"""simple docstring"""







import gc
import threading
import time

import psutil
import torch

class 	UpperCamelCase    :



				def __init__(self       :   Any)					->			Dict:
											__snake_case    :							Union[str, Any]   	=				psutil.Process()
											__snake_case    :							Optional[Any]   	=				False



				def       _lowercase  (self       :   Dict)					->			str:
											__snake_case    :							List[str]   	=				-1

											while True:
																		__snake_case    :							List[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       _lowercase  (self       :   Optional[int])					->			Union[str, Any]:
											__snake_case    :							Tuple   	=				True
											__snake_case    :							str   	=				threading.Thread(target=self.peak_monitor)
											__snake_case    :							int   	=				True
											self.thread.start()






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
											__snake_case    :							Dict   	=				False
											self.thread.join()
											return self.cpu_memory_peak







_a  :  List[str]=				PeakCPUMemory()
def 						__UpperCAmelCase       (					)							->    int:

							'''simple docstring'''





							__snake_case    :							List[str]   	=				{'time': time.time()}

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

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

							# GPU mem
							for i in range(torch.cuda.device_count()						):
														__snake_case    :							Optional[int]   	=				torch.cuda.memory_allocated(UpperCAmelCase_						)
							torch.cuda.reset_peak_memory_stats()

							return measures
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Optional[int]:

							'''simple docstring'''





							__snake_case    :							Union[str, Any]   	=				{'time': time.time() - start_measures['time']}

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

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

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

							return measures

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       List[str]						)							->    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(UpperCAmelCase_						)]:.2f}MiB"						)
														__snake_case    :							Optional[Any]   	=				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"						)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





