






"""simple docstring"""







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:
										a__          =  '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

					if generator_tokenizer_name_or_path is None:
										a__          =  generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										a__          =  question_encoder_name_or_path

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

					# Save model.
					a__          =  RagConfig.from_pretrained(lowerCamelCase_)
					a__          =  AutoConfig.from_pretrained(lowerCamelCase_)
					a__          =  AutoConfig.from_pretrained(lowerCamelCase_)

					a__          =  gen_config
					a__          =  question_encoder_config

					a__          =  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.
					a__          =  AutoTokenizer.from_pretrained(lowerCamelCase_)
					gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''')
					a__          =  AutoTokenizer.from_pretrained(lowerCamelCase_)
					question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''')


if __name__ == "__main__":
	__a       :	List[str]             = 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``'
	    ),
	)

	__a       :	List[Any]             = parser.parse_args()

	__a       :	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,
	)










"""simple docstring"""







__a       :	List[Any]             = frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'negative_prompt'])

__a       :	Any             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Dict             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Optional[Any]             = frozenset(['prompt', 'image', 'negative_prompt'])

__a       :	List[Any]             = frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

__a       :	Optional[Any]             = frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	int             = frozenset(['image', 'mask_image'])

__a       :	Tuple             = frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Optional[Any]             = frozenset(['example_image', 'image', 'mask_image'])

__a       :	Optional[Any]             = frozenset(['class_labels'])

__a       :	Tuple             = frozenset(['class_labels'])

__a       :	int             = frozenset(['batch_size'])

__a       :	int             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['batch_size'])

__a       :	Tuple             = frozenset([])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Dict             = frozenset(['prompt', 'negative_prompt'])
__a       :	Optional[int]             = frozenset(['input_tokens'])

__a       :	str             = frozenset(['input_tokens'])


