
"""simple docstring"""




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


snake_case					  =						logging.getLogger(__name__)




class 	UpperCamelCase      :

				"""simple docstring"""



				def __init__(							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      False


				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       str:
									"""simple docstring"""







									if not self.initialized:
														SCREAMING_SNAKE_CASE           =      RagRetriever(
														    lowercase__						,      question_encoder_tokenizer=lowercase__						,      generator_tokenizer=lowercase__						,      index=lowercase__						,      init_retrieval=lowercase__						,      )
														SCREAMING_SNAKE_CASE           =      True


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									self.retriever.index.init_index()


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									del self.retriever
									SCREAMING_SNAKE_CASE           =      False




				def 				A   (							self						,      lowercase__						,      lowercase__     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.retriever._main_retrieve(lowercase__						,      lowercase__     )
									SCREAMING_SNAKE_CASE           =      self.retriever.index.get_doc_dicts(lowercase__     )
									return doc_ids, retrieved_doc_embeds, doc_dicts






class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__=None     )       ->       Tuple:
									"""simple docstring"""







									if index is not None and index.is_initialized() and len(lowercase__     ) > 0:
														raise ValueError(
														    'When using Ray for distributed fine-tuning, '
														    'you\'ll need to provide the paths instead, '
														    'as the dataset and the index are loaded '
														    'separately. More info in examples/rag/use_own_knowledge_dataset.py '     )

									super().__init__(
									    lowercase__						,      question_encoder_tokenizer=lowercase__						,      generator_tokenizer=lowercase__						,      index=lowercase__						,      init_retrieval=lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      retrieval_workers
									SCREAMING_SNAKE_CASE           =      question_encoder_tokenizer
									SCREAMING_SNAKE_CASE           =      generator_tokenizer
									if len(self.retrieval_workers     ) > 0:
														ray.get(
														    [
														        worker.create_rag_retriever.remote(lowercase__						,      lowercase__						,      lowercase__						,      lowercase__     )
														        for worker in self.retrieval_workers
														    ]     )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									logger.info('initializing retrieval'     )

									if len(self.retrieval_workers     ) > 0:
														ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
									else:
														# Non-distributed training. Load index into this same process.
														self.index.init_index()


				def 				A   (							self						,      lowercase__						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									if len(self.retrieval_workers     ) > 0:
														# Select a random retrieval actor.
														SCREAMING_SNAKE_CASE           =      self.retrieval_workers[random.randint(0						,      len(self.retrieval_workers     ) - 1     )]
														SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      ray.get(
														    random_worker.retrieve.remote(lowercase__						,      lowercase__     )     )
									else:
														SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self._main_retrieve(lowercase__						,      lowercase__     )
														SCREAMING_SNAKE_CASE           =      self.index.get_doc_dicts(lowercase__     )
									return retrieved_doc_embeds, doc_ids, doc_dicts


				@classmethod
				def 				A   (							cls						,      lowercase__						,      lowercase__=None						,      **lowercase__     )       ->       List[str]:
									"""simple docstring"""







									return super(lowercase__						,      cls     ).get_tokenizers(lowercase__						,      lowercase__						,      **lowercase__     )


				@classmethod
				def 				A   (							cls						,      lowercase__						,      lowercase__						,      lowercase__=None						,      **lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      kwargs.pop('config'						,      lowercase__     ) or RagConfig.from_pretrained(lowercase__						,      **lowercase__     )
									SCREAMING_SNAKE_CASE           =      RagTokenizer.from_pretrained(lowercase__						,      config=lowercase__     )
									SCREAMING_SNAKE_CASE           =      rag_tokenizer.question_encoder
									SCREAMING_SNAKE_CASE           =      rag_tokenizer.generator

									if indexed_dataset is not None:
														SCREAMING_SNAKE_CASE           =      'custom'
														SCREAMING_SNAKE_CASE           =      CustomHFIndex(config.retrieval_vector_size						,      lowercase__     )
									else:
														SCREAMING_SNAKE_CASE           =      cls._build_index(lowercase__     )

									return cls(
									    lowercase__						,      question_encoder_tokenizer=lowercase__						,      generator_tokenizer=lowercase__						,      retrieval_workers=lowercase__						,      index=lowercase__						,      )




				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									logger.info('re-loading the new dataset with embeddings'     )
									# access from the training loop

									ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

									# build the index object again
									SCREAMING_SNAKE_CASE           =      self._build_index(self.config     )

									ray.get(
									    [
									        worker.create_rag_retriever.remote(
									            self.config						,      self.question_encoder_tokenizer						,      self.generator_tokenizer						,      lowercase__     )
									        for worker in self.retrieval_workers
									    ]     )


"""simple docstring"""




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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = np.pi / 2,	SCREAMING_SNAKE_CASE_ = np.pi / 2,	SCREAMING_SNAKE_CASE_ = np.pi / 2     ):
					SCREAMING_SNAKE_CASE           =      QuantumRegister(3,	'qr'     )  # Define the number of quantum bits
					SCREAMING_SNAKE_CASE           =      ClassicalRegister(1,	'cr'     )  # Define the number of classical bits

					SCREAMING_SNAKE_CASE           =      QuantumCircuit(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )  # Define the quantum circuit.

					# Build the circuit
					quantum_circuit.u(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	0     )  # Quantum State to teleport
					quantum_circuit.h(1     )  # add hadamard gate
					quantum_circuit.cx(
					    1,	2     )  # add control gate with qubit 1 as control and 2 as target.
					quantum_circuit.cx(0,	1     )
					quantum_circuit.h(0     )
					quantum_circuit.cz(0,	2     )  # add control z gate.
					quantum_circuit.cx(1,	2     )

					quantum_circuit.measure([2],	[0]     )  # measure the qubit.

					# Simulate the circuit using qasm simulator
					SCREAMING_SNAKE_CASE           =      Aer.get_backend('aer_simulator'     )
					SCREAMING_SNAKE_CASE           =      execute(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	shots=1_0_0_0     )

					return job.result().get_counts(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
	print(
	    'Total count for teleported state is: '
	    f'{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'
	)
