import gc
import threading
import time

import psutil
import torch





class 					lowercase__						:


						def __init__(       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							psutil.Process()
												SCREAMING_SNAKE_CASE							:    Any							=							False


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							-1

												while True:
																		SCREAMING_SNAKE_CASE							:    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        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							threading.Thread(target=self.peak_monitor						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
												self.thread.start()



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							False
												self.thread.join()
												return self.cpu_memory_peak






__UpperCamelCase		:       Tuple									=     PeakCPUMemory()
def       A	(    ):
						# Time
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							{"""time""": time.time()}

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

						# CPU mem
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							psutil.Process().memory_info().rss
						cpu_peak_tracker.start()

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

						return measures
def       A	(    _lowercase						):
						# Time
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{"""time""": time.time() - start_measures["""time"""]}

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

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

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

						return measures



def       A	(    _lowercase		,  _lowercase						):
						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"""						)
												SCREAMING_SNAKE_CASE							:    Dict							=							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 pytest


__UpperCAmelCase       :   Optional[Any]				       =							"__dummy_dataset1__"

__UpperCAmelCase       :   List[str]				       =							"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"







@pytest.fixture
def 			A__							(    )   ->    Optional[int]:
  return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def 			A__							(    )   ->    Tuple:
  return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[Any]   =						dataset_loading_script_name
  __snake_case:						Any   =						tmp_path / """datasets""" / script_name
  script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__)
  __snake_case:						int   =						script_dir / F'''{script_name}.py'''
  with open(SCREAMING_SNAKE_CASE__					,					"""w""") as f:
    f.write(SCREAMING_SNAKE_CASE__)
  return str(SCREAMING_SNAKE_CASE__)






