






"""simple docstring"""


import argparse
import os

import evaluate
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__SCREAMING_SNAKE_CASE      =      16
__SCREAMING_SNAKE_CASE      =      32


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						if args.with_tracking:
												lowerCAmelCase      :Optional[Any]				  = Accelerator(
												    cpu=args.cpu    ,			mixed_precision=args.mixed_precision    ,			log_with='all'    ,			project_dir=args.project_dir   )
						else:
												lowerCAmelCase      :Optional[int]				  = Accelerator(cpu=args.cpu    ,			mixed_precision=args.mixed_precision   )

						if hasattr(args.checkpointing_steps    ,			'isdigit'   ):
												if args.checkpointing_steps == "epoch":
																		lowerCAmelCase      :Tuple				  = args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		lowerCAmelCase      :Optional[int]				  = int(args.checkpointing_steps   )
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
						else:
												lowerCAmelCase      :Any				  = None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowerCAmelCase      :Dict				  = config['lr']
						lowerCAmelCase      :str				  = int(config['num_epochs']   )
						lowerCAmelCase      :List[str]				  = int(config['seed']   )
						lowerCAmelCase      :List[Any]				  = int(config['batch_size']   )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												lowerCAmelCase      :Dict				  = os.path.split(a__   )[-1].split('.'   )[0]
												accelerator.init_trackers(a__    ,			a__   )

						lowerCAmelCase      :int				  = AutoTokenizer.from_pretrained('bert-base-cased'   )
						lowerCAmelCase      :int				  = load_dataset('glue'    ,			'mrpc'   )
						lowerCAmelCase      :Union[str, Any]				  = evaluate.load('glue'    ,			'mrpc'   )

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

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												lowerCAmelCase      :Any				  = datasets.map(
												    a__    ,			batched=a__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						lowerCAmelCase      :str				  = tokenized_datasets.rename_column('label'    ,			'labels'   )

						# If the batch size is too big we use gradient accumulation
						lowerCAmelCase      :str				  = 1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowerCAmelCase      :Optional[Any]				  = batch_size // MAX_GPU_BATCH_SIZE
												lowerCAmelCase      :Tuple				  = MAX_GPU_BATCH_SIZE

						def collate_fn(a__   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowerCAmelCase      :Optional[Any]				  = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowerCAmelCase      :List[Any]				  = 16
												elif accelerator.mixed_precision != "no":
																		lowerCAmelCase      :Tuple				  = 8
												else:
																		lowerCAmelCase      :Optional[Any]				  = None

												return tokenizer.pad(
												    a__    ,			padding='longest'    ,			max_length=a__    ,			pad_to_multiple_of=a__    ,			return_tensors='pt'    ,			)

						# Instantiate dataloaders.
						lowerCAmelCase      :List[str]				  = DataLoader(
						    tokenized_datasets['train']    ,			shuffle=a__    ,			collate_fn=a__    ,			batch_size=a__   )
						lowerCAmelCase      :Optional[int]				  = DataLoader(
						    tokenized_datasets['validation']    ,			shuffle=a__    ,			collate_fn=a__    ,			batch_size=a__   )

						set_seed(a__   )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowerCAmelCase      :str				  = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,			return_dict=a__   )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						lowerCAmelCase      :Optional[Any]				  = model.to(accelerator.device   )

						# Instantiate optimizer
						lowerCAmelCase      :str				  = AdamW(params=model.parameters()    ,			lr=a__   )

						# Instantiate scheduler
						lowerCAmelCase      :int				  = get_linear_schedule_with_warmup(
						    optimizer=a__    ,			num_warmup_steps=1_00    ,			num_training_steps=(len(a__   ) * num_epochs) // gradient_accumulation_steps    ,			)

						# 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      :Any				  = accelerator.prepare(
						    a__    ,			a__    ,			a__    ,			a__    ,			a__   )

						# We need to keep track of how many total steps we have iterated over
						lowerCAmelCase      :int				  = 0
						# We also need to keep track of the stating epoch so files are named properly
						lowerCAmelCase      :Optional[int]				  = 0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""   )
																		accelerator.load_state(args.resume_from_checkpoint   )
																		lowerCAmelCase      :Optional[Any]				  = os.path.basename(args.resume_from_checkpoint   )
												else:
																		# Get the most recent checkpoint
																		lowerCAmelCase      :int				  = [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime   )
																		lowerCAmelCase      :str				  = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												lowerCAmelCase      :int				  = os.path.splitext(a__   )[0]

												if "epoch" in training_difference:
																		lowerCAmelCase      :Optional[int]				  = int(training_difference.replace('epoch_'    ,			''   )   ) + 1
																		lowerCAmelCase      :Optional[int]				  = None
												else:
																		lowerCAmelCase      :int				  = int(training_difference.replace('step_'    ,			''   )   )
																		lowerCAmelCase      :Any				  = resume_step // len(a__   )
																		resume_step -= starting_epoch * len(a__   )

    # Now we train the model
						for epoch in range(a__    ,			a__   ):
												model.train()
												if args.with_tracking:
																		lowerCAmelCase      :Tuple				  = 0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		lowerCAmelCase      :Optional[int]				  = accelerator.skip_first_batches(a__    ,			a__   )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		lowerCAmelCase      :Any				  = train_dataloader
												for step, batch in enumerate(a__   ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device   )
																		lowerCAmelCase      :Any				  = model(**a__   )
																		lowerCAmelCase      :Optional[int]				  = outputs.loss
																		lowerCAmelCase      :int				  = loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(a__   )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(a__    ,			a__   ):
																								lowerCAmelCase      :List[Any]				  = F"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				lowerCAmelCase      :Dict				  = os.path.join(args.output_dir    ,			a__   )
																														accelerator.save_state(a__   )

												model.eval()
												for step, batch in enumerate(a__   ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device   )
																		with torch.no_grad():
																								lowerCAmelCase      :int				  = model(**a__   )
																		lowerCAmelCase      :List[Any]				  = outputs.logits.argmax(dim=-1   )
																		lowerCAmelCase  ,   lowerCAmelCase      :str				  = accelerator.gather_for_metrics((predictions, batch['labels'])   )
																		metric.add_batch(
																		    predictions=a__    ,			references=a__    ,			)

												lowerCAmelCase      :Dict				  = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""    ,			a__   )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': eval_metric['accuracy'],
																		        'f1': eval_metric['f1'],
																		        'train_loss': total_loss.item() / len(a__   ),
																		        'epoch': epoch,
																		    }    ,			step=a__    ,			)

												if checkpointing_steps == "epoch":
																		lowerCAmelCase      :List[str]				  = F"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								lowerCAmelCase      :Dict				  = os.path.join(args.output_dir    ,			a__   )
																		accelerator.save_state(a__   )

						if args.with_tracking:
												accelerator.end_training()


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :Union[str, Any]				  = argparse.ArgumentParser(description='Simple example of training script.'   )
						parser.add_argument(
						    '--mixed_precision'    ,			type=a__    ,			default=a__    ,			choices=['no', 'fp16', 'bf16', 'fp8']    ,			help='Whether to use mixed precision. Choose'
						    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
						    'and an Nvidia Ampere GPU.'    ,			)
						parser.add_argument('--cpu'    ,			action='store_true'    ,			help='If passed, will train on the CPU.'   )
						parser.add_argument(
						    '--checkpointing_steps'    ,			type=a__    ,			default=a__    ,			help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'    ,			)
						parser.add_argument(
						    '--resume_from_checkpoint'    ,			type=a__    ,			default=a__    ,			help='If the training should continue from a checkpoint folder.'    ,			)
						parser.add_argument(
						    '--with_tracking'    ,			action='store_true'    ,			help='Whether to load in all available experiment trackers from the environment and use them for logging.'    ,			)
						parser.add_argument(
						    '--output_dir'    ,			type=a__    ,			default='.'    ,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'    ,			)
						parser.add_argument(
						    '--project_dir'    ,			type=a__    ,			default='logs'    ,			help='Location on where to store experiment tracking logs` and relevent project information'    ,			)
						lowerCAmelCase      :int				  = parser.parse_args()
						lowerCAmelCase      :str				  = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(a__    ,			a__   )


if __name__ == "__main__":
		main()







"""simple docstring"""


import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = 'runwayml/stable-diffusion-v1-5'
											lowerCAmelCase      :Tuple				  = TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase    ,   torch_dtype=torch.floataa      ).to('cuda'      )
											lowerCAmelCase      :Union[str, Any]				  = DDIMScheduler.from_config(pipe.scheduler.config      )
											lowerCAmelCase      :str				  = torch.Generator(device='cuda'      ).manual_seed(0      )

											lowerCAmelCase      :Any				  = 'A bear is playing a guitar on Times Square'
											lowerCAmelCase      :Optional[Any]				  = pipe(prompt=UpperCAmelCase    ,   generator=UpperCAmelCase      ).images

											lowerCAmelCase      :List[str]				  = load_pt(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'      )

											assert_mean_pixel_difference(UpperCAmelCase    ,   UpperCAmelCase      )