





'''simple docstring'''






import argparse
from pathlib import Path

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





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__				,  lowercase__ = None				,  lowercase__ = None				,  lowercase__ = None				,  )   ->Any:
						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:   List[str]  					=		generator_name_or_path

						if question_encoder_tokenizer_name_or_path is None:
												_snake_case:   Union[str, Any]  					=		question_encoder_name_or_path

						_snake_case:   int  					=		RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

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

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

						_snake_case:   List[Any]  					=		model_class.from_pretrained_question_encoder_generator(
						    lowercase__				,  lowercase__				,  config=lowercase__						)
						rag_model.save_pretrained(lowercase__						)

						# Sanity check.
						model_class.from_pretrained(lowercase__						)

						# Save tokenizers.
						_snake_case:   int  					=		AutoTokenizer.from_pretrained(lowercase__						)
						gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'						)
						_snake_case:   Dict  					=		AutoTokenizer.from_pretrained(lowercase__						)
						question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'						)


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

		A   : Tuple             =			parser.parse_args()

		A   : Optional[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,
		)












'''simple docstring'''






A   : str             =			{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


A   : Union[str, Any]             =			{value: key for key, value in encode_dict.items()}





def       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   Union[str, Any]  					=		''
						for letter in word.lower():
												if letter.isalpha() or letter == " ":
																		encoded += encode_dict[letter]
												else:
																		raise Exception('encode() accepts only letters of the alphabet and spaces'						)
						return encoded





def       lowercase_      (		lowercase__						)   ->str:
						if set(lowercase__						) - {"A", "B", " "} != set():
												raise Exception('decode() accepts only \'A\', \'B\' and spaces'						)
						_snake_case:   List[Any]  					=		''
						for word in coded.split():
												while len(lowercase__						) != 0:
																		decoded += decode_dict[word[:5]]
																		_snake_case:   Optional[int]  					=		word[5:]
												decoded += " "
						return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()





