
import copy

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


A     :	Optional[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(a			)
class 			__A( a			):
				snake_case_						   =    '''rag'''
				snake_case_						   =    True




				def __init__(     self		,     _snake_case=None		,     _snake_case=True		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=" / "		,     _snake_case=" // "		,     _snake_case=5		,     _snake_case=300		,     _snake_case=768		,     _snake_case=8		,     _snake_case="wiki_dpr"		,     _snake_case="train"		,     _snake_case="compressed"		,     _snake_case=None		,     _snake_case=None		,     _snake_case=False		,     _snake_case=False		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case=False		,     _snake_case=False		,     _snake_case=False		,     _snake_case=True		,     _snake_case=None		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(
								    bos_token_id=_snake_case		,     pad_token_id=_snake_case		,     eos_token_id=_snake_case		,     decoder_start_token_id=_snake_case		,     forced_eos_token_id=_snake_case		,     is_encoder_decoder=_snake_case		,     prefix=_snake_case		,     vocab_size=_snake_case		,     **_snake_case		,     )
								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(_snake_case		,     **_snake_case				)
								__a										=    AutoConfig.for_model(_snake_case		,     **_snake_case				)

								__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'''		,     _snake_case				)




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      PretrainedConfig:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




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




from math import factorial
def        lowercase_							(A : int		,					A : int		,					A : float      ):
       if successes > trials:
              raise ValueError('successes must be lower or equal to trials'      )
       if trials < 0 or successes < 0:
              raise ValueError('the function is defined for non-negative integers'      )
       if not isinstance(A		,					A      ) or not isinstance(A		,					A      ):
              raise ValueError('the function is defined for non-negative integers'      )
       if not 0 < prob < 1:
              raise ValueError('prob has to be in range of 1 - 0'      )
       snake_case__							:	List[Any]   	=	(prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case__							:	List[str]   	=	float(factorial(A      )      )
       coefficient /= factorial(A      ) * factorial(trials - successes      )
       return probability * coefficient


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print("Probability of 2 successes out of 4 trails")
 print("with probability of 0.75 is:", end=" ")
 print(binomial_distribution(2, 4, 0.75))






