import argparse
from pathlib import Path

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Path			,					__UpperCamelCase   :	str = None			,					__UpperCamelCase   :	str = None			,					__UpperCamelCase   :	str = None			,					)							-> Tuple:







  """simple docstring"""





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

  if generator_tokenizer_name_or_path is None:
    SCREAMING_SNAKE_CASE__      							=				generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    SCREAMING_SNAKE_CASE__      							=				question_encoder_name_or_path

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

  # Save model.
  SCREAMING_SNAKE_CASE__      							=				RagConfig.from_pretrained(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				AutoConfig.from_pretrained(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				AutoConfig.from_pretrained(__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				gen_config
  SCREAMING_SNAKE_CASE__      							=				question_encoder_config

  SCREAMING_SNAKE_CASE__      							=				model_class.from_pretrained_question_encoder_generator(
      __UpperCamelCase			,					__UpperCamelCase			,					config=__UpperCamelCase     )
  rag_model.save_pretrained(__UpperCamelCase     )

  # Sanity check.
  model_class.from_pretrained(__UpperCamelCase     )

  # Save tokenizers.
  SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
  gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""     )
  SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
  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      :			Optional[Any]							       =		parser.parse_args()

   __lowerCamelCase      :			Union[str, Any]							       =		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_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase      :			Optional[Any]							       =		{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Dict							       =		['''EfficientFormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFEfficientFormerForImageClassification''',
       '''TFEfficientFormerForImageClassificationWithTeacher''',
       '''TFEfficientFormerModel''',
       '''TFEfficientFormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			List[Any]							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
