


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 : int		=				16
a : Optional[Any]		=				32


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			return int(x / 2**20  )
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __enter__(				self						)   ->			Any:







								'''simple docstring'''

								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								__lowercase 					=   torch.cuda.memory_allocated()
								return self






					def __exit__(				self ,       *snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

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




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16       ,			_UpperCamelCase = "bert-base-cased"       ,			_UpperCamelCase = 3_20       ,			_UpperCamelCase = 1_60       ,			):


			'''simple docstring'''







			__lowercase 					=   AutoTokenizer.from_pretrained(_UpperCamelCase  )
			__lowercase 					=   load_dataset(
			    '''glue'''       ,			'''mrpc'''       ,			split={'''train''': F'train[:{n_train}]', '''validation''': F'validation[:{n_val}]'}  )

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

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

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

			def collate_fn(_UpperCamelCase  ):
						# 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(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(
			    tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=_UpperCamelCase  )
			__lowercase 					=   DataLoader(
			    tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=_UpperCamelCase  )

			return train_dataloader, eval_dataloader


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   Accelerator()

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

			set_seed(_UpperCamelCase  )
			__lowercase					, __lowercase 					=   get_dataloaders(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			args.n_train       ,			args.n_val  )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__lowercase 					=   AutoModelForSequenceClassification.from_pretrained(_UpperCamelCase       ,			return_dict=_UpperCamelCase  )

			# Instantiate optimizer
			__lowercase 					=   (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			__lowercase 					=   optimizer_cls(params=model.parameters()       ,			lr=_UpperCamelCase  )

			if accelerator.state.deepspeed_plugin is not None:
						__lowercase 					=   accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						__lowercase 					=   1
			__lowercase 					=   (len(_UpperCamelCase  ) * 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 					=   get_linear_schedule_with_warmup(
						    optimizer=_UpperCamelCase       ,			num_warmup_steps=0       ,			num_training_steps=_UpperCamelCase       ,			)
			else:
						__lowercase 					=   DummyScheduler(_UpperCamelCase       ,			total_num_steps=_UpperCamelCase       ,			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					, __lowercase					, __lowercase					, __lowercase					, __lowercase 					=   accelerator.prepare(
			    _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

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

			# Now we train the model
			__lowercase 					=   {}
			for epoch in range(_UpperCamelCase       ,			_UpperCamelCase  ):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(_UpperCamelCase  ):
												__lowercase 					=   model(**_UpperCamelCase  )
												__lowercase 					=   outputs.loss
												__lowercase 					=   loss / gradient_accumulation_steps
												accelerator.backward(_UpperCamelCase  )
												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 					=   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(_UpperCamelCase       ,			_UpperCamelCase  )


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''  )
			parser.add_argument(
			    '''--model_name_or_path'''       ,			type=_UpperCamelCase       ,			default='''bert-base-cased'''       ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,			required=_UpperCamelCase       ,			)
			parser.add_argument(
			    '''--output_dir'''       ,			type=_UpperCamelCase       ,			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=_UpperCamelCase       ,			default=_UpperCamelCase       ,			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=_UpperCamelCase       ,			default=3_20       ,			help='''Number of training examples to use.'''       ,			)
			parser.add_argument(
			    '''--n_val'''       ,			type=_UpperCamelCase       ,			default=1_60       ,			help='''Number of validation examples to use.'''       ,			)
			parser.add_argument(
			    '''--num_epochs'''       ,			type=_UpperCamelCase       ,			default=1       ,			help='''Number of train epochs.'''       ,			)
			__lowercase 					=   parser.parse_args()
			__lowercase 					=   {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
			training_function(_UpperCamelCase       ,			_UpperCamelCase  )


if __name__ == "__main__":
	main()






from ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)




class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)


