
'''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


_lowercase    =		16
_lowercase    =		32


def A				(__lowerCamelCase				:int		):
						return int(x / 2**20		)




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __enter__( self				):

												"""simple docstring"""



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




						def __exit__( self   ,       *_lowercase				):

												"""simple docstring"""



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






def A				(__lowerCamelCase				:Accelerator			,     __lowerCamelCase				:int = 16			,     __lowerCamelCase				:str = "bert-base-cased"			,     __lowerCamelCase				:int = 320			,     __lowerCamelCase				:int = 160			,     ):
						_lowerCAmelCase  						=			AutoTokenizer.from_pretrained(__lowerCamelCase		)
						_lowerCAmelCase  						=			load_dataset(
						    """glue"""			,     """mrpc"""			,     split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'}		)

						def tokenize_function(__lowerCamelCase				:Dict		):
												# max_length=None => use the model max length (it's actually the default)
												_lowerCAmelCase  						=			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
						_lowerCAmelCase  						=			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
						_lowerCAmelCase  						=			tokenized_datasets.rename_column("""label"""			,     """labels"""		)

						def collate_fn(__lowerCamelCase				:List[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(__lowerCamelCase			,     padding="""max_length"""			,     max_length=128			,     return_tensors="""pt"""		)
												return tokenizer.pad(__lowerCamelCase			,     padding="""longest"""			,     return_tensors="""pt"""		)

						# Instantiate dataloaders.
						_lowerCAmelCase  						=			DataLoader(
						    tokenized_datasets["""train"""]			,     shuffle=__lowerCamelCase			,     collate_fn=__lowerCamelCase			,     batch_size=__lowerCamelCase		)
						_lowerCAmelCase  						=			DataLoader(
						    tokenized_datasets["""validation"""]			,     shuffle=__lowerCamelCase			,     collate_fn=__lowerCamelCase			,     batch_size=__lowerCamelCase		)

						return train_dataloader, eval_dataloader


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:int		):
						# Initialize accelerator
						_lowerCAmelCase  						=			Accelerator()

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

						set_seed(__lowerCamelCase		)
						_lowerCAmelCase						,   _lowerCAmelCase  						=			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)
						_lowerCAmelCase  						=			AutoModelForSequenceClassification.from_pretrained(__lowerCamelCase			,     return_dict=__lowerCamelCase		)

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

						if accelerator.state.deepspeed_plugin is not None:
												_lowerCAmelCase  						=			accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												_lowerCAmelCase  						=			1
						_lowerCAmelCase  						=			(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
						):
												_lowerCAmelCase  						=			get_linear_schedule_with_warmup(
												    optimizer=__lowerCamelCase			,     num_warmup_steps=0			,     num_training_steps=__lowerCamelCase			,     )
						else:
												_lowerCAmelCase  						=			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.
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			accelerator.prepare(
						    __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

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

						# Now we train the model
						_lowerCAmelCase  						=			{}
						for epoch in range(__lowerCamelCase			,     __lowerCamelCase		):
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		for step, batch in enumerate(__lowerCamelCase		):
																								_lowerCAmelCase  						=			model(**__lowerCamelCase		)
																								_lowerCAmelCase  						=			outputs.loss
																								_lowerCAmelCase  						=			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		)		)		)
												_lowerCAmelCase  						=			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 A				():
						_lowerCAmelCase  						=			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."""			,     )
						_lowerCAmelCase  						=			parser.parse_args()
						_lowerCAmelCase  						=			{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(__lowerCamelCase			,     __lowerCamelCase		)


if __name__ == "__main__":
						main()


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(_lowercase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()
												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
