
"""simple docstring"""






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCAmelCase       =  TypeVar('T')




class 			_SCREAMING_SNAKE_CASE					(			Generic[T]							):
			UpperCAmelCase_  :deque[T]  # Cache store of keys
			UpperCAmelCase_  :set[T]  # References of the keys in cache
			UpperCAmelCase_  :int				 =						10  # Maximum capacity of cache






			def __init__(					self       ,   __A       )			->     None:
						lowerCAmelCase_    :Tuple			     =  deque()
						lowerCAmelCase_    :Any			     =  set()
						if not n:
									lowerCAmelCase_    :str			     =  sys.maxsize
						elif n < 0:
									raise ValueError("""n should be an integer greater than 0."""       )
						else:
									lowerCAmelCase_    :Any			     =  n






			def  __lowerCAmelCase (					self       ,   __A       )			->     None:
						if x not in self.key_reference:
									if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
												lowerCAmelCase_    :int			     =  self.dq_store.pop()
												self.key_reference.remove(__A       )
						else:
									self.dq_store.remove(__A       )

						self.dq_store.appendleft(__A       )
						self.key_reference.add(__A       )






			def  __lowerCAmelCase (					self       )			->     None:
						for k in self.dq_store:
									print(__A       )






			def __repr__(					self       )			->     str:
						return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}"""


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__UpperCAmelCase       =  LRUCache(4)
				lru_cache.refer('A')
				lru_cache.refer(2)
				lru_cache.refer(3)
				lru_cache.refer('A')
				lru_cache.refer(4)
				lru_cache.refer(5)
				lru_cache.display()

				print(lru_cache)
				assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"







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,
		)
