


"""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


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					'▁'

__snake_case    					=					{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__snake_case    					=					{
    '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',
    },
}

__snake_case    					=					{
    'facebook/m2m100_418M': 1024,
}

# fmt: off
__snake_case    					=					{
    '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 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



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

   _a   :       List[int]									=    []
   _a   :       List[int]									=    []


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="m2m100" ,     lowerCamelCase__ = None ,     lowerCamelCase__=8 ,     **lowerCamelCase__ ,     )      ->							None:
    lowercase__       :       Any				=						{} if sp_model_kwargs is None else sp_model_kwargs

    lowercase__       :       Any				=						language_codes
    lowercase__       :       List[Any]				=						FAIRSEQ_LANGUAGE_CODES[language_codes]
    lowercase__       :       List[Any]				=						{lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

    lowercase__       :       str				=						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__ ,     )

    lowercase__       :       List[Any]				=						vocab_file
    lowercase__       :       Dict				=						load_json(lowerCamelCase__						)
    lowercase__       :       List[str]				=						{v: k for k, v in self.encoder.items()}
    lowercase__       :       Optional[Any]				=						spm_file
    lowercase__       :       Dict				=						load_spm(lowerCamelCase__ ,     self.sp_model_kwargs						)

    lowercase__       :       List[Any]				=						len(self.encoder						)

    lowercase__       :       Any				=						{
        self.get_lang_token(lowerCamelCase__						): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__						)
    }
    lowercase__       :       str				=						{lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__						)}
    lowercase__       :       List[Any]				=						{v: k for k, v in self.lang_token_to_id.items()}

    lowercase__       :       Optional[Any]				=						src_lang if src_lang is not None else """en"""
    lowercase__       :       Optional[Any]				=						tgt_lang
    lowercase__       :       Dict				=						self.get_lang_id(self._src_lang						)
    self.set_src_lang_special_tokens(self._src_lang						)

    lowercase__       :       str				=						num_madeup_words


   @property
   def 					UpperCAmelCase__(						self						)      ->							int:
    return len(self.encoder						) + len(self.lang_token_to_id						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    return self._src_lang


   @src_lang.setter
   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							None:
    lowercase__       :       List[str]				=						new_src_lang
    self.set_src_lang_special_tokens(self._src_lang						)


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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    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 ,     lowerCamelCase__						)      ->							str:
    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 ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[Any]				=						[]
    lowercase__       :       Union[str, Any]				=						""""""
    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
      lowercase__       :       Optional[int]				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:

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

    lowercase__       :       Tuple				=						[1] * len(self.prefix_tokens						)
    lowercase__       :       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 ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    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						)      ->							Dict:
    lowercase__       :       List[str]				=						{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						)      ->							Dict:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Optional[int]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							None:
    lowercase__       :       List[str]				=						d

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

    lowercase__       :       List[Any]				=						load_spm(self.spm_file ,     self.sp_model_kwargs						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    lowercase__       :       List[Any]				=						Path(lowerCamelCase__						)
    if not save_dir.is_dir():
     raise OSError(F'''{save_directory} should be a directory'''						)
    lowercase__       :       str				=						save_dir / (
        (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
    )
    lowercase__       :       int				=						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:
      lowercase__       :       Optional[int]				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (str(lowerCamelCase__						), str(lowerCamelCase__						))


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = "en" ,     lowerCamelCase__ = None ,     lowerCamelCase__ = "ro" ,     **lowerCamelCase__ ,     )      ->							BatchEncoding:
    lowercase__       :       str				=						src_lang
    lowercase__       :       Tuple				=						tgt_lang
    self.set_src_lang_special_tokens(self.src_lang						)
    return super().prepare_seqaseq_batch(lowerCamelCase__ ,     lowerCamelCase__ ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Union[str, 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"""						)
    lowercase__       :       Optional[Any]				=						src_lang
    lowercase__       :       List[Any]				=						self(lowerCamelCase__ ,     add_special_tokens=lowerCamelCase__ ,     **lowerCamelCase__						)
    lowercase__       :       int				=						self.get_lang_id(lowerCamelCase__						)
    lowercase__       :       List[Any]				=						tgt_lang_id
    return inputs


   def 					UpperCAmelCase__(						self						)      ->							int:
    self.set_src_lang_special_tokens(self.src_lang						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    self.set_tgt_lang_special_tokens(self.tgt_lang						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							None:
    lowercase__       :       Dict				=						self.get_lang_token(lowerCamelCase__						)
    lowercase__       :       Any				=						self.lang_token_to_id[lang_token]
    lowercase__       :       List[Any]				=						[self.cur_lang_id]
    lowercase__       :       Tuple				=						[self.eos_token_id]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							None:
    lowercase__       :       Tuple				=						self.get_lang_token(lowerCamelCase__						)
    lowercase__       :       Any				=						self.lang_token_to_id[lang_token]
    lowercase__       :       Optional[Any]				=						[self.cur_lang_id]
    lowercase__       :       Union[str, Any]				=						[self.eos_token_id]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    return self.lang_code_to_token[lang]







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Union[str, Any]				=						self.get_lang_token(lowerCamelCase__						)
    return self.lang_token_to_id[lang_token]


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Dict[str, Any]  ):
 lowercase__       :       str				=						sentencepiece.SentencePieceProcessor(**lowerCamelCase__  )
 spm.Load(str(lowerCamelCase__  )  )
 return spm


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 with open(lowerCamelCase__       ,		"""r"""  ) as f:
  return json.load(lowerCamelCase__  )






def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							str  ):
 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    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

 # Save model.
 lowercase__       :       Union[str, Any]				=						RagConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						AutoConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__  )

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       str				=						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__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					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``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					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,
   )