
"""simple docstring"""




import copy

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


A__   :					List[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(SCREAMING_SNAKE_CASE__ )
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''rag'''
       UpperCamelCase_   =		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_ ,				)     ->	Tuple:





         """simple docstring"""




         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"
         _lowercase:			List[str]       =     kwargs.pop('''question_encoder'''       )
         _lowercase:			str       =     question_encoder_config.pop('''model_type'''       )
         _lowercase:			List[Any]       =     kwargs.pop('''generator'''       )
         _lowercase:			Optional[int]       =     decoder_config.pop('''model_type'''       )

         from ..auto.configuration_auto import AutoConfig

         _lowercase:			Any       =     AutoConfig.for_model(A_ ,				**A_       )
         _lowercase:			int       =     AutoConfig.for_model(A_ ,				**A_       )

         _lowercase:			List[Any]       =     reduce_loss
         _lowercase:			Optional[int]       =     label_smoothing
         _lowercase:			List[str]       =     exclude_bos_score
         _lowercase:			Tuple       =     do_marginalize

         _lowercase:			List[str]       =     title_sep
         _lowercase:			List[Any]       =     doc_sep
         _lowercase:			Any       =     n_docs
         _lowercase:			Optional[int]       =     max_combined_length

         _lowercase:			Optional[Any]       =     dataset
         _lowercase:			Union[str, Any]       =     dataset_split
         _lowercase:			List[str]       =     index_name

         _lowercase:			Optional[Any]       =     retrieval_vector_size
         _lowercase:			List[Any]       =     retrieval_batch_size
         _lowercase:			int       =     passages_path
         _lowercase:			Tuple       =     index_path
         _lowercase:			Union[str, Any]       =     use_dummy_dataset

         _lowercase:			Tuple       =     output_retrieved

         _lowercase:			Optional[int]       =     do_deduplication

         _lowercase:			Any       =     use_cache

         if self.forced_eos_token_id is None:
           _lowercase:			Optional[Any]       =     getattr(self.generator ,				'''forced_eos_token_id''' ,				A_       )
       @classmethod
       def 							lowercase_							(     cls ,				A_ ,				A_ ,				**A_       )     ->	PretrainedConfig:





         """simple docstring"""




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



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




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


"""simple docstring"""




import qiskit




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Dict       =     qiskit.Aer.get_backend('''aer_simulator'''						)

  # Create a Quantum Circuit acting on the q register
  _lowercase:			str       =     qiskit.QuantumCircuit(_UpperCamelCase ,				_UpperCamelCase						)

  # Apply X (NOT) Gate to Qubits 0 & 1
  circuit.x(0						)
  circuit.x(1						)

  # Map the quantum measurement to the classical bits
  circuit.measure([0, 1] ,				[0, 1]						)

  # Execute the circuit on the qasm simulator
  _lowercase:			Tuple       =     qiskit.execute(_UpperCamelCase ,				_UpperCamelCase ,				shots=1_000						)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(_UpperCamelCase						)


if __name__ == "__main__":
  A__   :					Optional[int]							=   single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")
