




import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__a    :						Optional[Any]						      =       1_6
__a    :						List[str]						      =       3_2
def    snake_case_	(						SCREAMING_SNAKE_CASE_							)     ->					str:
							return int(x / 2**20							)







class  UpperCAmelCase:



	"""simple docstring"""






	def __enter__(	self							)   ->     Dict:





								"""simple docstring"""


								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								lowercase__    :			Union[str, Any]     =				torch.cuda.memory_allocated()
								return self







	def __exit__(	self			, *lowerCamelCase							)   ->     int:





								"""simple docstring"""


								gc.collect()
								torch.cuda.empty_cache()
								lowercase__    :			List[str]     =				torch.cuda.memory_allocated()
								lowercase__    :			List[str]     =				torch.cuda.max_memory_allocated()
								lowercase__    :			Any     =				bamb(self.end - self.begin							)
								lowercase__    :			Any     =				bamb(self.peak - self.begin							)
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")

def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_ = 16							,SCREAMING_SNAKE_CASE_ = "bert-base-cased"							,SCREAMING_SNAKE_CASE_ = 3_20							,SCREAMING_SNAKE_CASE_ = 1_60							,)     ->					Union[str, Any]:
							lowercase__    :			str     =				AutoTokenizer.from_pretrained(snake_case__							)
							lowercase__    :			Dict     =				load_dataset(
							    "glue"							,"mrpc"							,split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}							)

							def tokenize_function(SCREAMING_SNAKE_CASE_							):
														# max_length=None => use the model max length (it's actually the default)
														lowercase__    :			Optional[int]     =				tokenizer(examples["sentence1"]							,examples["sentence2"]							,truncation=snake_case__							,max_length=snake_case__							)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowercase__    :			Optional[Any]     =				datasets.map(
							    snake_case__							,batched=snake_case__							,remove_columns=["idx", "sentence1", "sentence2"]							,load_from_cache_file=snake_case__							)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							lowercase__    :			Dict     =				tokenized_datasets.rename_column("label"							,"labels"							)

							def collate_fn(SCREAMING_SNAKE_CASE_							):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(snake_case__							,padding="max_length"							,max_length=1_28							,return_tensors="pt"							)
														return tokenizer.pad(snake_case__							,padding="longest"							,return_tensors="pt"							)

							# Instantiate dataloaders.
							lowercase__    :			Dict     =				DataLoader(
							    tokenized_datasets["train"]							,shuffle=snake_case__							,collate_fn=snake_case__							,batch_size=snake_case__							)
							lowercase__    :			str     =				DataLoader(
							    tokenized_datasets["validation"]							,shuffle=snake_case__							,collate_fn=snake_case__							,batch_size=snake_case__							)

							return train_dataloader, eval_dataloader
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					int:
							lowercase__    :			Optional[int]     =				Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowercase__    :			Optional[Any]     =				config["""lr"""]
							lowercase__    :			int     =				int(config["num_epochs"]							)
							lowercase__    :			List[str]     =				int(config["seed"]							)
							lowercase__    :			int     =				int(config["batch_size"]							)
							lowercase__    :			Optional[int]     =				args.model_name_or_path

							set_seed(snake_case__							)
							lowercase__    :			Union[str, Any]     =				get_dataloaders(snake_case__							,snake_case__							,snake_case__							,args.n_train							,args.n_val							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowercase__    :			Tuple     =				AutoModelForSequenceClassification.from_pretrained(snake_case__							,return_dict=snake_case__							)

							# Instantiate optimizer
							lowercase__    :			List[str]     =				(
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							lowercase__    :			Any     =				optimizer_cls(params=model.parameters()							,lr=snake_case__							)

							if accelerator.state.deepspeed_plugin is not None:
														lowercase__    :			Optional[int]     =				accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														lowercase__    :			Optional[int]     =				1
							lowercase__    :			Dict     =				(len(snake_case__							) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														lowercase__    :			Union[str, Any]     =				get_linear_schedule_with_warmup(
														    optimizer=snake_case__							,num_warmup_steps=0							,num_training_steps=snake_case__							,)
							else:
														lowercase__    :			Any     =				DummyScheduler(snake_case__							,total_num_steps=snake_case__							,warmup_num_steps=0							)

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							lowercase__    :			str     =				accelerator.prepare(
							    snake_case__							,snake_case__							,snake_case__							,snake_case__							,snake_case__							)

							# We need to keep track of how many total steps we have iterated over
							lowercase__    :			Dict     =				0
							# We also need to keep track of the stating epoch so files are named properly
							lowercase__    :			Any     =				0

							# Now we train the model
							lowercase__    :			Optional[int]     =				{}
							for epoch in range(snake_case__							,snake_case__							):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(snake_case__							):
																												lowercase__    :			Optional[int]     =				model(**snake_case__							)
																												lowercase__    :			Optional[Any]     =				outputs.loss
																												lowercase__    :			str     =				loss / gradient_accumulation_steps
																												accelerator.backward(snake_case__							)
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

																												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
														accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin							)							)							)
														accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used							)							)
														accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked							)							)
														accelerator.print(
														    "Total Peak Memory consumed during the train (max): {}".format(
														        tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
														lowercase__    :			Optional[Any]     =				tracemalloc.peaked + bamb(tracemalloc.begin							)
														if args.peak_memory_upper_bound is not None:
																					assert (
																					    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
																					), "Peak memory usage exceeded the upper bound"

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir							,"peak_memory_utilization.json"							)							,"w"							) as f:
																					json.dump(snake_case__							,snake_case__							)
def    snake_case_	(						)     ->					List[str]:
							lowercase__    :			List[Any]     =				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."							)
							parser.add_argument(
							    "--model_name_or_path"							,type=snake_case__							,default="bert-base-cased"							,help="Path to pretrained model or model identifier from huggingface.co/models."							,required=snake_case__							,)
							parser.add_argument(
							    "--output_dir"							,type=snake_case__							,default="."							,help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."							,)
							parser.add_argument(
							    "--peak_memory_upper_bound"							,type=snake_case__							,default=snake_case__							,help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."							,)
							parser.add_argument(
							    "--n_train"							,type=snake_case__							,default=3_20							,help="Number of training examples to use."							,)
							parser.add_argument(
							    "--n_val"							,type=snake_case__							,default=1_60							,help="Number of validation examples to use."							,)
							parser.add_argument(
							    "--num_epochs"							,type=snake_case__							,default=1							,help="Number of train epochs."							,)
							lowercase__    :			Tuple     =				parser.parse_args()
							lowercase__    :			Dict     =				{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(snake_case__							,snake_case__							)


if __name__ == "__main__":
						main()







def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     if digit_amount > 0:
          return round(number - int(snake_case__	)						,snake_case__	)
     return number - int(snake_case__	)


if __name__ == "__main__":
     print(decimal_isolate(1.5_3, 0))
     print(decimal_isolate(3_5.3_4_5, 1))
     print(decimal_isolate(3_5.3_4_5, 2))
     print(decimal_isolate(3_5.3_4_5, 3))
     print(decimal_isolate(-1_4.7_8_9, 3))
     print(decimal_isolate(0, 2))
     print(decimal_isolate(-1_4.1_2_3, 1))
     print(decimal_isolate(-1_4.1_2_3, 2))
     print(decimal_isolate(-1_4.1_2_3, 3))


