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







		SCREAMING_SNAKE_CASE_  :		Dict				      =    [0 for i in range(len(lowerCAmelCase	)	)]

		# initialize interval's left pointer and right pointer
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    0, 0

		for i in range(1   , len(lowerCAmelCase	)	):
				# case when current index is inside the interval
				if i <= right_pointer:
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    min(right_pointer - i + 1   , z_result[i - left_pointer]	)
						SCREAMING_SNAKE_CASE_  :		str				      =    min_edge

				while go_next(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	):
						z_result[i] += 1

				# if new index's result gives us more right interval,
				# we've to update left_pointer and right_pointer
				if i + z_result[i] - 1 > right_pointer:
						SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    i, i + z_result[i] - 1

		return z_result




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   list[int]   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		return i + z_result[i] < len(lowerCAmelCase	) and s[z_result[i]] == s[i + z_result[i]]




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    0
		# concatenate 'pattern' and 'input_str' and call z_function
		# with concatenated string
		SCREAMING_SNAKE_CASE_  :		str				      =    z_function(pattern + input_str	)

		for val in z_result:
				# if value is greater then length of the pattern string
				# that means this index is starting position of substring
				# which is equal to pattern string
				if val >= len(lowerCAmelCase	):
						answer += 1

		return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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,
		)
