


"""simple docstring"""






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

import sentencepiece as spm

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


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       "▁"

__snake_case		=       {"vocab_file": "sentencepiece.bpe.model"}

__snake_case		=       {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__snake_case		=       {
    "facebook/xglm-564M": 2048,
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[str]    =		VOCAB_FILES_NAMES
     A_       :	Tuple    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	List[Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_       :	int    =		['input_ids', 'attention_mask']


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase="<s>"    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          _a    	=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          _a    	=				7
          _a    	=				[F'<madeupword{i}>' for i in range(self.num_madeup_words							)]

          _a    	=				kwargs.get('''additional_special_tokens'''    ,				[]							)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)

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

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          _a    	=				1

          # Mimic fairseq token-to-id alignment for the first 4 token
          _a    	=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          _a    	=				len(self.sp_model							)
          _a    	=				{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
          self.fairseq_tokens_to_ids.update(__UpperCAmelCase							)

          _a    	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}


     def __getstate__(      self							)       ->							Union[str, Any]:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          _a    	=				self.sp_model.serialized_model_proto()
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Tuple:
          _a    	=				d

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

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:

          if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
          _a    	=				[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

          if token_ids_a is None:
               return [1] + ([0] * len(__UpperCAmelCase							))
          return [1] + ([0] * len(__UpperCAmelCase							)) + [1, 1] + ([0] * len(__UpperCAmelCase							))


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]

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


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				{self.convert_ids_to_tokens(__UpperCAmelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							int:
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          _a    	=				self.sp_model.PieceToId(__UpperCAmelCase							)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Dict:
          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							str:
          _a    	=				''''''.join(__UpperCAmelCase							).replace(__UpperCAmelCase    ,				''' '''							).strip()
          return out_string





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				os.path.join(
              __UpperCAmelCase    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

          return (out_vocab_file,)



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)