





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		"""▁"""

lowercase_          =		{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

lowercase_          =		{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

lowercase_          =		{
    """facebook/s2t-small-librispeech-asr""": 1_024,
}

lowercase_          =		["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

lowercase_          =		{"""mustc""": MUSTC_LANGS}
class     SCREAMING_SNAKE_CASE       (lowercase__				):
   _UpperCamelCase   :		List[str]			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Any			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		List[Any]			 =	MAX_MODEL_INPUT_SIZES
   _UpperCamelCase   :		Dict			 =	["""input_ids""", """attention_mask"""]

   _UpperCamelCase   :		List[int]			 =	[]


   def __init__(   self      :    List[str]      ,					a      :    Dict      ,					a      :    int      ,					a      :    Tuple="<s>"      ,					a      :    Optional[int]="</s>"      ,					a      :    Optional[Any]="<pad>"      ,					a      :    Dict="<unk>"      ,					a      :    Optional[int]=False      ,					a      :    Optional[Any]=False      ,					a      :    str=None      ,					a      :    Optional[Any]=None      ,					a      :    List[Any] = None      ,					**a      :    Tuple      ,					)->       Tuple:





     """simple docstring"""







     lowercase__           =  {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=lowercase_      ,					eos_token=lowercase_      ,					unk_token=lowercase_      ,					pad_token=lowercase_      ,					do_upper_case=lowercase_      ,					do_lower_case=lowercase_      ,					tgt_lang=lowercase_      ,					lang_codes=lowercase_      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**lowercase_      ,					)
     lowercase__           =  do_upper_case
     lowercase__           =  do_lower_case

     lowercase__           =  load_json(lowercase_	)
     lowercase__           =  {v: k for k, v in self.encoder.items()}
     lowercase__           =  spm_file
     lowercase__           =  load_spm(lowercase_      ,					self.sp_model_kwargs	)

     if lang_codes is not None:
       lowercase__           =  lang_codes
       lowercase__           =  LANGUAGES[lang_codes]
       lowercase__           =  [f"""<lang:{lang}>""" for lang in self.langs]
       lowercase__           =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""	) for lang in self.langs}

       lowercase__           =  self.lang_tokens
       lowercase__           =  tgt_lang if tgt_lang is not None else self.langs[0]

       self.set_tgt_lang_special_tokens(self._tgt_lang	)
     else:
       lowercase__           =  {}


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Optional[int]:





     """simple docstring"""







     return len(self.encoder	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       List[str]:





     """simple docstring"""







     return self._tgt_lang


   @tgt_lang.setter
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    Any	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  new_tgt_lang
     self.set_tgt_lang_special_tokens(lowercase_	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Optional[Any]	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.lang_code_to_id[tgt_lang]
     lowercase__           =  [lang_code_id]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Tuple	)->       int:





     """simple docstring"""







     return self.sp_model.encode(lowercase_      ,					out_type=lowercase_	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Tuple	)->       List[str]:





     """simple docstring"""







     return self.encoder.get(lowercase_      ,					self.encoder[self.unk_token]	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     return self.decoder.get(lowercase_      ,					self.unk_token	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Optional[int]	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  []
     lowercase__           =  ""
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         lowercase__           =  self.sp_model.decode(lowercase_	)
         out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
         lowercase__           =  []
       else:
         current_sub_tokens.append(lowercase_	)
     lowercase__           =  self.sp_model.decode(lowercase_	)
     out_string += decoded.upper() if self.do_upper_case else decoded
     return out_string.strip()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[Any]      ,					a      :    Optional[Any]=None	)->       int:





     """simple docstring"""







     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Any      ,					a      :    Tuple = None      ,					a      :    Union[str, Any] = False	)->       Optional[Any]:





     """simple docstring"""







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

     lowercase__           =  [1] * len(self.prefix_tokens	)
     lowercase__           =  [1]
     if token_ids_a is None:
       return prefix_ones + ([0] * len(lowercase_	)) + suffix_ones
     return prefix_ones + ([0] * len(lowercase_	)) + ([0] * len(lowercase_	)) + suffix_ones


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  self.encoder.copy()
     vocab.update(self.added_tokens_encoder	)
     return vocab


   def __getstate__(   self      :    Optional[int]	)->       Any:





     """simple docstring"""







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


   def __setstate__(   self      :    Tuple      ,					a      :    Optional[int]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  d

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

     lowercase__           =  load_spm(self.spm_file      ,					self.sp_model_kwargs	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    str      ,					a      :    Tuple = None	)->       str:





     """simple docstring"""







     lowercase__           =  Path(lowercase_	)
     assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
     lowercase__           =  save_dir / (
         (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
     )
     lowercase__           =  save_dir / (
         (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
     )

     save_json(self.encoder      ,					lowercase_	)

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

     return (str(lowercase_	), str(lowercase_	))






def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  lowercase__           =  sentencepiece.SentencePieceProcessor(**__lowerCamelCase		)
  spm.Load(str(__lowerCamelCase		)		)
  return spm




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  with open(__lowerCamelCase		,						'r'		) as f:
    return json.load(__lowerCamelCase		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Any:
  with open(__lowerCamelCase		,						'w'		) as f:
    json.dump(__lowerCamelCase		,						__lowerCamelCase		,						indent=2		)












import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

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

  return model


if __name__ == "__main__":
      lowercase_          =		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""")
      lowercase_          =		parser.parse_args()
      lowercase_          =		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)


