"""simple docstring"""
import argparse
from pathlib import Path

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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE = None		,					_SCREAMING_SNAKE_CASE = None		,					_SCREAMING_SNAKE_CASE = None		,					)     ->Optional[Any]:
  if config_name_or_path is None:
    a__:					Tuple      							=     'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    a__:					Any      							=     generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    a__:					Optional[Any]      							=     question_encoder_name_or_path

  a__:					Any      							=     RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  a__:					Union[str, Any]      							=     RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[Any]      							=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[int]      							=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )

  a__:					Optional[int]      							=     gen_config
  a__:					Any      							=     question_encoder_config

  a__:					Optional[int]      							=     model_class.from_pretrained_question_encoder_generator(
      _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					config=_SCREAMING_SNAKE_CASE       )
  rag_model.save_pretrained(_SCREAMING_SNAKE_CASE       )

  # Sanity check.
  model_class.from_pretrained(_SCREAMING_SNAKE_CASE       )

  # Save tokenizers.
  a__:					Tuple      							=     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  a__:					List[Any]      							=     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


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

 lowercase__ 	=     parser.parse_args()

 lowercase__ 	=     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,
 )



"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowercase__ 	=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'unc-nlp/lxmert-base-uncased': (
            'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': 512,
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		PRETRAINED_INIT_CONFIGURATION
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		LxmertTokenizer



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase=True   ,      lowercase="[UNK]"   ,      lowercase="[SEP]"   ,      lowercase="[PAD]"   ,      lowercase="[CLS]"   ,      lowercase="[MASK]"   ,      lowercase=True   ,      lowercase=None   ,      **lowercase   ,      )     ->      Dict:





        '''simple docstring'''





        super().__init__(
            lowercase   ,      tokenizer_file=lowercase   ,      do_lower_case=lowercase   ,      unk_token=lowercase   ,      sep_token=lowercase   ,      pad_token=lowercase   ,      cls_token=lowercase   ,      mask_token=lowercase   ,      tokenize_chinese_chars=lowercase   ,      strip_accents=lowercase   ,      **lowercase   ,      )

        a__:					Dict      							=     json.loads(self.backend_tokenizer.normalizer.__getstate__())
        if (
            normalizer_state.get('lowercase'   ,      lowercase) != do_lower_case
            or normalizer_state.get('strip_accents'   ,      lowercase) != strip_accents
            or normalizer_state.get('handle_chinese_chars'   ,      lowercase) != tokenize_chinese_chars
        ):
          a__:					int      							=     getattr(lowercase   ,      normalizer_state.pop('type'))
          a__:					Dict      							=     do_lower_case
          a__:					Dict      							=     strip_accents
          a__:					Optional[int]      							=     tokenize_chinese_chars
          a__:					List[Any]      							=     normalizer_class(**lowercase)

        a__:					Optional[int]      							=     do_lower_case



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=None)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

        return output



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.sep_token_id]
        a__:					List[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) * [0] + len(token_ids_a + sep) * [1]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        a__:					List[Any]      							=     self._tokenizer.model.save(lowercase   ,      name=lowercase)
        return tuple(lowercase)


