




"""simple docstring"""






import logging
import random

import ray

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


lowercase__			=		logging.getLogger(__name__)






class lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    ):
							_lowerCamelCase	:     List[str]       =       False
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    ):
							if not self.initialized:
											_lowerCamelCase	:     Union[str, Any]       =       RagRetriever(
											    lowercase    , question_encoder_tokenizer=lowercase    , generator_tokenizer=lowercase    , index=lowercase    , init_retrieval=lowercase    , )
											_lowerCamelCase	:     Optional[int]       =       True
			def    A_       (    self    ):
							self.retriever.index.init_index()






			def    A_       (    self    , lowercase    , lowercase    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       self.retriever._main_retrieve(lowercase    , lowercase    )
							return doc_ids, retrieved_doc_embeds






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase=None    ):
							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    , )
							_lowerCamelCase	:     int       =       retrieval_workers
							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    ):
							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    ):
							if len(self.retrieval_workers    ) > 0:
											# Select a random retrieval actor.
											_lowerCamelCase	:     int       =       self.retrieval_workers[random.randint(0    , len(self.retrieval_workers    ) - 1    )]
											_lowerCamelCase,					_lowerCamelCase	:     str       =       ray.get(random_worker.retrieve.remote(lowercase    , lowercase    )    )
							else:
											_lowerCamelCase,					_lowerCamelCase	:     Any       =       self._main_retrieve(lowercase    , lowercase    )
							return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , lowercase=None    , **lowercase    ):
							return super(lowercase    , cls    ).get_tokenizers(lowercase    , lowercase    , **lowercase    )






			@classmethod
			def    A_       (    cls    , lowercase    , lowercase    , lowercase=None    , **lowercase    ):
							_lowerCamelCase	:     List[str]       =       kwargs.pop('config'    , lowercase    ) or RagConfig.from_pretrained(lowercase    , **lowercase    )
							_lowerCamelCase	:     int       =       RagTokenizer.from_pretrained(lowercase    , config=lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       rag_tokenizer.question_encoder
							_lowerCamelCase	:     int       =       rag_tokenizer.generator
							if indexed_dataset is not None:
											_lowerCamelCase	:     List[Any]       =       'custom'
											_lowerCamelCase	:     Tuple       =       CustomHFIndex(config.retrieval_vector_size    , lowercase    )
							else:
											_lowerCamelCase	:     Optional[int]       =       cls._build_index(lowercase    )
							return cls(
							    lowercase    , question_encoder_tokenizer=lowercase    , generator_tokenizer=lowercase    , retrieval_workers=lowercase    , index=lowercase    , )





"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0						)

				for i in range(1				,       lowercase__						):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1				,       lowercase__						)

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(lowercase__						)						)				,       list(range(lowercase__						)						)						)

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


if __name__ == "__main__":
			print(F"Total count for various states are: {quantum_entanglement(3)}")