
import copy

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


SCREAMING_SNAKE_CASE							:Dict							 =				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(__a  )
class 							UpperCAmelCase      (  __a  ):




	'''simple docstring'''





	snake_case_					    =     "rag"
	snake_case_					    =     True






	def __init__(       self					: List[Any]							,A					: List[Any]=None							,A					: int=True							,A					: Optional[int]=None							,A					: Dict=None							,A					: Any=None							,A					: Any=None							,A					: str=None							,A					: Dict=" / "							,A					: Optional[int]=" // "							,A					: Optional[int]=5							,A					: List[str]=3_00							,A					: Dict=7_68							,A					: Dict=8							,A					: Union[str, Any]="wiki_dpr"							,A					: List[str]="train"							,A					: Optional[Any]="compressed"							,A					: Optional[Any]=None							,A					: Tuple=None							,A					: Optional[int]=False							,A					: List[str]=False							,A					: Optional[Any]=0.0							,A					: Optional[int]=True							,A					: List[str]=False							,A					: Dict=False							,A					: Dict=False							,A					: Union[str, Any]=True							,A					: Tuple=None							,**A					: int							,):
					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					: str							,A					: PretrainedConfig							,A					: PretrainedConfig							,**A					: Dict ):
					return cls(question_encoder=question_encoder_config.to_dict()							,generator=generator_config.to_dict()							,**_A )







	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= copy.deepcopy(self.__dict__ )
					__A   			= self.question_encoder.to_dict()
					__A   			= self.generator.to_dict()
					__A   			= self.__class__.model_type
					return output




'''simple docstring'''
import pprint

import requests

UpperCamelCase__    					=      '''https://zenquotes.io/api'''





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/today'''						).json()





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/random'''						).json()


if __name__ == "__main__":
  UpperCamelCase__    					=      random_quotes()
  pprint.pprint(response)
