

'''simple docstring'''

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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase__			    =			logging.get_logger(__name__)

lowercase__			    =			"""▁"""

lowercase__			    =			{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowercase__			    =			{
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

lowercase__			    =			{
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
lowercase__			    =			{
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}




class        A_ (				_snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:	int  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:	str  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:	int  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:	List[Any]  		=  ['input_ids', 'attention_mask']

     UpperCAmelCase_						:	List[int]  		=  []
     UpperCAmelCase_						:	List[int]  		=  []


     def __init__(		self :     Tuple     ,			lowercase_ :     List[str]     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     Dict=None     ,			lowercase_ :     str=None     ,			lowercase_ :     List[str]="<s>"     ,			lowercase_ :     Optional[int]="</s>"     ,			lowercase_ :     List[Any]="</s>"     ,			lowercase_ :     Union[str, Any]="<pad>"     ,			lowercase_ :     List[Any]="<unk>"     ,			lowercase_ :     List[Any]="m2m100"     ,			lowercase_ :     Optional[int] = None     ,			lowercase_ :     Union[str, Any]=8     ,			**lowercase_ :     Union[str, Any]     ,			)						->	int:
      UpperCAmelCase       :	Dict											= {} if sp_model_kwargs is None else sp_model_kwargs

      UpperCAmelCase       :	Dict											= language_codes
      UpperCAmelCase       :	Any											= FAIRSEQ_LANGUAGE_CODES[language_codes]
      UpperCAmelCase       :	Any											= {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

      UpperCAmelCase       :	Union[str, Any]											= kwargs.get('additional_special_tokens'     ,			[]       )
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(__lowerCAmelCase       )
          for lang_code in fairseq_language_code
          if self.get_lang_token(__lowerCAmelCase       ) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=__lowerCAmelCase     ,			tgt_lang=__lowerCAmelCase     ,			bos_token=__lowerCAmelCase     ,			eos_token=__lowerCAmelCase     ,			sep_token=__lowerCAmelCase     ,			unk_token=__lowerCAmelCase     ,			pad_token=__lowerCAmelCase     ,			language_codes=__lowerCAmelCase     ,			sp_model_kwargs=self.sp_model_kwargs     ,			num_madeup_words=__lowerCAmelCase     ,			**__lowerCAmelCase     ,			)

      UpperCAmelCase       :	int											= vocab_file
      UpperCAmelCase       :	Any											= load_json(__lowerCAmelCase       )
      UpperCAmelCase       :	str											= {v: k for k, v in self.encoder.items()}
      UpperCAmelCase       :	Optional[Any]											= spm_file
      UpperCAmelCase       :	Union[str, Any]											= load_spm(__lowerCAmelCase     ,			self.sp_model_kwargs       )

      UpperCAmelCase       :	Union[str, Any]											= len(self.encoder       )

      UpperCAmelCase       :	Optional[Any]											= {
          self.get_lang_token(__lowerCAmelCase       ): self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase       )
      }
      UpperCAmelCase       :	Tuple											= {lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase       )}
      UpperCAmelCase       :	Any											= {v: k for k, v in self.lang_token_to_id.items()}

      UpperCAmelCase       :	Dict											= src_lang if src_lang is not None else 'en'
      UpperCAmelCase       :	str											= tgt_lang
      UpperCAmelCase       :	Any											= self.get_lang_id(self._src_lang       )
      self.set_src_lang_special_tokens(self._src_lang       )

      UpperCAmelCase       :	Optional[int]											= num_madeup_words


     @property
     def    UpperCAmelCase_       (		self :     Optional[int]       )						->	Tuple:
      return len(self.encoder       ) + len(self.lang_token_to_id       )


     @property
     def    UpperCAmelCase_       (		self :     Dict       )						->	List[Any]:
      return self._src_lang


     @src_lang.setter
     def    UpperCAmelCase_       (		self :     int     ,			lowercase_ :     int       )						->	Optional[int]:
      UpperCAmelCase       :	int											= new_src_lang
      self.set_src_lang_special_tokens(self._src_lang       )


     def    UpperCAmelCase_       (		self :     Any     ,			lowercase_ :     List[str]       )						->	Tuple:
      return self.sp_model.encode(__lowerCAmelCase     ,			out_type=__lowerCAmelCase       )


     def    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     Tuple       )						->	Optional[int]:



      if token in self.lang_token_to_id:
       return self.lang_token_to_id[token]
      return self.encoder.get(__lowerCAmelCase     ,			self.encoder[self.unk_token]       )


     def    UpperCAmelCase_       (		self :     Tuple     ,			lowercase_ :     str       )						->	Dict:



      if index in self.id_to_lang_token:
       return self.id_to_lang_token[index]
      return self.decoder.get(__lowerCAmelCase     ,			self.unk_token       )


     def    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     Any       )						->	Any:
      UpperCAmelCase       :	Optional[Any]											= []
      UpperCAmelCase       :	List[str]											= ''
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        out_string += self.sp_model.decode(__lowerCAmelCase       ) + token
        UpperCAmelCase       :	List[str]											= []
       else:
        current_sub_tokens.append(__lowerCAmelCase       )
      out_string += self.sp_model.decode(__lowerCAmelCase       )
      return out_string.strip()


     def    UpperCAmelCase_       (		self :     Optional[int]     ,			lowercase_ :     Any     ,			lowercase_ :     Union[str, Any] = None     ,			lowercase_ :     Optional[Any] = False       )						->	Union[str, Any]:



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

      UpperCAmelCase       :	Any											= [1] * len(self.prefix_tokens       )
      UpperCAmelCase       :	Union[str, Any]											= [1] * len(self.suffix_tokens       )
      if token_ids_a is None:
       return prefix_ones + ([0] * len(__lowerCAmelCase       )) + suffix_ones
      return prefix_ones + ([0] * len(__lowerCAmelCase       )) + ([0] * len(__lowerCAmelCase       )) + suffix_ones


     def    UpperCAmelCase_       (		self :     Dict     ,			lowercase_ :     str     ,			lowercase_ :     int = None       )						->	str:



      if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # 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.suffix_tokens


     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	str:
      UpperCAmelCase       :	int											= {self.convert_ids_to_tokens(__lowerCAmelCase       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab


     def __getstate__(		self :     List[Any]       )						->	Any:
      UpperCAmelCase       :	str											= self.__dict__.copy()
      UpperCAmelCase       :	Any											= None
      return state


     def __setstate__(		self :     Optional[Any]     ,			lowercase_ :     Dict       )						->	Optional[int]:
      UpperCAmelCase       :	Union[str, Any]											= d

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

      UpperCAmelCase       :	Optional[Any]											= load_spm(self.spm_file     ,			self.sp_model_kwargs       )


     def    UpperCAmelCase_       (		self :     Optional[int]     ,			lowercase_ :     int     ,			lowercase_ :     Optional[int] = None       )						->	int:
      UpperCAmelCase       :	List[str]											= Path(__lowerCAmelCase       )
      if not save_dir.is_dir():
       raise OSError(f"""{save_directory} should be a directory"""       )
      UpperCAmelCase       :	Tuple											= save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
      )
      UpperCAmelCase       :	str											= save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
      )

      save_json(self.encoder     ,			__lowerCAmelCase       )

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

      return (str(__lowerCAmelCase       ), str(__lowerCAmelCase       ))


     def    UpperCAmelCase_       (		self :     Optional[Any]     ,			lowercase_ :     int     ,			lowercase_ :     int = "en"     ,			lowercase_ :     Tuple = None     ,			lowercase_ :     str = "ro"     ,			**lowercase_ :     List[str]     ,			)						->	Optional[Any]:
      UpperCAmelCase       :	Any											= src_lang
      UpperCAmelCase       :	Union[str, Any]											= tgt_lang
      self.set_src_lang_special_tokens(self.src_lang       )
      return super().prepare_seqaseq_batch(__lowerCAmelCase     ,			__lowerCAmelCase     ,			**__lowerCAmelCase       )


     def    UpperCAmelCase_       (		self :     int     ,			lowercase_ :     str     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     List[Any]     ,			**lowercase_ :     List[str]       )						->	List[Any]:



      if src_lang is None or tgt_lang is None:
       raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'       )
      UpperCAmelCase       :	List[str]											= src_lang
      UpperCAmelCase       :	Union[str, Any]											= self(__lowerCAmelCase     ,			add_special_tokens=__lowerCAmelCase     ,			**__lowerCAmelCase       )
      UpperCAmelCase       :	Any											= self.get_lang_id(__lowerCAmelCase       )
      UpperCAmelCase       :	Union[str, Any]											= tgt_lang_id
      return inputs


     def    UpperCAmelCase_       (		self :     List[Any]       )						->	Any:
      self.set_src_lang_special_tokens(self.src_lang       )


     def    UpperCAmelCase_       (		self :     Optional[int]       )						->	Union[str, Any]:
      self.set_tgt_lang_special_tokens(self.tgt_lang       )


     def    UpperCAmelCase_       (		self :     Any     ,			lowercase_ :     Any       )						->	List[str]:
      UpperCAmelCase       :	Any											= self.get_lang_token(__lowerCAmelCase       )
      UpperCAmelCase       :	str											= self.lang_token_to_id[lang_token]
      UpperCAmelCase       :	Union[str, Any]											= [self.cur_lang_id]
      UpperCAmelCase       :	Any											= [self.eos_token_id]


     def    UpperCAmelCase_       (		self :     Optional[int]     ,			lowercase_ :     Union[str, Any]       )						->	Tuple:
      UpperCAmelCase       :	Union[str, Any]											= self.get_lang_token(__lowerCAmelCase       )
      UpperCAmelCase       :	Tuple											= self.lang_token_to_id[lang_token]
      UpperCAmelCase       :	Any											= [self.cur_lang_id]
      UpperCAmelCase       :	Tuple											= [self.eos_token_id]


     def    UpperCAmelCase_       (		self :     str     ,			lowercase_ :     str       )						->	Any:
      return self.lang_code_to_token[lang]




     def    UpperCAmelCase_       (		self :     Tuple     ,			lowercase_ :     List[str]       )						->	List[str]:
      UpperCAmelCase       :	str											= self.get_lang_token(__lowerCAmelCase       )
      return self.lang_token_to_id[lang_token]




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	str											= sentencepiece.SentencePieceProcessor(**lowerCAmelCase__	)
 spm.Load(str(lowerCAmelCase__	)	)
 return spm




def       UpperCamelCase(     UpperCAmelCase_	):


 with open(lowerCAmelCase__  ,       'r'	) as f:
  return json.load(lowerCAmelCase__	)




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):


 with open(lowerCAmelCase__  ,       'w'	) as f:
  json.dump(lowerCAmelCase__  ,       lowerCAmelCase__  ,       indent=2	)





"""simple docstring"""






import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		) ->    Optional[int]:
      '''simple docstring'''


      if config_name_or_path is None:
            lowercase														=	"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

      if generator_tokenizer_name_or_path is None:
            lowercase														=	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowercase														=	question_encoder_name_or_path

      lowercase														=	RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

      # Save model.
      lowercase														=	RagConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)

      lowercase														=	gen_config
      lowercase														=	question_encoder_config

      lowercase														=	model_class.from_pretrained_question_encoder_generator(
          lowerCAmelCase__					,		lowerCAmelCase__					,		config=lowerCAmelCase__		)
      rag_model.save_pretrained(lowerCAmelCase__		)

      # Sanity check.
      model_class.from_pretrained(lowerCAmelCase__		)

      # Save tokenizers.
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""		)
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""		)


if __name__ == "__main__":
       __lowerCAmelCase    :       int					     =argparse.ArgumentParser()
       parser.add_argument(
           """--model_type""",
           choices=["""rag_sequence""", """rag_token"""],
           required=True,
           type=str,
           help="""RAG model type: rag_sequence, rag_token""",
       )
       parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
       parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
       parser.add_argument(
           """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
       )

       parser.add_argument(
           """--generator_tokenizer_name_or_path""",
           type=str,
           help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
       )
       parser.add_argument(
           """--question_encoder_tokenizer_name_or_path""",
           type=str,
           help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
       )
       parser.add_argument(
           """--config_name_or_path""",
           type=str,
           help=(
               """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
               """ ``model_type``"""
           ),
       )

       __lowerCAmelCase    :       List[str]					     =parser.parse_args()

       __lowerCAmelCase    :       Dict					     =Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )



