from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowerCamelCase					:	Union[str, Any]       =  TypeVar('''T''')




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		return (position - 1) // 2




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		return (2 * position) + 1




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		return (2 * position) + 2






class 		a__						(					Generic[T]					):

					def __init__(    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		list[tuple[T, int]]				      =    []
							SCREAMING_SNAKE_CASE_  :		dict[T, int]				      =    {}
							SCREAMING_SNAKE_CASE_  :		int				      =    0

					def __len__(    self      :       List[str]    ):







							"""simple docstring"""






							return self.elements

					def __repr__(    self      :       Dict    ):







							"""simple docstring"""






							return str(self.heap    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							return self.elements == 0

					def 				__UpperCamelCase   (    self      :       str,_A      :       T,_A      :       int    ):







							"""simple docstring"""






							self.heap.append((elem, weight)    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.elements
							self.elements += 1
							self._bubble_up(_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							if self.elements > 1:
									self._swap_nodes(0,self.elements - 1    )
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.heap.pop()
							del self.position_map[elem]
							self.elements -= 1
							if self.elements > 0:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.heap[0]
									self._bubble_down(_A    )
							return elem

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       T,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.position_map[elem]
							SCREAMING_SNAKE_CASE_  :		int				      =    (elem, weight)
							if position > 0:
									SCREAMING_SNAKE_CASE_  :		int				      =    get_parent_position(_A    )
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    self.heap[parent_position]
									if parent_weight > weight:
											self._bubble_up(_A    )
									else:
											self._bubble_down(_A    )
							else:
									self._bubble_down(_A    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       T    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.position_map[elem]
							if curr_pos == 0:
									return None
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    get_parent_position(_A    )
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.heap[curr_pos]
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    self.heap[parent_position]
							if parent_weight > weight:
									self._swap_nodes(_A,_A    )
									return self._bubble_up(_A    )
							return None

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       T    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.position_map[elem]
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    self.heap[curr_pos]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    get_child_left_position(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    get_child_right_position(_A    )
							if child_left_position < self.elements and child_right_position < self.elements:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    self.heap[child_left_position]
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.heap[child_right_position]
									if child_right_weight < child_left_weight and child_right_weight < weight:
											self._swap_nodes(_A,_A    )
											return self._bubble_down(_A    )
							if child_left_position < self.elements:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    self.heap[child_left_position]
									if child_left_weight < weight:
											self._swap_nodes(_A,_A    )
											return self._bubble_down(_A    )
							else:
									return None
							if child_right_position < self.elements:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.heap[child_right_position]
									if child_right_weight < weight:
											self._swap_nodes(_A,_A    )
											return self._bubble_down(_A    )
							return None






					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       int,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.heap[nodea_pos][0]
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.heap[nodea_pos][0]
							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    (
							    self.heap[nodea_pos],
							    self.heap[nodea_pos],
							)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    nodea_pos
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    nodea_pos







class 		a__						(					Generic[T]					):

					def __init__(    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		dict[T, dict[T, int]]				      =    {}
							SCREAMING_SNAKE_CASE_  :		int				      =    0

					def __repr__(    self      :       Optional[Any]    ):







							"""simple docstring"""






							return str(self.connections    )

					def __len__(    self      :       str    ):







							"""simple docstring"""






							return self.nodes

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       T    ):







							"""simple docstring"""






							if node not in self.connections:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {}
									self.nodes += 1






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       T,_A      :       T,_A      :       int    ):







							"""simple docstring"""






							self.add_node(_A    )
							self.add_node(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    weight
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    weight







def       _snake_case	(   lowerCAmelCase							:   GraphUndirectedWeighted[T]   , ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		dict[T, int]				      =    {node: maxsize for node in graph.connections}
		SCREAMING_SNAKE_CASE_  :		dict[T, T | None]				      =    {node: None for node in graph.connections}

		SCREAMING_SNAKE_CASE_  :		MinPriorityQueue[T]				      =    MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(lowerCAmelCase   , lowerCAmelCase	)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    priority_queue.extract_min()
		SCREAMING_SNAKE_CASE_  :		Dict				      =    0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						SCREAMING_SNAKE_CASE_  :		int				      =    dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(lowerCAmelCase   , dist[neighbour]	)
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    node

    # running prim's algorithm
		while not priority_queue.is_empty():
				SCREAMING_SNAKE_CASE_  :		Any				      =    priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								SCREAMING_SNAKE_CASE_  :		Dict				      =    dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(lowerCAmelCase   , dist[neighbour]	)
								SCREAMING_SNAKE_CASE_  :		int				      =    node
		return dist, parent

import argparse
from pathlib import Path

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




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , ):
		"""simple docstring"""







		if config_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    generator_name_or_path

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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

		# Save model.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    RagConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    AutoConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		int				      =    AutoConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    gen_config
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    question_encoder_config

		SCREAMING_SNAKE_CASE_  :		Dict				      =    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.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[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``'''
		    ),
		)

		__lowerCamelCase					:	str       =  parser.parse_args()

		__lowerCamelCase					:	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,
		)
