



"""simple docstring"""






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowerCAmelCase__              =							logging.getLogger(__name__)






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		List[str]		,   __lowerCAmelCase	:		Optional[int]		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Any		,   __lowerCAmelCase	:		List[str]=None						):






         """simple docstring"""






         super().__init__(
             __lowerCAmelCase		,   question_encoder_tokenizer=__lowerCAmelCase		,   generator_tokenizer=__lowerCAmelCase		,   index=__lowerCAmelCase		,   init_retrieval=__lowerCAmelCase		,   )
         _lowerCamelCase		:							List[str]            =					None




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         logger.info('''initializing retrieval'''						)

         # initializing a separate process group for retrieval as the default
         # nccl backend doesn't support gather/scatter operations while gloo
         # is too slow to replace nccl for the core gpu communication
         if dist.is_initialized():
                logger.info('''dist initialized'''						)
                # needs to be set manually
                _lowerCamelCase		:							str            =					self._infer_socket_ifname()
                # avoid clash with the NCCL port
                _lowerCamelCase		:							Optional[int]            =					str(distributed_port + 1						)
                _lowerCamelCase		:							Dict            =					dist.new_group(ranks=__lowerCAmelCase		,   backend='''gloo'''						)

         # initialize retriever only on the main worker
         if not dist.is_initialized() or self._is_main():
                logger.info('''dist not initialized / main'''						)
                self.index.init_index()

         # all processes wait untill the retriever is initialized by the main process
         if dist.is_initialized():
                torch.distributed.barrier(group=self.process_group						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         return dist.get_rank(group=self.process_group						) == 0




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		List[Any]		,   __lowerCAmelCase	:		Dict=torch.floataa						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					torch.empty(__lowerCAmelCase		,   dtype=__lowerCAmelCase						)
         dist.scatter(__lowerCAmelCase		,   src=0		,   scatter_list=__lowerCAmelCase		,   group=self.process_group						)
         return target_tensor




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         _lowerCamelCase		:							int            =					next((addr for addr in addrs if addr.startswith('''e'''						))		,   __lowerCAmelCase						)
         return ifname




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		np.ndarray		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         if not dist.is_initialized():
                _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					self._main_retrieve(__lowerCAmelCase		,   __lowerCAmelCase						)
                return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase						)

         # distributed training
         _lowerCamelCase		:							Tuple            =					dist.get_world_size(group=self.process_group						)

         # gather logic
         _lowerCamelCase		:							int            =					None
         if self._is_main():
                _lowerCamelCase		:							Dict            =					[torch.empty(question_hidden_states.shape		,   dtype=torch.floataa						) for _ in range(__lowerCAmelCase						)]
         dist.gather(torch.tensor(__lowerCAmelCase						)		,   dst=0		,   gather_list=__lowerCAmelCase		,   group=self.process_group						)

         # scatter logic
         _lowerCamelCase		:							List[Any]            =					question_hidden_states.shape[0]
         _lowerCamelCase		:							Optional[int]            =					[]
         _lowerCamelCase		:							Any            =					[]
         if self._is_main():
                assert len(__lowerCAmelCase						) == world_size
                _lowerCamelCase     ,       _lowerCamelCase		:							Any            =					self._main_retrieve(torch.cat(__lowerCAmelCase						).numpy()		,   __lowerCAmelCase						)
                _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					torch.tensor(__lowerCAmelCase						), torch.tensor(__lowerCAmelCase						)
                _lowerCamelCase		:							Union[str, Any]            =					self._chunk_tensor(__lowerCAmelCase		,   __lowerCAmelCase						)
                _lowerCamelCase		:							Dict            =					self._chunk_tensor(__lowerCAmelCase		,   __lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					self._scattered(__lowerCAmelCase		,   [n_queries, n_docs]		,   target_type=torch.intaa						)
         _lowerCamelCase		:							int            =					self._scattered(__lowerCAmelCase		,   [n_queries, n_docs, question_hidden_states.shape[1]]						)

         return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCAmelCase						)






import math

import qiskit

def 			lowerCAmelCase__					(     a__:						int = 1     ,    a__:						int = 1     ,    a__:						int = 1			)    ->						qiskit.result.counts.Counts:


							'''simple docstring'''





							if (
							    isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							    or isinstance(a__     ,    a__			)
							):
														raise TypeError('inputs must be integers.'			)

							if (input_a < 0) or (input_a < 0) or (carry_in < 0):
														raise ValueError('inputs must be positive.'			)

							if (
							    (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != input_a)
							    or (math.floor(a__			) != carry_in)
							):
														raise ValueError('inputs must be exact integers.'			)

							if (input_a > 2) or (input_a > 2) or (carry_in > 2):
														raise ValueError('inputs must be less or equal to 2.'			)

							# build registers
							_UpperCAmelCase											=     qiskit.QuantumRegister(4     ,    'qr'			)
							_UpperCAmelCase											=     qiskit.ClassicalRegister(2     ,    'cr'			)
							# list the entries
							_UpperCAmelCase											=     [input_a, input_a, carry_in]

							_UpperCAmelCase											=     qiskit.QuantumCircuit(a__     ,    a__			)

							for i in range(0     ,    3			):
														if entry[i] == 2:
																					quantum_circuit.h(a__			)  # for hadamard entries
														elif entry[i] == 1:
																					quantum_circuit.x(a__			)  # for 1 entries
														elif entry[i] == 0:
																					quantum_circuit.i(a__			)  # for 0 entries

    # build the circuit
							quantum_circuit.ccx(0     ,    1     ,    3			)  # ccx = toffoli gate
							quantum_circuit.cx(0     ,    1			)
							quantum_circuit.ccx(1     ,    2     ,    3			)
							quantum_circuit.cx(1     ,    2			)
							quantum_circuit.cx(0     ,    1			)

							quantum_circuit.measure([2, 3]     ,    a__			)  # measure the last two qbits

							_UpperCAmelCase											=     qiskit.Aer.get_backend('aer_simulator'			)
							_UpperCAmelCase											=     qiskit.execute(a__     ,    a__     ,    shots=1_0_0_0			)

							return job.result().get_counts(a__			)


if __name__ == "__main__":
		print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
