


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			:			List[str]												=					16
a			:			int												=					32


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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



					'''simple docstring'''







					def __enter__(					self    ) ->      Union[str, 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_    ) ->      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()






a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


