






'''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	:			Tuple         =16
_A	:			str         =32





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase = 16      )      ->       Union[str, Any]:
							lowerCamelCase__					:	Union[str, Any]         =       AutoTokenizer.from_pretrained("""bert-base-cased"""      )
							lowerCamelCase__					:	Any         =       load_dataset("""glue"""				,			"""mrpc"""      )

							def tokenize_function(UpperCamelCase      ):
														# max_length=None => use the model max length (it's actually the default)
														lowerCamelCase__					:	List[Any]         =       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
							# starting with the main process first:
							with accelerator.main_process_first():
														lowerCamelCase__					:	Any         =       datasets.map(
														    UpperCamelCase				,			batched=UpperCamelCase				,			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__					:	Optional[Any]         =       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.
														lowerCamelCase__					:	int         =       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":
																					lowerCamelCase__					:	Optional[int]         =       16
														elif accelerator.mixed_precision != "no":
																					lowerCamelCase__					:	List[Any]         =       8
														else:
																					lowerCamelCase__					:	Dict         =       None

														return tokenizer.pad(
														    UpperCamelCase				,			padding="""longest"""				,			max_length=UpperCamelCase				,			pad_to_multiple_of=UpperCamelCase				,			return_tensors="""pt"""				,			)

							# Instantiate dataloaders.
							lowerCamelCase__					:	Union[str, Any]         =       DataLoader(
							    tokenized_datasets["""train"""]				,			shuffle=UpperCamelCase				,			collate_fn=UpperCamelCase				,			batch_size=UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       DataLoader(
							    tokenized_datasets["""validation"""]				,			shuffle=UpperCamelCase				,			collate_fn=UpperCamelCase				,			batch_size=UpperCamelCase      )

							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	:			Optional[int]         =mocked_dataloaders  # noqa: F811





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,			UpperCamelCase      ) == "1":
														lowerCamelCase__					:	List[Any]         =       2
							# New Code #
							lowerCamelCase__					:	Union[str, Any]         =       int(args.gradient_accumulation_steps      )
							lowerCamelCase__					:	List[str]         =       int(args.local_sgd_steps      )
							# Initialize accelerator
							lowerCamelCase__					:	Tuple         =       Accelerator(
							    cpu=args.cpu				,			mixed_precision=args.mixed_precision				,			gradient_accumulation_steps=UpperCamelCase      )
							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
							lowerCamelCase__					:	Any         =       config["""lr"""]
							lowerCamelCase__					:	List[str]         =       int(config["""num_epochs"""]      )
							lowerCamelCase__					:	Any         =       int(config["""seed"""]      )
							lowerCamelCase__					:	Dict         =       int(config["""batch_size"""]      )

							lowerCamelCase__					:	Union[str, Any]         =       evaluate.load("""glue"""				,			"""mrpc"""      )

							set_seed(UpperCamelCase      )
							lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       get_dataloaders(UpperCamelCase				,			UpperCamelCase      )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCamelCase__					:	int         =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,			return_dict=UpperCamelCase      )

							# 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__					:	List[Any]         =       model.to(accelerator.device      )

							# Instantiate optimizer
							lowerCamelCase__					:	Optional[Any]         =       AdamW(params=model.parameters()				,			lr=UpperCamelCase      )

							# Instantiate scheduler
							lowerCamelCase__					:	int         =       get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase				,			num_warmup_steps=100				,			num_training_steps=(len(UpperCamelCase      ) * 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.
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       accelerator.prepare(
							    UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )

							# Now we train the model
							for epoch in range(UpperCamelCase      ):
														model.train()
														with LocalSGD(
														    accelerator=UpperCamelCase				,			model=UpperCamelCase				,			local_sgd_steps=UpperCamelCase				,			enabled=local_sgd_steps is not None      ) as local_sgd:
																					for step, batch in enumerate(UpperCamelCase      ):
																												# 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(UpperCamelCase      ):
																																			lowerCamelCase__					:	str         =       model(**UpperCamelCase      )
																																			lowerCamelCase__					:	int         =       output.loss
																																			accelerator.backward(UpperCamelCase      )
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

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

														lowerCamelCase__					:	Any         =       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f'''epoch {epoch}:'''				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       ()      ->       Tuple:
							lowerCamelCase__					:	Optional[int]         =       argparse.ArgumentParser(description="""Simple example of training script."""      )
							parser.add_argument(
							    """--mixed_precision"""				,			type=UpperCamelCase				,			default=UpperCamelCase				,			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=UpperCamelCase				,			default=1				,			help="""The number of minibatches to be ran before gradients are accumulated."""				,			)
							parser.add_argument(
							    """--local_sgd_steps"""				,			type=UpperCamelCase				,			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."""      )
							lowerCamelCase__					:	Tuple         =       parser.parse_args()
							lowerCamelCase__					:	str         =       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(UpperCamelCase				,			UpperCamelCase      )


if __name__ == "__main__":
			main()









'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



@slow
@require_torch_gpu
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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

