



'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
  import torch


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


lowercase_									=    logging.get_logger(__name__)
lowercase_									=    {"vocab_file": "spiece.model"}

lowercase_									=    {
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

lowercase_									=    {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			List[str]		=			PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			Any		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase       :			Union[str, Any]		=			['input_ids', 'attention_mask']



 def __init__(self					,  A					,  A=False					,  A=False					,  A=False					,  A=None					,  A=None					,  A=None					,  A=None					,  A = None					,  **A					,  )       ->	None:


      """simple docstring"""



      _a           =    {} if sp_model_kwargs is None else sp_model_kwargs

      _a           =    kwargs.get('''name_or_path'''			)
      if name_or_path is None:
           logger.warning(
               '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
               ''' you are testing the model, this can safely be ignored'''			)
           _a           =    '''None'''

      # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
      _a           =    '''<|endoftext|>''' if eos_token is None else eos_token
      _a           =    '''<unk>''' if unk_token is None else unk_token
      if "gpt-sw3-7b" in name_or_path:
           _a           =    unk_token if pad_token is None else pad_token
           _a           =    eos_token if bos_token is None else bos_token
      else:
           _a           =    '''<pad>''' if pad_token is None else pad_token
           _a           =    '''<s>''' if bos_token is None else bos_token

      super().__init__(
          do_lower_case=A					,  remove_space=A					,  keep_accents=A					,  bos_token=A					,  eos_token=A					,  unk_token=A					,  pad_token=A					,  sp_model_kwargs=self.sp_model_kwargs					,  **A					,  )

      _a           =    do_lower_case
      _a           =    remove_space
      _a           =    keep_accents
      _a           =    vocab_file

      _a           =    spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(A			)

      # Used for whitespace normalization in input texts
      # fmt : off
      _a           =    {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      _a           =    re.compile(
          f'''[{''.join(map(A					,  list(range(0					,  9			)			) + list(range(11					,  32			)			) + list(range(127					,  160			)			) + [160, 173, 8_203]			)			)}]'''			)



 def __getstate__(self			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.__dict__.copy()
      _a           =    None
      return state



 def __setstate__(self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    d

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

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



 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return len(self.sp_model			)



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    self.non_printing_characters_re.sub(''''''					,  A			)

      # Normalize whitespaces
      _a           =    ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]			)

      # NFC Unicode normalization
      _a           =    unicodedata.normalize('''NFC'''					,  A			)
      return text



 def    a__    (self					,  A					,  **A			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.preprocess_text(A			)
      return self.sp_model.encode(A					,  out_type=A			)



 def    a__    (self					,  A			)       ->	int:


      """simple docstring"""



      return self.sp_model.PieceToId(A			)



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      return self.sp_model.IdToPiece(A			)



 @staticmethod
 def    a__    (A			)       ->	str:


      """simple docstring"""



      return out_string



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    []
      _a           =    ''''''
      _a           =    False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                if not prev_is_special:
                     out_string += " "

                out_string += self.sp_model.decode(A			) + token
                _a           =    True
                _a           =    []
           else:
                current_sub_tokens.append(A			)
                _a           =    False
      out_string += self.sp_model.decode(A			)

      return out_string



 def    a__    (self			)       ->	Dict[str, int]:


      """simple docstring"""



      _a           =    {self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab



 def    a__    (self					,  A					,  A = None			)       ->	Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A			):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
           return
      _a           =    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			) and os.path.isfile(self.vocab_file			):
           copyfile(self.vocab_file					,  A			)
      elif not os.path.isfile(self.vocab_file			):
           with open(A					,  '''wb'''			) as fi:
                _a           =    self.sp_model.serialized_model_proto()
                fi.write(A			)

      return (out_vocab_file,)



 def    a__    (self					,  A					,  A = False			)       ->	Union[List[int], List[List[int]], "torch.Tensor"]:


      """simple docstring"""



      if isinstance(A					,  A			):
           _a           =    self.preprocess_text(A			)
           _a           =    self.sp_model.encode(A			)
      else:
           _a           =    [self.preprocess_text(A			) for t in text]
           _a           =    self.sp_model.encode(A			)

      if return_tensors is True or return_tensors == "pt":
           _a           =    torch.tensor(A			)

      return token_ids



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      return self.sp_model.decode(A			)



 def    a__    (self					,  A			)       ->	List[int]:


      """simple docstring"""



      _a           =    [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
      _a           =    (
          f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A			) + f'''{self.bos_token}Bot:'''
      )
      return self.encode(text=A			)







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



