



import copy

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


A__  :  Union[str, Any]								=					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(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''rag'''
							_a			      = True




							def __init__(       self     :       str				,				A_     :       List[Any]=None				,				A_     :       str=True				,				A_     :       Tuple=None				,				A_     :       Union[str, Any]=None				,				A_     :       List[str]=None				,				A_     :       List[str]=None				,				A_     :       List[Any]=None				,				A_     :       Union[str, Any]=" / "				,				A_     :       Tuple=" // "				,				A_     :       Any=5				,				A_     :       Optional[Any]=3_0_0				,				A_     :       Tuple=7_6_8				,				A_     :       Union[str, Any]=8				,				A_     :       Dict="wiki_dpr"				,				A_     :       Optional[Any]="train"				,				A_     :       Dict="compressed"				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				A_     :       str=False				,				A_     :       Dict=False				,				A_     :       Dict=0.0				,				A_     :       List[str]=True				,				A_     :       List[str]=False				,				A_     :       List[Any]=False				,				A_     :       Any=False				,				A_     :       Optional[int]=True				,				A_     :       int=None				,				**A_     :       List[str]				,				):
									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"
									lowerCAmelCase_			:     List[str]									= kwargs.pop('''question_encoder''')
									lowerCAmelCase_			:     Tuple									= question_encoder_config.pop('''model_type''')
									lowerCAmelCase_			:     Tuple									= kwargs.pop('''generator''')
									lowerCAmelCase_			:     Dict									= decoder_config.pop('''model_type''')

									from ..auto.configuration_auto import AutoConfig

									lowerCAmelCase_			:     Union[str, Any]									= AutoConfig.for_model(A_				,				**A_)
									lowerCAmelCase_			:     int									= AutoConfig.for_model(A_				,				**A_)

									lowerCAmelCase_			:     List[Any]									= reduce_loss
									lowerCAmelCase_			:     Optional[Any]									= label_smoothing
									lowerCAmelCase_			:     Union[str, Any]									= exclude_bos_score
									lowerCAmelCase_			:     List[Any]									= do_marginalize

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

									lowerCAmelCase_			:     Union[str, Any]									= dataset
									lowerCAmelCase_			:     int									= dataset_split
									lowerCAmelCase_			:     Dict									= index_name

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

									lowerCAmelCase_			:     Tuple									= output_retrieved

									lowerCAmelCase_			:     List[Any]									= do_deduplication

									lowerCAmelCase_			:     Union[str, Any]									= use_cache

									if self.forced_eos_token_id is None:
											lowerCAmelCase_			:     List[Any]									= getattr(self.generator				,				'''forced_eos_token_id'''				,				A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       str				,				A_     :       PretrainedConfig				,				A_     :       PretrainedConfig				,				**A_     :       Any):
									return cls(question_encoder=question_encoder_config.to_dict()				,				generator=generator_config.to_dict()				,				**A_)







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Tuple									= self.question_encoder.to_dict()
									lowerCAmelCase_			:     Dict									= self.generator.to_dict()
									lowerCAmelCase_			:     str									= self.__class__.model_type
									return output








import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def 			UpperCAmelCase__				(			_A      : int = 3							):





  '''simple docstring'''







  if isinstance(_A	,      _A							):
    raise TypeError('''number of qubits must be a integer.'''							)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''							)
  if math.floor(_A							) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''							)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''							)

  a__  =QuantumRegister(_A	,      '''qr'''							)
  a__  =ClassicalRegister(_A	,      '''cr'''							)

  a__  =QuantumCircuit(_A	,      _A							)

  a__  =number_of_qubits

  for i in range(_A							):
    quantum_circuit.h(number_of_qubits - i - 1							)
    counter -= 1
    for j in range(_A							):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)	,      _A	,      _A							)

  for k in range(number_of_qubits // 2							):
    quantum_circuit.swap(_A	,      number_of_qubits - k - 1							)

  # measure all the qubits
  quantum_circuit.measure(_A	,      _A							)
  # simulate with 10000 shots
  a__  =Aer.get_backend('''qasm_simulator'''							)
  a__  =execute(_A	,      _A	,      shots=1_00_00							)

  return job.result().get_counts(_A							)


if __name__ == "__main__":
       print(
           f"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
       )
