


import copy

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


_lowercase							:   str						=						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    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'rag'
				lowerCAmelCase					=			True

				def __init__(    self					, a__=None					, a__=True					, a__=None					, a__=None					, a__=None					, a__=None					, a__=None					, a__=" / "					, a__=" // "					, a__=5					, a__=300					, a__=768					, a__=8					, a__="wiki_dpr"					, a__="train"					, a__="compressed"					, a__=None					, a__=None					, a__=False					, a__=False					, a__=0.0					, a__=True					, a__=False					, a__=False					, a__=False					, a__=True					, a__=None					, **a__					, )						->					Optional[Any]:
					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"
					A             =					kwargs.pop("""question_encoder""" )
					A             =					question_encoder_config.pop("""model_type""" )
					A             =					kwargs.pop("""generator""" )
					A             =					decoder_config.pop("""model_type""" )

					from ..auto.configuration_auto import AutoConfig

					A             =					AutoConfig.for_model(a__					, **a__ )
					A             =					AutoConfig.for_model(a__					, **a__ )

					A             =					reduce_loss
					A             =					label_smoothing
					A             =					exclude_bos_score
					A             =					do_marginalize

					A             =					title_sep
					A             =					doc_sep
					A             =					n_docs
					A             =					max_combined_length

					A             =					dataset
					A             =					dataset_split
					A             =					index_name

					A             =					retrieval_vector_size
					A             =					retrieval_batch_size
					A             =					passages_path
					A             =					index_path
					A             =					use_dummy_dataset

					A             =					output_retrieved

					A             =					do_deduplication

					A             =					use_cache

					if self.forced_eos_token_id is None:
						A             =					getattr(self.generator					, """forced_eos_token_id"""					, a__ )

				@classmethod
				def   _UpperCAmelCase       (    cls					, a__					, a__					, **a__ )						->					PretrainedConfig:
					return cls(question_encoder=question_encoder_config.to_dict()					, generator=generator_config.to_dict()					, **a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					copy.deepcopy(self.__dict__ )
					A             =					self.question_encoder.to_dict()
					A             =					self.generator.to_dict()
					A             =					self.__class__.model_type
					return output







import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase					( UpperCamelCase__:  str = "https://www.worldometers.info/coronavirus"    )			-> dict:





	"""simple docstring"""







	A             =					BeautifulSoup(requests.get(UpperCamelCase__    ).text				,       """html.parser"""    )
	A             =					soup.findAll("""h1"""    )
	A             =					soup.findAll("""div"""				,       {"""class""": """maincounter-number"""}    )
	keys += soup.findAll("""span"""				,       {"""class""": """panel-title"""}    )
	values += soup.findAll("""div"""				,       {"""class""": """number-table-main"""}    )
	return {key.text.strip(): value.text.strip() for key, value in zip(UpperCamelCase__				,       UpperCamelCase__    )}


if __name__ == "__main__":
				print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
				for key, value in world_covidaa_stats().items():
								print(f'''{key}\n{value}\n''')



