokens are concatenated
            _A      :       int				    = "".join(string.split(__lowerCamelCase))

            return string


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

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

            _A      :       Union[str, Any]				    = 0
            if self.bpe_ranks is None:
                   return (vocab_file,)

            with open(__lowerCamelCase ,						"w" ,						encoding="utf-8") as writer:
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,						key=lambda __lowerCamelCase: kv[1]):
                          if index != token_index:
                                 logger.warning(
                                     F"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
                                     " Please check that the tokenizer is not corrupted!")
                                 _A      :       Optional[int]				    = token_index
                          writer.write(" ".join(__lowerCamelCase) + "\n")
                          index += 1

            return (vocab_file, merges_file)

'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_A			:      str					      =				logging.get_logger(__name__)

_A			:      str					      =				[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  __lowerCAmelCase											=						k.replace(snake_case_						,		snake_case_      )

 if k.startswith("""encoder"""      ):
  __lowerCAmelCase											=						k.replace(""".attn"""						,		""".self_attn"""      )
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""final_layer_norm"""      )
 elif k.startswith("""decoder"""      ):
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""encoder_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm3"""						,		"""final_layer_norm"""      )
 return k







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __lowerCAmelCase											=						sd.pop(snake_case_      )
  __lowerCAmelCase											=						k.replace("""layernorm_embedding"""						,		"""layer_norm"""      )
  assert new_k not in sd
  __lowerCAmelCase											=						v


_A			:      int					      =				['''START''']







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Any						,		snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						model["""model"""]
 __lowerCAmelCase											=						BlenderbotConfig.from_json_file(snake_case_      )
 __lowerCAmelCase											=						BlenderbotForConditionalGeneration(snake_case_      )
 __lowerCAmelCase											=						m.model.state_dict().keys()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						{}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  __lowerCAmelCase											=						rename_state_dict_key(snake_case_      )
  if new_k not in valid_keys:
   failures.append([k, new_k]      )
  else:
   __lowerCAmelCase											=						v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(snake_case_      )
 m.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 m.half()
 m.save_pretrained(snake_case_      )


if __name__ == "__main__":
 _A			:      Optional[Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
 parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
 parser.add_argument(
     '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
 )
 _A			:      Tuple					      =				parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






