

import gc
import threading
import time

import psutil
import torch



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self     ):
							UpperCAmelCase_						=				psutil.Process()
							UpperCAmelCase_						=				False



					def    A__		(			self     ):
							UpperCAmelCase_						=				-1

							while True:
									UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_						=				True
							UpperCAmelCase_						=				threading.Thread(target=self.peak_monitor     )
							UpperCAmelCase_						=				True
							self.thread.start()



					def    A__		(			self     ):
							UpperCAmelCase_						=				False
							self.thread.join()
							return self.cpu_memory_peak





SCREAMING_SNAKE_CASE     				=						PeakCPUMemory()







def snake_case__  (			)		->							List[Any]:
		# Time
		UpperCAmelCase_						=				{"time": time.time()}

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

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

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

		return measures







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		# Time
		UpperCAmelCase_						=				{"time": time.time() - start_measures["time"]}

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

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

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

		return measures







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_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(__SCREAMING_SNAKE_CASE )]:.2f}MiB''' )
				UpperCAmelCase_						=				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_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

