
"""simple docstring"""





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a = None				,   __a = None				,   __a = None				,   ):

					'''simple docstring'''





					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(__a				)
					A_				 =						AutoConfig.from_pretrained(__a				)
					A_				 =						AutoConfig.from_pretrained(__a				)

					A_				 =						gen_config
					A_				 =						question_encoder_config

					A_				 =						model_class.from_pretrained_question_encoder_generator(
					    __a				,   __a				,   config=__a				)
					rag_model.save_pretrained(__a				)

					# Sanity check.
					model_class.from_pretrained(__a				)

					# Save tokenizers.
					A_				 =						AutoTokenizer.from_pretrained(__a				)
					gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"				)
					A_				 =						AutoTokenizer.from_pretrained(__a				)
					question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"				)


if __name__ == "__main__":
		UpperCamelCase_					:						Optional[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``'''
		    ),
		)

		UpperCamelCase_					:						Dict				= parser.parse_args()

		UpperCamelCase_					:						Tuple				= 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"""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0] * n for i in range(__a				)]
					for i in range(__a				):
										A_				 =						y_points[i]

					for i in range(2				,   __a				):
										for j in range(__a				,   __a				):
															A_				 =						(
															    (xa - x_points[j - i + 1]) * q[j][i - 1]
															    - (xa - x_points[j]) * q[j - 1][i - 1]
															) / (x_points[j] - x_points[j - i + 1])

					return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




