
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


_UpperCAmelCase	:     Tuple		       = 16
_UpperCAmelCase	:     Tuple		       = 32





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''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
											snake_case_           =					torch.cuda.memory_allocated()
											return self




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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__ = 16     ,							UpperCamelCase__ = "bert-base-cased"     ,							UpperCamelCase__ = 320     ,							UpperCamelCase__ = 160     ,							):







							'''simple docstring'''






							snake_case_           =					AutoTokenizer.from_pretrained(__UpperCamelCase   )
							snake_case_           =					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)
														snake_case_           =					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
							snake_case_           =					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
							snake_case_           =					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=128     ,							return_tensors='pt'   )
														return tokenizer.pad(__UpperCamelCase     ,							padding='longest'     ,							return_tensors='pt'   )

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

							return train_dataloader, eval_dataloader





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					Accelerator()

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

							set_seed(__UpperCamelCase   )
							snake_case_       ,					snake_case_           =					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)
							snake_case_           =					AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase     ,							return_dict=__UpperCamelCase   )

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

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

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

							# Now we train the model
							snake_case_           =					{}
							for epoch in range(__UpperCamelCase     ,							__UpperCamelCase   ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(__UpperCamelCase   ):
																												snake_case_           =					model(**__UpperCamelCase   )
																												snake_case_           =					outputs.loss
																												snake_case_           =					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   )   )   )
														snake_case_           =					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       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					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=320     ,							help='Number of training examples to use.'     ,							)
							parser.add_argument(
							    '--n_val'     ,							type=__UpperCamelCase     ,							default=160     ,							help='Number of validation examples to use.'     ,							)
							parser.add_argument(
							    '--num_epochs'     ,							type=__UpperCamelCase     ,							default=1     ,							help='Number of train epochs.'     ,							)
							snake_case_           =					parser.parse_args()
							snake_case_           =					{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
							training_function(__UpperCamelCase     ,							__UpperCamelCase   )


if __name__ == "__main__":
					main()







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




