
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'''						)


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
