


'''simple docstring'''







import copy

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


lowerCAmelCase    :     List[str]					=						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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "rag"
     __magic_name__    			=    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__      ,	):

            '''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"
            _lowerCAmelCase    :							Dict   			=       kwargs.pop('question_encoder'   )
            _lowerCAmelCase    :							int   			=       question_encoder_config.pop('model_type'   )
            _lowerCAmelCase    :							str   			=       kwargs.pop('generator'   )
            _lowerCAmelCase    :							Tuple   			=       decoder_config.pop('model_type'   )

            from ..auto.configuration_auto import AutoConfig

            _lowerCAmelCase    :							Any   			=       AutoConfig.for_model(snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       AutoConfig.for_model(snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       reduce_loss
            _lowerCAmelCase    :							Optional[int]   			=       label_smoothing
            _lowerCAmelCase    :							str   			=       exclude_bos_score
            _lowerCAmelCase    :							int   			=       do_marginalize

            _lowerCAmelCase    :							Union[str, Any]   			=       title_sep
            _lowerCAmelCase    :							Union[str, Any]   			=       doc_sep
            _lowerCAmelCase    :							Dict   			=       n_docs
            _lowerCAmelCase    :							List[str]   			=       max_combined_length

            _lowerCAmelCase    :							Dict   			=       dataset
            _lowerCAmelCase    :							Dict   			=       dataset_split
            _lowerCAmelCase    :							Tuple   			=       index_name

            _lowerCAmelCase    :							List[Any]   			=       retrieval_vector_size
            _lowerCAmelCase    :							Dict   			=       retrieval_batch_size
            _lowerCAmelCase    :							Dict   			=       passages_path
            _lowerCAmelCase    :							str   			=       index_path
            _lowerCAmelCase    :							Optional[int]   			=       use_dummy_dataset

            _lowerCAmelCase    :							Dict   			=       output_retrieved

            _lowerCAmelCase    :							str   			=       do_deduplication

            _lowerCAmelCase    :							Tuple   			=       use_cache

            if self.forced_eos_token_id is None:
                   _lowerCAmelCase    :							str   			=       getattr(self.generator      ,	'forced_eos_token_id'      ,	snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       copy.deepcopy(self.__dict__   )
            _lowerCAmelCase    :							int   			=       self.question_encoder.to_dict()
            _lowerCAmelCase    :							List[str]   			=       self.generator.to_dict()
            _lowerCAmelCase    :							str   			=       self.__class__.model_type
            return output





'''simple docstring'''







import socket






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       socket.socket(socket.AF_INET   ,						socket.SOCK_STREAM					)
       _lowerCAmelCase    :							Optional[int]   			=       socket.gethostname()
       _lowerCAmelCase    :							Any   			=       1_2_3_1_2

       sock.connect((host, port)					)
       sock.send(B'Hello server!'					)

       with open('Received_file'   ,						'wb'					) as out_file:
              print('File opened'					)
              print('Receiving data...'					)
              while True:
                     _lowerCAmelCase    :							Union[str, Any]   			=       sock.recv(1_0_2_4					)
                     if not data:
                            break
                     out_file.write(_A					)

       print('Successfully received the file'					)
       sock.close()
       print('Connection closed'					)


if __name__ == "__main__":
       main()

