




'''simple docstring'''





import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCamelCase_		:     List[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     List[Any]          =		{'''vocab_file''': '''spiece.model'''}

UpperCamelCase_		:     Tuple          =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCamelCase_		:     int          =		{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

UpperCamelCase_		:     str          =		'''▁'''

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Any       							=		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_				:       int       							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		["""input_ids""", """attention_mask"""]






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="<unk>"   ,_SCREAMING_SNAKE_CASE="<pad>"   ,_SCREAMING_SNAKE_CASE=100   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     None:
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
               _snake_case				= [f"""<extra_id_{i}>""" for i in range(_SCREAMING_SNAKE_CASE       )]
          elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra_id special tokens
               _snake_case				= len(set(filter(lambda _SCREAMING_SNAKE_CASE       : bool("extra_id" in str(_SCREAMING_SNAKE_CASE       )       )   ,_SCREAMING_SNAKE_CASE       )       )       )
               if extra_tokens != extra_ids:
                    raise ValueError(
                        f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                        " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                        " tokens"       )
          if legacy:
               logger.warning_once(
                   f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
                   " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"       )

          _snake_case				= legacy
          _snake_case				= {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=_SCREAMING_SNAKE_CASE   ,unk_token=_SCREAMING_SNAKE_CASE   ,pad_token=_SCREAMING_SNAKE_CASE   ,extra_ids=_SCREAMING_SNAKE_CASE   ,additional_special_tokens=_SCREAMING_SNAKE_CASE   ,sp_model_kwargs=self.sp_model_kwargs   ,legacy=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= vocab_file
          _snake_case				= extra_ids

          _snake_case				= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
          self.sp_model.Load(_SCREAMING_SNAKE_CASE       )






     @staticmethod
     def 							_lowercase      (					_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     List[Any]:
          if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
               _snake_case				= TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
               if init_max_model_length is not None and init_max_model_length != max_model_length:
                    return init_max_model_length
               elif init_max_model_length is None:
                    warnings.warn(
                        "This tokenizer was incorrectly instantiated with a model max length of"
                        f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                        " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                        " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                        f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                        f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                        f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                        " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                        " instantiate this tokenizer with `model_max_length` set to your preferred value."   ,_SCREAMING_SNAKE_CASE   ,)

          return max_model_length






     @property
     def 							_lowercase      (					self       )				->     Optional[int]:
          return self.sp_model.get_piece_size() + self._extra_ids






     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				= {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE       ): i for i in range(self.vocab_size       )}
          vocab.update(self.added_tokens_encoder       )
          return vocab






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = False       )				->     List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_SCREAMING_SNAKE_CASE   ,token_ids_a=_SCREAMING_SNAKE_CASE   ,already_has_special_tokens=_SCREAMING_SNAKE_CASE       )

          # normal case: some special tokens
          if token_ids_a is None:
               return ([0] * len(_SCREAMING_SNAKE_CASE       )) + [1]
          return ([0] * len(_SCREAMING_SNAKE_CASE       )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE       )) + [1]






     def 							_lowercase      (					self       )				->     Optional[Any]:
          return list(
              set(filter(lambda _SCREAMING_SNAKE_CASE       : bool(re.search(r"<extra_id_\d+>"   ,_SCREAMING_SNAKE_CASE       )       ) is not None   ,self.additional_special_tokens       )       )       )






     def 							_lowercase      (					self       )				->     int:
          return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE       ) for token in self.get_sentinel_tokens()]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     List[int]:
          if len(_SCREAMING_SNAKE_CASE       ) > 0 and token_ids[-1] == self.eos_token_id:
               warnings.warn(
                   f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                   " eos tokens being added."       )
               return token_ids
          else:
               return token_ids + [self.eos_token_id]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     List[int]:
          _snake_case				= [self.eos_token_id]

          if token_ids_a is None:
               return len(token_ids_a + eos       ) * [0]
          return len(token_ids_a + eos + token_ids_a + eos       ) * [0]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     List[int]:
          _snake_case				= self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE       )
          if token_ids_a is None:
               return token_ids_a
          else:
               _snake_case				= self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE       )
               return token_ids_a + token_ids_a






     def __getstate__(					self       )				->     Dict:
          _snake_case				= self.__dict__.copy()
          _snake_case				= None
          return state






     def __setstate__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= d

          # for backward compatibility
          if not hasattr(self   ,"sp_model_kwargs"       ):
               _snake_case				= {}

          _snake_case				= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
          self.sp_model.Load(self.vocab_file       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     List[str]:
          # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
          # the beginning of the text
          if not self.legacy:
               _snake_case				= SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE   ," "       )
          return super().tokenize(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          if not self.legacy:
               _snake_case				= text.startswith(_SCREAMING_SNAKE_CASE       )
               if is_first:
                    _snake_case				= text[1:]

          _snake_case				= self.sp_model.encode(_SCREAMING_SNAKE_CASE   ,out_type=_SCREAMING_SNAKE_CASE       )

          if not self.legacy and not is_first and not text.startswith(" "       ) and tokens[0].startswith(_SCREAMING_SNAKE_CASE       ):
               _snake_case				= ([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
          return tokens






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if token.startswith("<extra_id_"       ):
               _snake_case				= re.match(r"<extra_id_(\d+)>"   ,_SCREAMING_SNAKE_CASE       )
               _snake_case				= int(match.group(1       )       )
               return self.vocab_size - num - 1
          return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          if index < self.sp_model.get_piece_size():
               _snake_case				= self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE       )
          else:
               _snake_case				= f"""<extra_id_{self.vocab_size - 1 - index}>"""
          return token






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= []
          _snake_case				= ""
          _snake_case				= False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special:
                         out_string += " "
                    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE       ) + token
                    _snake_case				= True
                    _snake_case				= []
               else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE       )
                    _snake_case				= False
          out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE       )
          return out_string.strip()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[str]:
          if not os.path.isdir(_SCREAMING_SNAKE_CASE       ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
               return
          _snake_case				= os.path.join(
              _SCREAMING_SNAKE_CASE   ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

          if os.path.abspath(self.vocab_file       ) != os.path.abspath(_SCREAMING_SNAKE_CASE       ) and os.path.isfile(self.vocab_file       ):
               copyfile(self.vocab_file   ,_SCREAMING_SNAKE_CASE       )
          elif not os.path.isfile(self.vocab_file       ):
               with open(_SCREAMING_SNAKE_CASE   ,"wb"       ) as fi:
                    _snake_case				= self.sp_model.serialized_model_proto()
                    fi.write(_SCREAMING_SNAKE_CASE       )

          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_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''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      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




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

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

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





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




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


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




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

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


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


