




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


__lowerCamelCase   :						Tuple				=			16
__lowerCamelCase   :						Dict				=			32







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					return int(x / 2**2_0				)


class    lowerCamelCase    :






			'''simple docstring'''





			def __enter__( self			:   Optional[int]  )						->   List[Any]:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								__magic_name__							:	int    							=							torch.cuda.memory_allocated()
								return self


			def __exit__( self			:   str       ,						*lowerCamelCase_			:   str  )						->   Any:
								gc.collect()
								torch.cuda.empty_cache()
								__magic_name__							:	Optional[int]    							=							torch.cuda.memory_allocated()
								__magic_name__							:	Optional[Any]    							=							torch.cuda.max_memory_allocated()
								__magic_name__							:	Union[str, Any]    							=							bamb(self.end - self.begin  )
								__magic_name__							:	Tuple    							=							bamb(self.peak - self.begin  )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 		lowercase__   (		__A:	Accelerator							,__A:	int = 1_6							,__A:	str = "bert-base-cased"							,__A:	int = 3_2_0							,__A:	int = 1_6_0							,):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							AutoTokenizer.from_pretrained(__A				)
					__magic_name__							:	str    							=							load_dataset(
					    '''glue'''							,'''mrpc'''							,split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}				)

					def tokenize_function(__A:	str				):
										# max_length=None => use the model max length (it's actually the default)
										__magic_name__							:	int    							=							tokenizer(examples['''sentence1''']							,examples['''sentence2''']							,truncation=__A							,max_length=__A				)
										return outputs

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

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__magic_name__							:	Optional[int]    							=							tokenized_datasets.rename_column('''label'''							,'''labels'''				)

					def collate_fn(__A:	Dict				):
										# 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(__A							,padding='''max_length'''							,max_length=1_2_8							,return_tensors='''pt'''				)
										return tokenizer.pad(__A							,padding='''longest'''							,return_tensors='''pt'''				)

					# Instantiate dataloaders.
					__magic_name__							:	str    							=							DataLoader(
					    tokenized_datasets['''train''']							,shuffle=__A							,collate_fn=__A							,batch_size=__A				)
					__magic_name__							:	Tuple    							=							DataLoader(
					    tokenized_datasets['''validation''']							,shuffle=__A							,collate_fn=__A							,batch_size=__A				)

					return train_dataloader, eval_dataloader







def 		lowercase__   (		__A:	Dict							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							Accelerator()

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

					set_seed(__A				)
					__magic_name__							,    __magic_name__							:	Tuple    							=							get_dataloaders(__A							,__A							,__A							,args.n_train							,args.n_val				)

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

					# Instantiate optimizer
					__magic_name__							:	Optional[int]    							=							(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__magic_name__							:	int    							=							optimizer_cls(params=model.parameters()							,lr=__A				)

					if accelerator.state.deepspeed_plugin is not None:
										__magic_name__							:	Dict    							=							accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										__magic_name__							:	List[Any]    							=							1
					__magic_name__							:	Union[str, Any]    							=							(len(__A				) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										__magic_name__							:	str    							=							get_linear_schedule_with_warmup(
										    optimizer=__A							,num_warmup_steps=0							,num_training_steps=__A							,)
					else:
										__magic_name__							:	int    							=							DummyScheduler(__A							,total_num_steps=__A							,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.
					__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							:	Dict    							=							accelerator.prepare(
					    __A							,__A							,__A							,__A							,__A				)

					# We need to keep track of how many total steps we have iterated over
					__magic_name__							:	Any    							=							0
					# We also need to keep track of the stating epoch so files are named properly
					__magic_name__							:	Union[str, Any]    							=							0

					# Now we train the model
					__magic_name__							:	Optional[int]    							=							{}
					for epoch in range(__A							,__A				):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(__A				):
																				__magic_name__							:	Dict    							=							model(**__A				)
																				__magic_name__							:	int    							=							outputs.loss
																				__magic_name__							:	Any    							=							loss / gradient_accumulation_steps
																				accelerator.backward(__A				)
																				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				)				)				)
										__magic_name__							:	int    							=							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(__A							,__A				)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
					parser.add_argument(
					    '''--model_name_or_path'''							,type=__A							,default='''bert-base-cased'''							,help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,required=__A							,)
					parser.add_argument(
					    '''--output_dir'''							,type=__A							,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=__A							,default=__A							,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=__A							,default=3_2_0							,help='''Number of training examples to use.'''							,)
					parser.add_argument(
					    '''--n_val'''							,type=__A							,default=1_6_0							,help='''Number of validation examples to use.'''							,)
					parser.add_argument(
					    '''--num_epochs'''							,type=__A							,default=1							,help='''Number of train epochs.'''							,)
					__magic_name__							:	int    							=							parser.parse_args()
					__magic_name__							:	Union[str, Any]    							=							{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
					training_function(__A							,__A				)


if __name__ == "__main__":
	main()









import functools







def 		lowercase__   (		__A:	list[int]							,__A:	list[int]				):





					'''simple docstring'''






					if not isinstance(__A							,__A				) or not all(isinstance(__A							,__A				) for day in days				):
										raise ValueError('''The parameter days should be a list of integers'''				)

					if len(__A				) != 3 or not all(isinstance(__A							,__A				) for cost in costs				):
										raise ValueError('''The parameter costs should be a list of three integers'''				)

					if len(__A				) == 0:
										return 0

					if min(__A				) <= 0:
										raise ValueError('''All days elements should be greater than 0'''				)

					if max(__A				) >= 3_6_6:
										raise ValueError('''All days elements should be less than 366'''				)

					__magic_name__							:	Tuple    							=							set(__A				)

					@functools.cache
					def dynamic_programming(__A:	int				) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1				)

										return min(
										    costs[0] + dynamic_programming(index + 1				)							,costs[1] + dynamic_programming(index + 7				)							,costs[2] + dynamic_programming(index + 3_0				)							,)

					return dynamic_programming(1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



