
import argparse
from pathlib import Path

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		):
    if config_name_or_path is None:
        snake_case__						=				"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

    if generator_tokenizer_name_or_path is None:
        snake_case__						=				generator_name_or_path

    if question_encoder_tokenizer_name_or_path is None:
        snake_case__						=				question_encoder_name_or_path

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

    # Save model.
    snake_case__						=				RagConfig.from_pretrained(__lowerCAmelCase							)
    snake_case__						=				AutoConfig.from_pretrained(__lowerCAmelCase							)
    snake_case__						=				AutoConfig.from_pretrained(__lowerCAmelCase							)

    snake_case__						=				gen_config
    snake_case__						=				question_encoder_config

    snake_case__						=				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.
    snake_case__						=				AutoTokenizer.from_pretrained(__lowerCAmelCase							)
    gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"							)
    snake_case__						=				AutoTokenizer.from_pretrained(__lowerCAmelCase							)
    question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"							)


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

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__            =  {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPTokenizerFast''']

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPFeatureExtractor''']
     __magic_name__            =  ['''CLIPImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''CLIPModel''',
         '''CLIPPreTrainedModel''',
         '''CLIPTextModel''',
         '''CLIPTextModelWithProjection''',
         '''CLIPVisionModel''',
         '''CLIPVisionModelWithProjection''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFCLIPModel''',
         '''TFCLIPPreTrainedModel''',
         '''TFCLIPTextModel''',
         '''TFCLIPVisionModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''FlaxCLIPModel''',
         '''FlaxCLIPPreTrainedModel''',
         '''FlaxCLIPTextModel''',
         '''FlaxCLIPTextPreTrainedModel''',
         '''FlaxCLIPVisionModel''',
         '''FlaxCLIPVisionPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

     __magic_name__            =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



