


'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_camembert import CamembertTokenizer
else:
      __UpperCAmelCase      :Any			    =       None


__UpperCAmelCase      :List[str]			    =       logging.get_logger(__name__)

__UpperCAmelCase      :List[str]			    =       {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase      :List[Any]			    =       {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase      :str			    =       {
    "camembert-base": 5_1_2,
}

__UpperCAmelCase      :Optional[int]			    =       "▁"



class      a    (		snake_case__	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[str]							       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Tuple							       =       ["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       CamembertTokenizer

     def __init__(    self					:		Optional[Any]							,			snake_case					:		Optional[Any]=None							,			snake_case					:		Any=None							,			snake_case					:		Any="<s>"							,			snake_case					:		str="</s>"							,			snake_case					:		str="</s>"							,			snake_case					:		str="<s>"							,			snake_case					:		Tuple="<unk>"							,			snake_case					:		Optional[Any]="<pad>"							,			snake_case					:		Dict="<mask>"							,			snake_case					:		Tuple=["<s>NOTUSED", "</s>NOTUSED"]							,			**snake_case					:		int							,			)						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      AddedToken(_A							,			lstrip=_A							,			rstrip=_A      ) if isinstance(_A							,			_A      ) else mask_token

          super().__init__(
              _A							,			tokenizer_file=_A							,			bos_token=_A							,			eos_token=_A							,			sep_token=_A							,			cls_token=_A							,			unk_token=_A							,			pad_token=_A							,			mask_token=_A							,			additional_special_tokens=_A							,			**_A							,			)

          __UpperCAmelCase     :			Dict  							=      vocab_file
          __UpperCAmelCase     :			Optional[Any]  							=      False if not self.vocab_file else True

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		str							,			snake_case					:		str = None      )						->							Optional[Any]:



          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCAmelCase     :			Optional[int]  							=      [self.cls_token_id]
          __UpperCAmelCase     :			List[Any]  							=      [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		int = None      )						->							str:
          __UpperCAmelCase     :			List[Any]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			Dict  							=      [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 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str] = None      )						->							str:



          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                   '''tokenizer.'''      )

          if not os.path.isdir(_A      ):
               logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
               return
          __UpperCAmelCase     :			int  							=      os.path.join(
              _A							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(_A      ):
               copyfile(self.vocab_file							,			_A      )

          return (out_vocab_file,)



'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase      :Any			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Dict			    =       [
    ["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      _a     (						_lowercase :     Tuple  ):





     '''simple docstring'''






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

     for parlai_name, hf_name in PATTERNS:
          __UpperCAmelCase     :			Any  							=      k.replace(_lowercase      , _lowercase  )

     if k.startswith('''encoder'''  ):
          __UpperCAmelCase     :			str  							=      k.replace('''.attn'''      , '''.self_attn'''  )
          __UpperCAmelCase     :			Any  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[str]  							=      k.replace('''norm2'''      , '''final_layer_norm'''  )
     elif k.startswith('''decoder'''  ):
          __UpperCAmelCase     :			int  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      k.replace('''norm2'''      , '''encoder_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[Any]  							=      k.replace('''norm3'''      , '''final_layer_norm'''  )
     return k




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      [
         '''model.encoder.layernorm_embedding.weight''',
         '''model.encoder.layernorm_embedding.bias''',
         '''model.decoder.layernorm_embedding.weight''',
         '''model.decoder.layernorm_embedding.bias''',
     ]
     for k in keys:
          __UpperCAmelCase     :			Any  							=      sd.pop(_lowercase  )
          __UpperCAmelCase     :			Optional[int]  							=      k.replace('''layernorm_embedding'''      , '''layer_norm'''  )
          assert new_k not in sd
          __UpperCAmelCase     :			List[str]  							=      v


__UpperCAmelCase      :str			    =       ["START"]




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      torch.load(_lowercase      , map_location='''cpu'''  )
     __UpperCAmelCase     :			List[str]  							=      model['''model''']
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotConfig.from_json_file(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotForConditionalGeneration(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      m.model.state_dict().keys()
     __UpperCAmelCase     :			int  							=      []
     __UpperCAmelCase     :			List[str]  							=      {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[int]			    =       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"
      )
      __UpperCAmelCase      :Tuple			    =       parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)