
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
lowercase__		:Union[str, Any]   =    logging.getLogger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						git.Repo(search_parent_directories=lowerCAmelCase__		)
					lowercase							=						{
					    '''repo_id''': str(lowerCAmelCase__		),
					    '''repo_sha''': str(repo.head.object.hexsha		),
					    '''repo_branch''': str(repo.active_branch		),
					}

					with open(os.path.join(lowerCAmelCase__ ,			'''git_log.json'''		) ,			'''w'''		) as f:
										json.dump(lowerCAmelCase__ ,			lowerCAmelCase__ ,			indent=4		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if params.n_gpu <= 0:
										lowercase							=						0
										lowercase							=						-1
										lowercase							=						True
										lowercase							=						False
										return

					assert torch.cuda.is_available()

					logger.info('''Initializing GPUs'''		)
					if params.n_gpu > 1:
										assert params.local_rank != -1

										lowercase							=						int(os.environ['''WORLD_SIZE''']		)
										lowercase							=						int(os.environ['''N_GPU_NODE''']		)
										lowercase							=						int(os.environ['''RANK''']		)

										# number of nodes / node ID
										lowercase							=						params.world_size // params.n_gpu_per_node
										lowercase							=						params.global_rank // params.n_gpu_per_node
										lowercase							=						True

										assert params.n_nodes == int(os.environ['''N_NODES''']		)
										assert params.node_id == int(os.environ['''NODE_RANK''']		)

					# local job (single GPU)
					else:
										assert params.local_rank == -1

										lowercase							=						1
										lowercase							=						0
										lowercase							=						0
										lowercase							=						0
										lowercase							=						1
										lowercase							=						1
										lowercase							=						False

					# sanity checks
					assert params.n_nodes >= 1
					assert 0 <= params.node_id < params.n_nodes
					assert 0 <= params.local_rank <= params.global_rank < params.world_size
					assert params.world_size == params.n_nodes * params.n_gpu_per_node

					# define whether this is the master process / if we are in multi-node distributed mode
					lowercase							=						params.node_id == 0 and params.local_rank == 0
					lowercase							=						params.n_nodes > 1

					# summary
					lowercase							=						f'--- Global rank: {params.global_rank} - '
					logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes		)
					logger.info(PREFIX + '''Node ID        : %i''' % params.node_id		)
					logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank		)
					logger.info(PREFIX + '''World size     : %i''' % params.world_size		)
					logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node		)
					logger.info(PREFIX + '''Master         : %s''' % str(params.is_master		)		)
					logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node		)		)
					logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu		)		)
					logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()		)

					# set GPU device
					torch.cuda.set_device(params.local_rank		)

					# initialize multi-GPU
					if params.multi_gpu:
										logger.info('''Initializing PyTorch distributed'''		)
										torch.distributed.init_process_group(
										    init_method='''env://''' ,			backend='''nccl''' ,			)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					np.random.seed(args.seed		)
					torch.manual_seed(args.seed		)
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed		)











"""simple docstring"""
import qiskit



def      __lowerCamelCase   (							a_     :		int ,   a_     :		int							)			->      qiskit.result.counts.Counts:
      __SCREAMING_SNAKE_CASE   :Tuple						=  qiskit.Aer.get_backend('''aer_simulator'''							)

      # Create a Quantum Circuit acting on the q register
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  qiskit.QuantumCircuit(a_ ,   a_							)

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0							)
      circuit.x(1							)

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1] ,   [0, 1]							)

      # Execute the circuit on the qasm simulator
      __SCREAMING_SNAKE_CASE   :Tuple						=  qiskit.execute(a_ ,   a_ ,   shots=10_00							)

      # Return the histogram data of the results of the experiment.
      return job.result().get_counts(a_							)


if __name__ == "__main__":
  lowerCamelCase_								=		single_qubit_measure(2, 2)
  print(f'Total count for various states are: {counts}')