





import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


__lowerCamelCase :  Optional[Any]										=  logging.get_logger(__name__)

__lowerCamelCase :  Optional[Any]										=  {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

__lowerCamelCase :  str										=  {
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

__lowerCamelCase :  Dict										=  {
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}



def 	A_       (  _lowerCAmelCase  )		->				str:
 UpperCamelCase			:      str 			=     set()
 UpperCamelCase			:      List[str] 			=     word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)  )
  UpperCamelCase			:      int 			=     char

 UpperCamelCase			:      List[str] 			=     set(_lowerCAmelCase  )
 return pairs



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:str							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:int							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





      def __init__(				self ,							A_ ,							A_ ,							A_="<s>" ,							A_="</s>" ,							A_="</s>" ,							A_="<s>" ,							A_="<unk>" ,							A_="<pad>" ,							A_="<mask>" ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							sep_token=A_ ,							cls_token=A_ ,							pad_token=A_ ,							mask_token=A_ ,							**A_ ,							)

       UpperCamelCase			:      Any 			=     vocab_file
       UpperCamelCase			:      Tuple 			=     merges_file

       UpperCamelCase			:      str 			=     {}
       UpperCamelCase			:      Union[str, Any] 			=     0
       UpperCamelCase			:      Dict 			=     1
       UpperCamelCase			:      Dict 			=     2
       UpperCamelCase			:      Optional[Any] 			=     3

       self.add_from_file(A_     )

       UpperCamelCase			:      str 			=     {v: k for k, v in self.encoder.items()}

       with open(A_ ,							encoding="utf-8"     ) as merges_handle:
        UpperCamelCase			:      Optional[Any] 			=     merges_handle.read().split("\n"     )[:-1]
       UpperCamelCase			:      Union[str, Any] 			=     [tuple(merge.split()[:-1]     ) for merge in merges]
       UpperCamelCase			:      List[Any] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      Union[str, Any] 			=     {}





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase			:      Optional[int] 			=     [self.cls_token_id]
       UpperCamelCase			:      str 			=     [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = False     ):
       '''simple docstring'''




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

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





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      Any 			=     [self.cls_token_id]

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





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return len(self.encoder     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return dict(self.encoder ,							**self.added_tokens_encoder     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if token in self.cache:
        return self.cache[token]
       UpperCamelCase			:      Tuple 			=     tuple(A_     )
       UpperCamelCase			:      List[Any] 			=     tuple(list(word[:-1]     ) + [word[-1] + "</w>"]     )
       UpperCamelCase			:      str 			=     get_pairs(A_     )

       if not pairs:
        return token

       while True:
        UpperCamelCase			:      Any 			=     min(A_ ,							key=lambda A_     : self.bpe_ranks.get(A_ ,							float("inf"     )     )     )
        if bigram not in self.bpe_ranks:
         break
        UpperCamelCase			,					UpperCamelCase			:      Any 			=     bigram
        UpperCamelCase			:      Optional[Any] 			=     []
        UpperCamelCase			:      List[Any] 			=     0
        while i < len(A_     ):
         try:
          UpperCamelCase			:      Tuple 			=     word.index(A_ ,							A_     )
         except ValueError:
          new_word.extend(word[i:]     )
          break
         else:
          new_word.extend(word[i:j]     )
          UpperCamelCase			:      int 			=     j

         if word[i] == first and i < len(A_     ) - 1 and word[i + 1] == second:
          new_word.append(first + second     )
          i += 2
         else:
          new_word.append(word[i]     )
          i += 1
        UpperCamelCase			:      int 			=     tuple(A_     )
        UpperCamelCase			:      Optional[int] 			=     new_word
        if len(A_     ) == 1:
         break
        else:
         UpperCamelCase			:      Optional[Any] 			=     get_pairs(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     "@@ ".join(A_     )
       UpperCamelCase			:      str 			=     word[:-4]
       UpperCamelCase			:      str 			=     word
       return word





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     []

       UpperCamelCase			:      Optional[Any] 			=     re.findall(R"\S+\n?" ,							A_     )

       for token in words:
        split_tokens.extend(list(self.bpe(A_     ).split(" "     )     )     )
       return split_tokens





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.encoder.get(A_ ,							self.encoder.get(self.unk_token     )     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     " ".join(A_     ).replace("@@ " ,							""     ).strip()
       return out_string





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if not os.path.isdir(A_     ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
        return
       UpperCamelCase			:      Dict 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Union[str, Any] 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

       if os.path.abspath(self.vocab_file     ) != os.path.abspath(A_     ):
        copyfile(self.vocab_file ,							A_     )

       if os.path.abspath(self.merges_file     ) != os.path.abspath(A_     ):
        copyfile(self.merges_file ,							A_     )

       return out_vocab_file, out_merge_file





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if isinstance(A_ ,							A_     ):
        try:
         with open(A_ ,							"r" ,							encoding="utf-8"     ) as fd:
          self.add_from_file(A_     )
        except FileNotFoundError as fnfe:
         raise fnfe
        except UnicodeError:
         raise Exception(F"""Incorrect encoding detected in {f}, please rebuild the dataset"""     )
        return

       UpperCamelCase			:      Optional[int] 			=     f.readlines()
       for lineTmp in lines:
        UpperCamelCase			:      str 			=     lineTmp.strip()
        UpperCamelCase			:      Optional[Any] 			=     line.rfind(" "     )
        if idx == -1:
         raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"     )
        UpperCamelCase			:      List[Any] 			=     line[:idx]
        UpperCamelCase			:      int 			=     len(self.encoder     )







import argparse
from pathlib import Path

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     )		->				str:
 if config_name_or_path is None:
  UpperCamelCase			:      Dict 			=     "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

 if generator_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     question_encoder_name_or_path

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

 # Save model.
 UpperCamelCase			:      Optional[Any] 			=     RagConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Tuple 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )

 UpperCamelCase			:      int 			=     gen_config
 UpperCamelCase			:      Dict 			=     question_encoder_config

 UpperCamelCase			:      Tuple 			=     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.
 UpperCamelCase			:      Optional[Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"  )


if __name__ == "__main__":
    __lowerCamelCase :  Tuple										=  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 :  Dict										=  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,
    )
