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





def     UpperCAmelCase						(   lowercase = np.pi / 2							,      lowercase = np.pi / 2							,      lowercase = np.pi / 2							):







				"""simple docstring"""



				__lowercase			      =							QuantumRegister(3							,      '''qr'''							)  # Define the number of quantum bits
				__lowercase			      =							ClassicalRegister(1							,      '''cr'''							)  # Define the number of classical bits

				__lowercase			      =							QuantumCircuit(lowercase							,      lowercase							)  # Define the quantum circuit.

				# Build the circuit
				quantum_circuit.u(lowercase							,      lowercase							,      lowercase							,      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
				__lowercase			      =							Aer.get_backend('''aer_simulator'''							)
				__lowercase			      =							execute(lowercase							,      lowercase							,      shots=1000							)

				return job.result().get_counts(lowercase							)


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

import ray

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


__a  :					Optional[Any]          =  logging.getLogger(__name__)




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							self.retriever.index.init_index()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							del self.retriever
							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.retriever._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.retriever.index.get_doc_dicts(lowerCAmelCase__						)
							return doc_ids, retrieved_doc_embeds, doc_dicts




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if index is not None and index.is_initialized() and len(lowerCAmelCase__						) > 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__(
							    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       index=lowerCAmelCase__				,       init_retrieval=lowerCAmelCase__				,       )

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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


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





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


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





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


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

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

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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> 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
							__lowercase			      =							self._build_index(self.config						)

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