



"""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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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)
#
# 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
#
########################################################################


A_						:    Dict				=      16
A_						:    List[str]				=      32
def  __snake_case							(	__A      :						Accelerator							,		__A      :						int = 16     )     ->       str:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     AutoTokenizer.from_pretrained('bert-base-cased'     )
				SCREAMING_SNAKE_CASE						:							List[str]							=     load_dataset('glue'							,		'mrpc'     )

				def tokenize_function(__A      :						List[str]     ):
								# max_length=None => use the model max length (it's actually the default)
								SCREAMING_SNAKE_CASE						:							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():
								SCREAMING_SNAKE_CASE						:							str							=     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
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     tokenized_datasets.rename_column('label'							,		'labels'     )

				def collate_fn(__A      :						int     ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								SCREAMING_SNAKE_CASE						:							Any							=     128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												SCREAMING_SNAKE_CASE						:							List[str]							=     16
								elif accelerator.mixed_precision != "no":
												SCREAMING_SNAKE_CASE						:							str							=     8
								else:
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     None

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

				# Instantiate dataloaders.
				SCREAMING_SNAKE_CASE						:							Any							=     DataLoader(
				    tokenized_datasets['train']							,		shuffle=__A							,		collate_fn=__A							,		batch_size=__A     )
				SCREAMING_SNAKE_CASE						:							int							=     DataLoader(
				    tokenized_datasets['validation']							,		shuffle=__A							,		collate_fn=__A							,		batch_size=__A     )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							A_						:    List[Any]				=      mocked_dataloaders  # noqa: F811
def  __snake_case							(	__A      :						List[str]							,		__A      :						str     )     ->       Dict:






				'''simple docstring'''
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS'							,		__A     ) == "1":
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     2
				# New Code #
				SCREAMING_SNAKE_CASE						:							Tuple							=     int(args.gradient_accumulation_steps     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     int(args.local_sgd_steps     )
				# Initialize accelerator
				SCREAMING_SNAKE_CASE						:							Any							=     Accelerator(
				    cpu=args.cpu							,		mixed_precision=args.mixed_precision							,		gradient_accumulation_steps=__A     )
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     config['lr']
				SCREAMING_SNAKE_CASE						:							str							=     int(config['num_epochs']     )
				SCREAMING_SNAKE_CASE						:							List[str]							=     int(config['seed']     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     int(config['batch_size']     )

				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     evaluate.load('glue'							,		'mrpc'     )

				set_seed(__A     )
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Any							=     get_dataloaders(__A							,		__A     )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				SCREAMING_SNAKE_CASE						:							Tuple							=     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).
				SCREAMING_SNAKE_CASE						:							int							=     model.to(accelerator.device     )

				# Instantiate optimizer
				SCREAMING_SNAKE_CASE						:							List[Any]							=     AdamW(params=model.parameters()							,		lr=__A     )

				# Instantiate scheduler
				SCREAMING_SNAKE_CASE						:							Tuple							=     get_linear_schedule_with_warmup(
				    optimizer=__A							,		num_warmup_steps=100							,		num_training_steps=(len(__A     ) * num_epochs)							,		)

				# 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.
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     accelerator.prepare(
				    __A							,		__A							,		__A							,		__A							,		__A     )

				# Now we train the model
				for epoch in range(__A     ):
								model.train()
								with LocalSGD(
								    accelerator=__A							,		model=__A							,		local_sgd_steps=__A							,		enabled=local_sgd_steps is not None     ) as local_sgd:
												for step, batch in enumerate(__A     ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device     )
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(__A     ):
																				SCREAMING_SNAKE_CASE						:							Dict							=     model(**__A     )
																				SCREAMING_SNAKE_CASE						:							Tuple							=     output.loss
																				accelerator.backward(__A     )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								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():
																SCREAMING_SNAKE_CASE						:							List[str]							=     model(**__A     )
												SCREAMING_SNAKE_CASE						:							Any							=     outputs.logits.argmax(dim=-1     )
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[Any]							=     accelerator.gather_for_metrics((predictions, batch['labels'])     )
												metric.add_batch(
												    predictions=__A							,		references=__A							,		)

								SCREAMING_SNAKE_CASE						:							List[str]							=     metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""							,		__A     )
def  __snake_case							(	)     ->       Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							int							=     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.'							,		)
				# New Code #
				parser.add_argument(
				    '--gradient_accumulation_steps'							,		type=__A							,		default=1							,		help='The number of minibatches to be ran before gradients are accumulated.'							,		)
				parser.add_argument(
				    '--local_sgd_steps'							,		type=__A							,		default=8							,		help='Number of local SGD steps or None to disable local SGD'     )
				parser.add_argument('--cpu'							,		action='store_true'							,		help='If passed, will train on the CPU.'     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     parser.parse_args()
				SCREAMING_SNAKE_CASE						:							Tuple							=     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(__A							,		__A     )


if __name__ == "__main__":
							main()










"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A_						:    Union[str, Any]				=      False
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	pass




@nightly
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[str]:





					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Dict							=     VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     generator.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					assert np.abs(image - new_image    ).sum() < 1E-5, "Models don't have the same forward pass"







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     VersatileDiffusionTextToImagePipeline.from_pretrained(
					    'shi-labs/versatile-diffusion'     ,       torch_dtype=torch.floataa    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=50     ,       output_type='numpy'    ).images

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





