





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


SCREAMING_SNAKE_CASE_   :      Any			=   logging.getLogger(__name__)

class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     str   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     False




	def    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     int   )       -> Dict:

			'''simple docstring'''




			if not self.initialized:
					__lowercase											=     RagRetriever(
					    __lowerCamelCase					,      question_encoder_tokenizer=__lowerCamelCase					,      generator_tokenizer=__lowerCamelCase					,      index=__lowerCamelCase					,      init_retrieval=__lowerCamelCase					,      )
					__lowercase											=     True




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Dict:

			'''simple docstring'''




			self.retriever.index.init_index()






	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     self.retriever._main_retrieve(__lowerCamelCase					,      __lowerCamelCase   )
			return doc_ids, retrieved_doc_embeds





class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Any					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     Optional[Any]					,      __lowerCamelCase							:     Optional[int]=None   )       -> Any:

			'''simple docstring'''




			if index is not None and index.is_initialized() and len(__lowerCamelCase   ) > 0:
					raise ValueError(
					    'When using Ray for distributed fine-tuning, '
					    'you\'ll need to provide the paths instead, '
					    'as the dataset and the index are loaded '
					    'separately. More info in examples/rag/use_own_knowledge_dataset.py '   )
			super().__init__(
			    __lowerCamelCase					,      question_encoder_tokenizer=__lowerCamelCase					,      generator_tokenizer=__lowerCamelCase					,      index=__lowerCamelCase					,      init_retrieval=__lowerCamelCase					,      )
			__lowercase											=     retrieval_workers
			if len(self.retrieval_workers   ) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase   )
					        for worker in self.retrieval_workers
					    ]   )




	def    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			logger.info('initializing retrieval'   )

			if len(self.retrieval_workers   ) > 0:
					ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]   )
			else:
					# Non-distributed training. Load index into this same process.
					self.index.init_index()




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     Tuple					,      __lowerCamelCase							:     Any   )       -> int:

			'''simple docstring'''




			if len(self.retrieval_workers   ) > 0:
					# Select a random retrieval actor.
					__lowercase											=     self.retrieval_workers[random.randint(0					,      len(self.retrieval_workers   ) - 1   )]
					__lowercase						,      __lowercase											=     ray.get(random_worker.retrieve.remote(__lowerCamelCase					,      __lowerCamelCase   )   )
			else:
					__lowercase						,      __lowercase											=     self._main_retrieve(__lowerCamelCase					,      __lowerCamelCase   )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase   )




	@classmethod
	def    UpperCAmelCase			(						cls							:     Optional[Any]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     str   )       -> str:

			'''simple docstring'''




			return super(__lowerCamelCase					,      cls   ).get_tokenizers(__lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase   )






	@classmethod
	def    UpperCAmelCase			(						cls							:     List[str]					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[int]=None					,      **__lowerCamelCase							:     str   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     kwargs.pop('config'					,      __lowerCamelCase   ) or RagConfig.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase   )
			__lowercase											=     RagTokenizer.from_pretrained(__lowerCamelCase					,      config=__lowerCamelCase   )
			__lowercase											=     rag_tokenizer.question_encoder
			__lowercase											=     rag_tokenizer.generator
			if indexed_dataset is not None:
					__lowercase											=     'custom'
					__lowercase											=     CustomHFIndex(config.retrieval_vector_size					,      __lowerCamelCase   )
			else:
					__lowercase											=     cls._build_index(__lowerCamelCase   )
			return cls(
			    __lowerCamelCase					,      question_encoder_tokenizer=__lowerCamelCase					,      generator_tokenizer=__lowerCamelCase					,      retrieval_workers=__lowerCamelCase					,      index=__lowerCamelCase					,      )











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




