

"""simple docstring"""


import copy

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


snake_case_    					=	R"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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







@add_start_docstrings(SCREAMING_SNAKE_CASE_    )
class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""rag"""
						__UpperCamelCase		    =							True






						def __init__( self			:Optional[int]       ,  lowercase_			:Optional[Any]=None       ,  lowercase_			:List[str]=True       ,  lowercase_			:Tuple=None       ,  lowercase_			:int=None       ,  lowercase_			:Optional[Any]=None       ,  lowercase_			:str=None       ,  lowercase_			:Dict=None       ,  lowercase_			:List[str]=" / "       ,  lowercase_			:Dict=" // "       ,  lowercase_			:str=5       ,  lowercase_			:Optional[int]=3_00       ,  lowercase_			:Dict=7_68       ,  lowercase_			:str=8       ,  lowercase_			:List[str]="wiki_dpr"       ,  lowercase_			:Optional[Any]="train"       ,  lowercase_			:List[str]="compressed"       ,  lowercase_			:str=None       ,  lowercase_			:Optional[Any]=None       ,  lowercase_			:List[str]=False       ,  lowercase_			:List[str]=False       ,  lowercase_			:List[str]=0.0       ,  lowercase_			:Optional[Any]=True       ,  lowercase_			:List[str]=False       ,  lowercase_			:Union[str, Any]=False       ,  lowercase_			:int=False       ,  lowercase_			:List[Any]=True       ,  lowercase_			:Optional[Any]=None       ,  **lowercase_			:int       ,  )    ->      str:
												super().__init__(
												    bos_token_id=lowercase_       ,  pad_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  decoder_start_token_id=lowercase_       ,  forced_eos_token_id=lowercase_       ,  is_encoder_decoder=lowercase_       ,  prefix=lowercase_       ,  vocab_size=lowercase_       ,  **lowercase_       ,  )
												assert (
												    "question_encoder" in kwargs and "generator" in kwargs
												), "Config has to be initialized with question_encoder and generator config"
												UpperCAmelCase					=			kwargs.pop('question_encoder' )
												UpperCAmelCase					=			question_encoder_config.pop('model_type' )
												UpperCAmelCase					=			kwargs.pop('generator' )
												UpperCAmelCase					=			decoder_config.pop('model_type' )

												from ..auto.configuration_auto import AutoConfig

												UpperCAmelCase					=			AutoConfig.for_model(lowercase_       ,  **lowercase_ )
												UpperCAmelCase					=			AutoConfig.for_model(lowercase_       ,  **lowercase_ )

												UpperCAmelCase					=			reduce_loss
												UpperCAmelCase					=			label_smoothing
												UpperCAmelCase					=			exclude_bos_score
												UpperCAmelCase					=			do_marginalize

												UpperCAmelCase					=			title_sep
												UpperCAmelCase					=			doc_sep
												UpperCAmelCase					=			n_docs
												UpperCAmelCase					=			max_combined_length

												UpperCAmelCase					=			dataset
												UpperCAmelCase					=			dataset_split
												UpperCAmelCase					=			index_name

												UpperCAmelCase					=			retrieval_vector_size
												UpperCAmelCase					=			retrieval_batch_size
												UpperCAmelCase					=			passages_path
												UpperCAmelCase					=			index_path
												UpperCAmelCase					=			use_dummy_dataset

												UpperCAmelCase					=			output_retrieved

												UpperCAmelCase					=			do_deduplication

												UpperCAmelCase					=			use_cache

												if self.forced_eos_token_id is None:
																		UpperCAmelCase					=			getattr(self.generator       ,  'forced_eos_token_id'       ,  lowercase_ )






						@classmethod
						def      UpperCAmelCase__					( cls			:Tuple       ,  lowercase_			:PretrainedConfig       ,  lowercase_			:PretrainedConfig       ,  **lowercase_			:Union[str, Any] )    ->      PretrainedConfig:
												return cls(question_encoder=question_encoder_config.to_dict()       ,  generator=generator_config.to_dict()       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      List[Any]:
												UpperCAmelCase					=			copy.deepcopy(self.__dict__ )
												UpperCAmelCase					=			self.question_encoder.to_dict()
												UpperCAmelCase					=			self.generator.to_dict()
												UpperCAmelCase					=			self.__class__.model_type
												return output










"""simple docstring"""


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_    					=	3

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating primitive root of p'						)
						while True:
												UpperCAmelCase					=			random.randrange(3		,     lowercase_						)
												if pow(lowercase_		,     2		,     lowercase_						) == 1:
																		continue
												if pow(lowercase_		,     lowercase_		,     lowercase_						) == 1:
																		continue
												return g

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating prime p...'						)
						UpperCAmelCase					=			rabin_miller.generate_large_prime(lowercase_						)  # select large prime number.
						UpperCAmelCase					=			primitive_root(lowercase_						)  # one primitive root on modulo p.
						UpperCAmelCase					=			random.randrange(3		,     lowercase_						)  # private_key -> have to be greater than 2 for safety.
						UpperCAmelCase					=			cryptomath.find_mod_inverse(pow(lowercase_		,     lowercase_		,     lowercase_						)		,     lowercase_						)

						UpperCAmelCase					=			(key_size, e_a, e_a, p)
						UpperCAmelCase					=			(key_size, d)

						return public_key, private_key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						if os.path.exists(F"""{name}_pubkey.txt"""						) or os.path.exists(F"""{name}_privkey.txt"""						):
												print('\nWARNING:'						)
												print(
												    F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    'Use a different name or delete these files and re-run this program.'						)
												sys.exit()

						UpperCAmelCase				,   UpperCAmelCase					=			generate_key(lowercase_						)
						print(F"""\nWriting public key to file {name}_pubkey.txt..."""						)
						with open(F"""{name}_pubkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""						)

						print(F"""Writing private key to file {name}_privkey.txt..."""						)
						with open(F"""{name}_privkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{private_key[0]},{private_key[1]}"""						)

def 			_lowerCAmelCase				(       ):
						print('Making key files...'						)
						make_key_files('elgamal'		,     2048						)
						print('Key files generation successful'						)


if __name__ == "__main__":
				main()





