





"""simple docstring"""




import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       Union[str, Any]   )					->Optional[int]:






	'''simple docstring'''






	a :						List[Any]      =							torch.load(_lowercase       ,			map_location="cpu"   )

	a :						str      =							chkpt["model"]

	# We have the base model one level deeper than the original XLM repository
	a :						Union[str, Any]      =							{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			a :						Dict      =							v
		else:
			a :						Union[str, Any]      =							v

	a :						int      =							chkpt["params"]
	a :						Tuple      =							{n: v for n, v in config.items() if not isinstance(_lowercase       ,			(torch.FloatTensor, numpy.ndarray)   )}

	a :						str      =							chkpt["dico_word2id"]
	a :						str      =							{s + "</w>" if s.find("@@"   ) == -1 and i > 13 else s.replace("@@"       ,			""   ): i for s, i in vocab.items()}

	# Save pytorch-model
	a :						str      =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
	a :						str      =							pytorch_dump_folder_path + "/" + CONFIG_NAME
	a :						Optional[int]      =							pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

	print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
	torch.save(_lowercase       ,			_lowercase   )

	print(F"""Save configuration file to {pytorch_config_dump_path}"""   )
	with open(_lowercase       ,			"w"       ,			encoding="utf-8"   ) as f:
		f.write(json.dumps(_lowercase       ,			indent=2   ) + "\n"   )

	print(F"""Save vocab file to {pytorch_config_dump_path}"""   )
	with open(_lowercase       ,			"w"       ,			encoding="utf-8"   ) as f:
		f.write(json.dumps(_lowercase       ,			indent=2   ) + "\n"   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					str	      =     parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




import gc
import threading
import time

import psutil
import torch


class 			__UpperCamelCase							:






							def __init__(  self )      ->					Any:
								a :						str      =							psutil.Process()
								a :						str      =							False






							def        __a  (  self )      ->					str:
								a :						List[Any]      =							-1

								while True:
									a :						str      =							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 )      ->					List[str]:
								a :						Dict      =							True
								a :						Optional[Any]      =							threading.Thread(target=self.peak_monitor )
								a :						List[Any]      =							True
								self.thread.start()






							def        __a  (  self )      ->					List[str]:
								a :						Optional[int]      =							False
								self.thread.join()
								return self.cpu_memory_peak
a		:					Tuple	      =     PeakCPUMemory()






def  _SCREAMING_SNAKE_CASE						(      )					->List[Any]:






	'''simple docstring'''






	a :						List[str]      =							{"time": time.time()}

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

	# CPU mem
	a :						Tuple      =							psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()   ):
		a :						Any      =							torch.cuda.memory_allocated(_lowercase   )
	torch.cuda.reset_peak_memory_stats()

	return measures






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]   )					->Union[str, Any]:






	'''simple docstring'''






	a :						int      =							{"time": time.time() - start_measures["time"]}

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

	# CPU mem
	a :						List[Any]      =							(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
	a :						Union[str, Any]      =							(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

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

	return measures







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       Union[str, Any]   )					->Optional[int]:






	'''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"""   )
		a :						str      =							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"""   )


