

import pytest

from datasets import inspect_metric, list_metrics, load_metric






@pytest.fixture
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     List[str]:
	monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'	, set()			)






@pytest.fixture
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     str:






	class        A_		:
								def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
									__lowerCamelCase				:				Optional[int]					       = metric_id






	class        A_		:
								_UpperCAmelCase    :  Tuple						      =      [MetricMock(_UpperCAmelCase				) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]
								def     lowerCAmelCase     (		self		:			Optional[int]):
									return self._metrics
	monkeypatch.setattr('datasets.inspect.huggingface_hub'	, HfhMock()			)






@pytest.mark.parametrize(
    'func, args'	, [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]			)
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     List[str]:



	if "tmp_path" in args:
		__lowerCamelCase				:				List[str]					       = tuple(arg if arg != 'tmp_path' else tmp_path for arg in args			)
	with pytest.warns(lowerCamelCase__	, match='https://huggingface.co/docs/evaluate'			):
		func(*lowerCamelCase__			)





import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

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

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

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

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

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

				# CPU mem
				__lowercase 			=							(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__lowercase 			=							(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							(torch.cuda.memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20
								__lowercase 			=							(torch.cuda.max_memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""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(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							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"							)