





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	,			):







    if config_name_or_path is None:
        _snake_case :			Optional[Any]  						=   'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

    if generator_tokenizer_name_or_path is None:
        _snake_case :			Union[str, Any]  						=   generator_name_or_path

    if question_encoder_tokenizer_name_or_path is None:
        _snake_case :			str  						=   question_encoder_name_or_path

    _snake_case :			Optional[int]  						=   RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

    # Save model.
    _snake_case :			int  						=   RagConfig.from_pretrained(__snake_case )
    _snake_case :			Any  						=   AutoConfig.from_pretrained(__snake_case )
    _snake_case :			Union[str, Any]  						=   AutoConfig.from_pretrained(__snake_case )

    _snake_case :			List[Any]  						=   gen_config
    _snake_case :			Any  						=   question_encoder_config

    _snake_case :			Dict  						=   model_class.from_pretrained_question_encoder_generator(
        __snake_case	,			__snake_case	,			config=__snake_case )
    rag_model.save_pretrained(__snake_case )

    # Sanity check.
    model_class.from_pretrained(__snake_case )

    # Save tokenizers.
    _snake_case :			List[str]  						=   AutoTokenizer.from_pretrained(__snake_case )
    gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
    _snake_case :			Union[str, Any]  						=   AutoTokenizer.from_pretrained(__snake_case )
    question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )


if __name__ == "__main__":
  lowercase_		:				Any				=						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_		:				List[str]				=						parser.parse_args()

  lowercase_		:				List[str]				=						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,
  )













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_		:				Optional[int]				=						{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Any				=						[
		    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MegaForCausalLM''',
		    '''MegaForMaskedLM''',
		    '''MegaForMultipleChoice''',
		    '''MegaForQuestionAnswering''',
		    '''MegaForSequenceClassification''',
		    '''MegaForTokenClassification''',
		    '''MegaModel''',
		    '''MegaPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






