
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


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



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return int(x / 2**20	)







class        lowercase	:




							def __enter__(       self):
												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     ,*A__):
												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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 16				,    lowerCAmelCase__ = "bert-base-cased"				,    lowerCAmelCase__ = 320				,    lowerCAmelCase__ = 160				,    ):
					'''simple docstring'''
					lowercase					=		AutoTokenizer.from_pretrained(lowerCAmelCase__	)
					lowercase					=		load_dataset(
					    '''glue'''				,    '''mrpc'''				,    split={'''train''': f'train[:{n_train}]', '''validation''': f'validation[:{n_val}]'}	)

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

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

					# 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(lowerCAmelCase__	):
										# 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(lowerCAmelCase__				,    padding='''max_length'''				,    max_length=128				,    return_tensors='''pt'''	)
										return tokenizer.pad(lowerCAmelCase__				,    padding='''longest'''				,    return_tensors='''pt'''	)

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

					return train_dataloader, eval_dataloader



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Initialize accelerator
					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(lowerCAmelCase__	)
					lowercase			,		lowercase					=		get_dataloaders(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    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(lowerCAmelCase__				,    return_dict=lowerCAmelCase__	)

					# 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=lowerCAmelCase__	)

					if accelerator.state.deepspeed_plugin is not None:
										lowercase					=		accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										lowercase					=		1
					lowercase					=		(len(lowerCAmelCase__	) * 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=lowerCAmelCase__				,    num_warmup_steps=0				,    num_training_steps=lowerCAmelCase__				,    )
					else:
										lowercase					=		DummyScheduler(lowerCAmelCase__				,    total_num_steps=lowerCAmelCase__				,    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(
					    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)

					# 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(lowerCAmelCase__				,    lowerCAmelCase__	):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(lowerCAmelCase__	):
																				lowercase					=		model(**lowerCAmelCase__	)
																				lowercase					=		outputs.loss
																				lowercase					=		loss / gradient_accumulation_steps
																				accelerator.backward(lowerCAmelCase__	)
																				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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
					parser.add_argument(
					    '''--model_name_or_path'''				,    type=lowerCAmelCase__				,    default='''bert-base-cased'''				,    help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,    required=lowerCAmelCase__				,    )
					parser.add_argument(
					    '''--output_dir'''				,    type=lowerCAmelCase__				,    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=lowerCAmelCase__				,    default=lowerCAmelCase__				,    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=lowerCAmelCase__				,    default=320				,    help='''Number of training examples to use.'''				,    )
					parser.add_argument(
					    '''--n_val'''				,    type=lowerCAmelCase__				,    default=160				,    help='''Number of validation examples to use.'''				,    )
					parser.add_argument(
					    '''--num_epochs'''				,    type=lowerCAmelCase__				,    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(lowerCAmelCase__				,    lowerCAmelCase__	)


if __name__ == "__main__":
							main()






def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					return int((input_a, input_a).count(0	) == 0	)



def  UpperCamelCase					(							):
					'''simple docstring'''
					assert and_gate(0				,    0	) == 0
					assert and_gate(0				,    1	) == 0
					assert and_gate(1				,    0	) == 0
					assert and_gate(1				,    1	) == 1


if __name__ == "__main__":
							test_and_gate()
							print(and_gate(1, 0))
							print(and_gate(0, 0))
							print(and_gate(0, 1))
							print(and_gate(1, 1))




