






"""simple docstring"""





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							=  16
__lowerCamelCase							=  32



def   a    (     __UpperCAmelCase      :    Tuple  )    ->							str:
						return int(x / 2**2_0  )



class       __A		:

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



def   a    (     __UpperCAmelCase      :    Accelerator      ,   __UpperCAmelCase      :    int = 1_6      ,   __UpperCAmelCase      :    str = "bert-base-cased"      ,   __UpperCAmelCase      :    int = 3_2_0      ,   __UpperCAmelCase      :    int = 1_6_0      ,   )    ->							List[str]:
						__magic_name__:	Optional[Any]							=       AutoTokenizer.from_pretrained(__UpperCAmelCase  )
						__magic_name__:	List[str]							=       load_dataset(
						    """glue"""      ,   """mrpc"""      ,   split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'}  )

						def tokenize_function(__UpperCAmelCase      :    List[Any]  ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__:	str							=       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
						__magic_name__:	Any							=       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
						__magic_name__:	List[Any]							=       tokenized_datasets.rename_column("""label"""      ,   """labels"""  )

						def collate_fn(__UpperCAmelCase      :    Union[str, Any]  ):
												# 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_2_8      ,   return_tensors="""pt"""  )
												return tokenizer.pad(__UpperCAmelCase      ,   padding="""longest"""      ,   return_tensors="""pt"""  )

						# Instantiate dataloaders.
						__magic_name__:	Any							=       DataLoader(
						    tokenized_datasets["""train"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )
						__magic_name__:	str							=       DataLoader(
						    tokenized_datasets["""validation"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )

						return train_dataloader, eval_dataloader



def   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    Optional[int]  )    ->							Any:
						# Initialize accelerator
						__magic_name__:	List[Any]							=       Accelerator()

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

						set_seed(__UpperCAmelCase  )
						__magic_name__,			__magic_name__:	str							=       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)
						__magic_name__:	int							=       AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase      ,   return_dict=__UpperCAmelCase  )

						# Instantiate optimizer
						__magic_name__:	Any							=       (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__magic_name__:	Any							=       optimizer_cls(params=model.parameters()      ,   lr=__UpperCAmelCase  )

						if accelerator.state.deepspeed_plugin is not None:
												__magic_name__:	Union[str, Any]							=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												__magic_name__:	Dict							=       1
						__magic_name__:	Any							=       (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
						):
												__magic_name__:	Optional[int]							=       get_linear_schedule_with_warmup(
												    optimizer=__UpperCAmelCase      ,   num_warmup_steps=0      ,   num_training_steps=__UpperCAmelCase      ,   )
						else:
												__magic_name__:	Union[str, Any]							=       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.
						__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	str							=       accelerator.prepare(
						    __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )

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

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



def   a    (     )    ->							List[Any]:
						__magic_name__:	Any							=       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_2_0      ,   help="""Number of training examples to use."""      ,   )
						parser.add_argument(
						    """--n_val"""      ,   type=__UpperCAmelCase      ,   default=1_6_0      ,   help="""Number of validation examples to use."""      ,   )
						parser.add_argument(
						    """--num_epochs"""      ,   type=__UpperCAmelCase      ,   default=1      ,   help="""Number of train epochs."""      ,   )
						__magic_name__:	Optional[Any]							=       parser.parse_args()
						__magic_name__:	Any							=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
						training_function(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							main()




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






