



"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__snake_case							:   List[Any]      =    R'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'



@add_start_docstrings(__SCREAMING_SNAKE_CASE							)
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'rag'
	SCREAMING_SNAKE_CASE          =	True

	def __init__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=None       ,			_SCREAMING_SNAKE_CASE:		Tuple=True       ,			_SCREAMING_SNAKE_CASE:		List[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Tuple=None       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			_SCREAMING_SNAKE_CASE:		Any=None       ,			_SCREAMING_SNAKE_CASE:		List[Any]=" / "       ,			_SCREAMING_SNAKE_CASE:		List[Any]=" // "       ,			_SCREAMING_SNAKE_CASE:		int=5       ,			_SCREAMING_SNAKE_CASE:		List[Any]=300       ,			_SCREAMING_SNAKE_CASE:		str=768       ,			_SCREAMING_SNAKE_CASE:		List[str]=8       ,			_SCREAMING_SNAKE_CASE:		Tuple="wiki_dpr"       ,			_SCREAMING_SNAKE_CASE:		Any="train"       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="compressed"       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			_SCREAMING_SNAKE_CASE:		List[str]=None       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		Tuple=True       ,			_SCREAMING_SNAKE_CASE:		Any=False       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			_SCREAMING_SNAKE_CASE:		Any=False       ,			_SCREAMING_SNAKE_CASE:		Dict=True       ,			_SCREAMING_SNAKE_CASE:		Tuple=None       ,			**_SCREAMING_SNAKE_CASE:		Dict       ,			)				->       Optional[Any]:

				"""simple docstring"""

				super().__init__(
				    bos_token_id=_SCREAMING_SNAKE_CASE       ,			pad_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE       ,			decoder_start_token_id=_SCREAMING_SNAKE_CASE       ,			forced_eos_token_id=_SCREAMING_SNAKE_CASE       ,			is_encoder_decoder=_SCREAMING_SNAKE_CASE       ,			prefix=_SCREAMING_SNAKE_CASE       ,			vocab_size=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)
				assert (
				    "question_encoder" in kwargs and "generator" in kwargs
				), "Config has to be initialized with question_encoder and generator config"
				__lowerCAmelCase      :  int												=       kwargs.pop("question_encoder")
				__lowerCAmelCase      :  Any												=       question_encoder_config.pop("model_type")
				__lowerCAmelCase      :  Optional[Any]												=       kwargs.pop("generator")
				__lowerCAmelCase      :  List[Any]												=       decoder_config.pop("model_type")

				from ..auto.configuration_auto import AutoConfig

				__lowerCAmelCase      :  Union[str, Any]												=       AutoConfig.for_model(_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       AutoConfig.for_model(_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       reduce_loss
				__lowerCAmelCase      :  int												=       label_smoothing
				__lowerCAmelCase      :  Union[str, Any]												=       exclude_bos_score
				__lowerCAmelCase      :  List[str]												=       do_marginalize

				__lowerCAmelCase      :  Optional[int]												=       title_sep
				__lowerCAmelCase      :  Tuple												=       doc_sep
				__lowerCAmelCase      :  Optional[Any]												=       n_docs
				__lowerCAmelCase      :  List[str]												=       max_combined_length

				__lowerCAmelCase      :  Optional[int]												=       dataset
				__lowerCAmelCase      :  str												=       dataset_split
				__lowerCAmelCase      :  str												=       index_name

				__lowerCAmelCase      :  str												=       retrieval_vector_size
				__lowerCAmelCase      :  Union[str, Any]												=       retrieval_batch_size
				__lowerCAmelCase      :  Optional[Any]												=       passages_path
				__lowerCAmelCase      :  List[str]												=       index_path
				__lowerCAmelCase      :  str												=       use_dummy_dataset

				__lowerCAmelCase      :  Optional[int]												=       output_retrieved

				__lowerCAmelCase      :  int												=       do_deduplication

				__lowerCAmelCase      :  List[str]												=       use_cache

				if self.forced_eos_token_id is None:
							__lowerCAmelCase      :  Optional[Any]												=       getattr(self.generator       ,			"forced_eos_token_id"       ,			_SCREAMING_SNAKE_CASE)

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		List[str]       ,			_SCREAMING_SNAKE_CASE:		PretrainedConfig       ,			_SCREAMING_SNAKE_CASE:		PretrainedConfig       ,			**_SCREAMING_SNAKE_CASE:		Tuple)				->       PretrainedConfig:

				"""simple docstring"""

				return cls(question_encoder=question_encoder_config.to_dict()       ,			generator=generator_config.to_dict()       ,			**_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       copy.deepcopy(self.__dict__)
				__lowerCAmelCase      :  str												=       self.question_encoder.to_dict()
				__lowerCAmelCase      :  Optional[Any]												=       self.generator.to_dict()
				__lowerCAmelCase      :  Any												=       self.__class__.model_type
				return output




"""simple docstring"""






from __future__ import annotations


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   list:
			__lowerCAmelCase      :  Dict												=       []
			__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       input_list[low:mid], input_list[mid : high + 1]
			while left and right:
						result.append((left if left[0] <= right[0] else right).pop(0	)	)
			__lowerCAmelCase      :  int												=       result + left + right
			return input_list


def 			_lowercase				(  __snake_case	)						->   list:
			if len(__snake_case	) <= 1:
						return input_list
			__lowerCAmelCase      :  int												=       list(__snake_case	)

			# iteration for two-way merging
			__lowerCAmelCase      :  Optional[int]												=       2
			while p <= len(__snake_case	):
						# getting low, high and middle value for merge-sort of single list
						for i in range(0					,len(__snake_case	)					,__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       i
									__lowerCAmelCase      :  Tuple												=       i + p - 1
									__lowerCAmelCase      :  Optional[Any]												=       (low + high + 1) // 2
									__lowerCAmelCase      :  Any												=       merge(__snake_case					,__snake_case					,__snake_case					,__snake_case	)
						# final merge of last two parts
						if p * 2 >= len(__snake_case	):
									__lowerCAmelCase      :  Optional[Any]												=       i
									__lowerCAmelCase      :  Union[str, Any]												=       merge(__snake_case					,0					,__snake_case					,len(__snake_case	) - 1	)
									break
						p *= 2

			return input_list


if __name__ == "__main__":
	__snake_case							:   Union[str, Any]      =    input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		__snake_case							:   Optional[int]      =    []
	else:
		__snake_case							:   int      =    [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))