




import gc
import threading
import time

import psutil
import torch



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						str ):
						snake_case__				:   List[str]     =						psutil.Process()
						snake_case__				:   int     =						False



					def  _lowercase  (      self  :						int ):
						snake_case__				:   List[str]     =						-1

						while True:
							snake_case__				:   List[Any]     =						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  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						True
						snake_case__				:   int     =						threading.Thread(target=self.peak_monitor )
						snake_case__				:   Dict     =						True
						self.thread.start()






					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   str     =						False
						self.thread.join()
						return self.cpu_memory_peak
__lowerCamelCase      :    Dict   		=   PeakCPUMemory()







def   SCREAMING_SNAKE_CASE       (				):
	# Time
	snake_case__				:   int     =						{"time": time.time()}

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

	# CPU mem
	snake_case__				:   List[str]     =						psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()     ):
		snake_case__				:   Dict     =						torch.cuda.memory_allocated(snake_case_     )
	torch.cuda.reset_peak_memory_stats()

	return measures







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# Time
	snake_case__				:   Tuple     =						{"time": time.time() - start_measures["time"]}

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

	# CPU mem
	snake_case__				:   int     =						(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
	snake_case__				:   Dict     =						(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

	# GPU mem
	for i in range(torch.cuda.device_count()     ):
		snake_case__				:   Dict     =						(torch.cuda.memory_allocated(snake_case_     ) - start_measures[str(snake_case_     )]) / 2**20
		snake_case__				:   Optional[Any]     =						(torch.cuda.max_memory_allocated(snake_case_     ) - start_measures[str(snake_case_     )]) / 2**20

	return measures







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Optional[int]     ):
	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(snake_case_     )]:.2f}MiB'''     )
		snake_case__				:   Tuple     =						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'''     )











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Tuple     =						get_activation("gelu" )
						self.assertTrue(torch.allclose(gelu_python(__A )							,	torch_builtin(__A ) ) )
						self.assertFalse(torch.allclose(gelu_python(__A )							,	gelu_new(__A ) ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						snake_case__				:   int     =						get_activation("gelu_10" )

						snake_case__				:   Optional[int]     =						torch_builtin(__A )
						snake_case__				:   Dict     =						geluaa(__A )

						snake_case__				:   Optional[Any]     =						torch.where(y_gelu_aa < 1_0.0							,	1							,	0 )

						self.assertTrue(torch.max(__A ).item() == 1_0.0 )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask							,	y_gelu_aa * clipped_mask ) )



					def  _lowercase  (      self  :						str ):
						get_activation("gelu" )
						get_activation("gelu_10" )
						get_activation("gelu_fast" )
						get_activation("gelu_new" )
						get_activation("gelu_python" )
						get_activation("gelu_pytorch_tanh" )
						get_activation("linear" )
						get_activation("mish" )
						get_activation("quick_gelu" )
						get_activation("relu" )
						get_activation("sigmoid" )
						get_activation("silu" )
						get_activation("swish" )
						get_activation("tanh" )
						with self.assertRaises(__A ):
							get_activation("bogus" )
						with self.assertRaises(__A ):
							get_activation(__A )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						get_activation("gelu" )
						snake_case__				:   Any     =						1
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						self.assertEqual(acta.a							,	1 )
						with self.assertRaises(__A ):
							snake_case__				:   int     =						acta.a



