
import gc
import threading
import time

import psutil
import torch





class 							UpperCAmelCase				:




			'''simple docstring'''





			def __init__(	self :			Any			):
							__A             =			psutil.Process()
							__A             =			False






			def     UpperCamelCase_  (	self :			Optional[Any]			):
							__A             =			-1

							while True:
											__A             =			max(self.process.memory_info().rss							,self.cpu_memory_peak			)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
															break






			def     UpperCamelCase_  (	self :			str			):
							__A             =			True
							__A             =			threading.Thread(target=self.peak_monitor			)
							__A             =			True
							self.thread.start()







			def     UpperCamelCase_  (	self :			Any			):
							__A             =			False
							self.thread.join()
							return self.cpu_memory_peak





SCREAMING_SNAKE_CASE							:Union[str, Any]										=    PeakCPUMemory()


def 						UpperCAmelCase      (     )		->     List[Any]:







				"""simple docstring"""



				__A             =			{"time": time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__A             =			psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()						):
								__A             =			torch.cuda.memory_allocated(a_						)
				torch.cuda.reset_peak_memory_stats()

				return measures


def 						UpperCAmelCase      (     a_						)		->     Dict:







				"""simple docstring"""



				__A             =			{"time": time.time() - start_measures["time"]}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__A             =			(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**2_0
				__A             =			(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**2_0

				# GPU mem
				for i in range(torch.cuda.device_count()						):
								__A             =			(torch.cuda.memory_allocated(a_						) - start_measures[str(a_						)]) / 2**2_0
								__A             =			(torch.cuda.max_memory_allocated(a_						) - start_measures[str(a_						)]) / 2**2_0

				return measures


def 						UpperCAmelCase      (     a_		,       a_						)		->     List[str]:







				"""simple docstring"""



				print(F'''{description}:'''						)
				print(F'''- Time: {measures['time']:.2f}s'''						)
				for i in range(torch.cuda.device_count()						):
								print(F'''- GPU {i} allocated: {measures[str(a_						)]:.2f}MiB'''						)
								__A             =			measures[F'''{i}-peak''']
								print(F'''- GPU {i} peak: {peak:.2f}MiB'''						)
				print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''						)
				print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''						)


from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE							:Dict										=    300  # TEMPERATURE (unit = K)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       )		->     float:







				"""simple docstring"""



				if donor_conc <= 0:
								raise ValueError("Donor concentration should be positive"						)
				elif acceptor_conc <= 0:
								raise ValueError("Acceptor concentration should be positive"						)
				elif intrinsic_conc <= 0:
								raise ValueError("Intrinsic concentration should be positive"						)
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    "Donor concentration should be greater than intrinsic concentration"						)
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    "Acceptor concentration should be greater than intrinsic concentration"						)
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
