




import argparse
from pathlib import Path

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




def 			__magic_name__				( A		:					List[str],  A		:					str,  A		:					str,  A		:					Path,  A		:					str = None,  A		:					str = None,  A		:					str = 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__":
		__lowerCAmelCase		:				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``'
		    ),
		)

		__lowerCAmelCase		:				Any       			=		parser.parse_args()

		__lowerCAmelCase		:				Optional[int]       			=		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 __future__ import annotations

import os
from collections.abc import Mapping

_UpperCAmelCase					: Tuple      =       tuple[int, int]


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     vertices
				UpperCamelCase		=     {
				    (min(A_       ), max(A_       )): weight for edge, weight in edges.items()
				}



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				self.vertices.add(edge[0]       )
				self.vertices.add(edge[1]       )
				UpperCamelCase		=     weight



		def __UpperCamelCase ( self       )  ->					Graph:
				"""simple docstring"""
				UpperCamelCase		=     Graph({min(self.vertices       )}				,      {}       )
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42

				while len(subgraph.vertices       ) < len(self.vertices       ):
						UpperCamelCase		=     max(self.edges.values()       ) + 1
						for edge, weight in self.edges.items():
								if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
										if weight < min_weight:
												UpperCamelCase		=     edge
												UpperCamelCase		=     weight

						subgraph.add_edge(A_				,      A_       )

				return subgraph






def       A       (					lowercase = "p107_network.txt" )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     os.path.abspath(os.path.dirname(lowercase ) )
		UpperCamelCase		=     os.path.join(lowercase    ,       lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     42
		UpperCamelCase		=     42
		UpperCamelCase		=     42

		with open(lowercase ) as f:
				UpperCamelCase		=     f.read().strip().split('\n' )

		UpperCamelCase		=     [line.split(',' ) for line in data]

		for edgea in range(1    ,       len(lowercase ) ):
				for edgea in range(lowercase ):
						if adjaceny_matrix[edgea][edgea] != "-":
								UpperCamelCase		=     int(adjaceny_matrix[edgea][edgea] )

		UpperCamelCase		=     Graph(set(range(len(lowercase ) ) )    ,       lowercase )

		UpperCamelCase		=     graph.prims_algorithm()

		UpperCamelCase		=     sum(graph.edges.values() )
		UpperCamelCase		=     sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
					print(F'''{solution() = }''')
