'''simple docstring'''




import copy

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


__UpperCAmelCase     							=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(snake_case_ )
class     a__      (   snake_case_ ):
     lowerCamelCase  :     Optional[int]							="rag"
     lowerCamelCase  :     Optional[Any]							=True






     def __init__( self		:		Dict  ,				a		:		Tuple=None  ,				a		:		str=True  ,				a		:		List[str]=None  ,				a		:		Union[str, Any]=None  ,				a		:		Tuple=None  ,				a		:		Optional[Any]=None  ,				a		:		int=None  ,				a		:		Union[str, Any]=" / "  ,				a		:		Optional[Any]=" // "  ,				a		:		int=5  ,				a		:		Optional[int]=3_00  ,				a		:		Dict=7_68  ,				a		:		Optional[int]=8  ,				a		:		List[Any]="wiki_dpr"  ,				a		:		Optional[Any]="train"  ,				a		:		Dict="compressed"  ,				a		:		Any=None  ,				a		:		Optional[int]=None  ,				a		:		Tuple=False  ,				a		:		Tuple=False  ,				a		:		Dict=0.0  ,				a		:		Optional[Any]=True  ,				a		:		List[str]=False  ,				a		:		Any=False  ,				a		:		List[str]=False  ,				a		:		Dict=True  ,				a		:		int=None  ,				**a		:		Optional[Any]  ,				):





         """simple docstring"""



         super().__init__(
             bos_token_id=a  ,				pad_token_id=a  ,				eos_token_id=a  ,				decoder_start_token_id=a  ,				forced_eos_token_id=a  ,				is_encoder_decoder=a  ,				prefix=a  ,				vocab_size=a  ,				**a  ,				)
         assert (
             "question_encoder" in kwargs and "generator" in kwargs
         ), "Config has to be initialized with question_encoder and generator config"
         __lowerCamelCase		=  kwargs.pop('''question_encoder'''    )
         __lowerCamelCase		=  question_encoder_config.pop('''model_type'''    )
         __lowerCamelCase		=  kwargs.pop('''generator'''    )
         __lowerCamelCase		=  decoder_config.pop('''model_type'''    )

         from ..auto.configuration_auto import AutoConfig

         __lowerCamelCase		=  AutoConfig.for_model(a  ,				**a    )
         __lowerCamelCase		=  AutoConfig.for_model(a  ,				**a    )

         __lowerCamelCase		=  reduce_loss
         __lowerCamelCase		=  label_smoothing
         __lowerCamelCase		=  exclude_bos_score
         __lowerCamelCase		=  do_marginalize

         __lowerCamelCase		=  title_sep
         __lowerCamelCase		=  doc_sep
         __lowerCamelCase		=  n_docs
         __lowerCamelCase		=  max_combined_length

         __lowerCamelCase		=  dataset
         __lowerCamelCase		=  dataset_split
         __lowerCamelCase		=  index_name

         __lowerCamelCase		=  retrieval_vector_size
         __lowerCamelCase		=  retrieval_batch_size
         __lowerCamelCase		=  passages_path
         __lowerCamelCase		=  index_path
         __lowerCamelCase		=  use_dummy_dataset

         __lowerCamelCase		=  output_retrieved

         __lowerCamelCase		=  do_deduplication

         __lowerCamelCase		=  use_cache

         if self.forced_eos_token_id is None:
             __lowerCamelCase		=  getattr(self.generator  ,				'''forced_eos_token_id'''  ,				a    )






     @classmethod
     def    SCREAMING_SNAKE_CASE__   ( cls		:		Tuple  ,				a		:		Optional[Any]  ,				a		:		Optional[int]  ,				**a		:		List[Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  copy.deepcopy(self.__dict__    )
         __lowerCamelCase		=  self.question_encoder.to_dict()
         __lowerCamelCase		=  self.generator.to_dict()
         __lowerCamelCase		=  self.__class__.model_type
         return output


import math





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				if 0 not in (x, y):
								# We use the relation x^y = y*log10(x), where 10 is the base.
								return y * math.logaa(lowerCamelCase__    )
				else:
								if x == 0:  # 0 raised to any number is 0
												return 0
								elif y == 0:
												return 1  # any number raised to 0 is 1
				raise AssertionError("This should never happen"    )


if __name__ == "__main__":  # Main function
			# Read two numbers from input and typecast them to int using map function.
			# Here x is the base and y is the power.
			__A				='''Enter the base and the power separated by a comma: '''
			__A,						__A				=map(int, input(prompt).split(''','''))
			__A,						__A				=map(int, input(prompt).split(''','''))

			# We find the log of each number, using the function res(), which takes two
			# arguments.
			__A				=res(xa, ya)
			__A				=res(xa, ya)

			# We check for the largest number
			if resa > resa:
						print('''Largest number is''', xa, '''^''', ya)
			elif resa > resa:
						print('''Largest number is''', xa, '''^''', ya)
			else:
						print('''Both are equal''')


