


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class     __snake_case							(      _UpperCAmelCase  ,			_UpperCAmelCase  ,			_UpperCAmelCase  ,			unittest.TestCase):
					SCREAMING_SNAKE_CASE_		:					Optional[int] 				= StableDiffusionImageVariationPipeline
					SCREAMING_SNAKE_CASE_		:					List[str] 				= IMAGE_VARIATION_PARAMS
					SCREAMING_SNAKE_CASE_		:					Any 				= IMAGE_VARIATION_BATCH_PARAMS
					SCREAMING_SNAKE_CASE_		:					List[Any] 				= frozenset([])
					# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					SCREAMING_SNAKE_CASE_		:					List[str] 				= frozenset([])
					def   a (	self					:	int     )       ->				int:



											'''simple docstring'''





											torch.manual_seed(0     )
											__magic_name__ 		=			UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)  ,			layers_per_block=2  ,			sample_size=3_2  ,			in_channels=4  ,			out_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,			cross_attention_dim=3_2  ,			)
											__magic_name__ 		=			PNDMScheduler(skip_prk_steps=_lowerCAmelCase     )
											torch.manual_seed(0     )
											__magic_name__ 		=			AutoencoderKL(
											    block_out_channels=[3_2, 6_4]  ,			in_channels=3  ,			out_channels=3  ,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,			latent_channels=4  ,			)
											torch.manual_seed(0     )
											__magic_name__ 		=			CLIPVisionConfig(
											    hidden_size=3_2  ,			projection_dim=3_2  ,			intermediate_size=3_7  ,			layer_norm_eps=1E-0_5  ,			num_attention_heads=4  ,			num_hidden_layers=5  ,			image_size=3_2  ,			patch_size=4  ,			)
											__magic_name__ 		=			CLIPVisionModelWithProjection(_lowerCAmelCase     )
											__magic_name__ 		=			CLIPImageProcessor(crop_size=3_2  ,			size=3_2     )

											__magic_name__ 		=			{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'image_encoder': image_encoder,
											    'feature_extractor': feature_extractor,
											    'safety_checker': None,
											}
											return components
					def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	Optional[int]=0     )       ->				int:



											'''simple docstring'''





											__magic_name__ 		=			floats_tensor((1, 3, 3_2, 3_2)  ,			rng=random.Random(_lowerCAmelCase     )     )
											__magic_name__ 		=			image.cpu().permute(0  ,			2  ,			3  ,			1     )[0]
											__magic_name__ 		=			Image.fromarray(np.uinta(_lowerCAmelCase     )     ).convert('RGB'     ).resize((3_2, 3_2)     )
											if str(_lowerCAmelCase     ).startswith('mps'     ):
																	__magic_name__ 		=			torch.manual_seed(_lowerCAmelCase     )
											else:
																	__magic_name__ 		=			torch.Generator(device=_lowerCAmelCase     ).manual_seed(_lowerCAmelCase     )
											__magic_name__ 		=			{
											    'image': image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs
					def   a (	self					:	Any     )       ->				Union[str, Any]:



											'''simple docstring'''





											__magic_name__ 		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
											__magic_name__ 		=			self.get_dummy_components()
											__magic_name__ 		=			StableDiffusionImageVariationPipeline(**_lowerCAmelCase     )
											__magic_name__ 		=			sd_pipe.to(_lowerCAmelCase     )
											sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

											__magic_name__ 		=			self.get_dummy_inputs(_lowerCAmelCase     )
											__magic_name__ 		=			sd_pipe(**_lowerCAmelCase     ).images
											__magic_name__ 		=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__magic_name__ 		=			np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]     )

											assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3
					def   a (	self					:	Union[str, Any]     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
											__magic_name__ 		=			self.get_dummy_components()
											__magic_name__ 		=			StableDiffusionImageVariationPipeline(**_lowerCAmelCase     )
											__magic_name__ 		=			sd_pipe.to(_lowerCAmelCase     )
											sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

											__magic_name__ 		=			self.get_dummy_inputs(_lowerCAmelCase     )
											__magic_name__ 		=			2 * [inputs['image']]
											__magic_name__ 		=			sd_pipe(**_lowerCAmelCase     )

											__magic_name__ 		=			output.images

											__magic_name__ 		=			image[-1, -3:, -3:, -1]

											assert image.shape == (2, 6_4, 6_4, 3)
											__magic_name__ 		=			np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]     )

											assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3







					def   a (	self					:	Any     )       ->				Dict:



											'''simple docstring'''





											super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







@slow
@require_torch_gpu
class     __snake_case							(      unittest.TestCase):
					def   a (	self					:	Any     )       ->				List[Any]:



											'''simple docstring'''





											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
					def   a (	self					:	int  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Any="cpu"  ,			_lowerCAmelCase					:	Optional[int]=torch.floataa  ,			_lowerCAmelCase					:	Optional[Any]=0     )       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			torch.Generator(device=_lowerCAmelCase     ).manual_seed(_lowerCAmelCase     )
											__magic_name__ 		=			load_image(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
											    '/stable_diffusion_imgvar/input_image_vermeer.png'     )
											__magic_name__ 		=			np.random.RandomState(_lowerCAmelCase     ).standard_normal((1, 4, 6_4, 6_4)     )
											__magic_name__ 		=			torch.from_numpy(_lowerCAmelCase     ).to(device=_lowerCAmelCase  ,			dtype=_lowerCAmelCase     )
											__magic_name__ 		=			{
											    'image': init_image,
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'guidance_scale': 7.5,
											    'output_type': 'numpy',
											}
											return inputs
					def   a (	self					:	Union[str, Any]     )       ->				Optional[Any]:



											'''simple docstring'''





											__magic_name__ 		=			StableDiffusionImageVariationPipeline.from_pretrained(
											    'lambdalabs/sd-image-variations-diffusers'  ,			safety_checker=_lowerCAmelCase     )
											__magic_name__ 		=			sd_pipe.to(_lowerCAmelCase     )
											sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

											__magic_name__ 		=			self.get_inputs(_lowerCAmelCase     )
											__magic_name__ 		=			sd_pipe(**_lowerCAmelCase     ).images
											__magic_name__ 		=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__ 		=			np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379]     )
											assert np.abs(image_slice - expected_slice     ).max() < 6E-3
					def   a (	self					:	Optional[int]     )       ->				int:



											'''simple docstring'''





											__magic_name__ 		=			0

											def callback_fn(_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	torch.FloatTensor     ) -> None:
																	__magic_name__ 		=			True
																	nonlocal number_of_steps
																	number_of_steps += 1
																	if step == 1:
																							__magic_name__ 		=			latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 6_4, 6_4)
																							__magic_name__ 		=			latents[0, -3:, -3:, -1]
																							__magic_name__ 		=			np.array(
																							    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]     )

																							assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
																	elif step == 2:
																							__magic_name__ 		=			latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 6_4, 6_4)
																							__magic_name__ 		=			latents[0, -3:, -3:, -1]
																							__magic_name__ 		=			np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]     )

																							assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2

											__magic_name__ 		=			False

											__magic_name__ 		=			StableDiffusionImageVariationPipeline.from_pretrained(
											    'fusing/sd-image-variations-diffusers'  ,			safety_checker=_lowerCAmelCase  ,			torch_dtype=torch.floataa  ,			)
											pipe.to(_lowerCAmelCase     )
											pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
											pipe.enable_attention_slicing()

											__magic_name__ 		=			self.get_inputs(_lowerCAmelCase  ,			dtype=torch.floataa     )
											pipe(**_lowerCAmelCase  ,			callback=_lowerCAmelCase  ,			callback_steps=1     )
											assert callback_fn.has_been_called
											assert number_of_steps == inputs["num_inference_steps"]







					def   a (	self					:	Optional[Any]     )       ->				Any:



											'''simple docstring'''





											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__magic_name__ 		=			'fusing/sd-image-variations-diffusers'
											__magic_name__ 		=			StableDiffusionImageVariationPipeline.from_pretrained(
											    _lowerCAmelCase  ,			safety_checker=_lowerCAmelCase  ,			torch_dtype=torch.floataa     )
											__magic_name__ 		=			pipe.to(_lowerCAmelCase     )
											pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
											pipe.enable_attention_slicing(1     )
											pipe.enable_sequential_cpu_offload()

											__magic_name__ 		=			self.get_inputs(_lowerCAmelCase  ,			dtype=torch.floataa     )
											__magic_name__ 		=			pipe(**_lowerCAmelCase     )

											__magic_name__ 		=			torch.cuda.max_memory_allocated()
											# make sure that less than 2.6 GB is allocated
											assert mem_bytes < 2.6 * 1_0**9







@nightly
@require_torch_gpu
class     __snake_case							(      unittest.TestCase):
					def   a (	self					:	int     )       ->				str:



											'''simple docstring'''





											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
					def   a (	self					:	Tuple  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	str="cpu"  ,			_lowerCAmelCase					:	str=torch.floataa  ,			_lowerCAmelCase					:	Any=0     )       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			torch.Generator(device=_lowerCAmelCase     ).manual_seed(_lowerCAmelCase     )
											__magic_name__ 		=			load_image(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
											    '/stable_diffusion_imgvar/input_image_vermeer.png'     )
											__magic_name__ 		=			np.random.RandomState(_lowerCAmelCase     ).standard_normal((1, 4, 6_4, 6_4)     )
											__magic_name__ 		=			torch.from_numpy(_lowerCAmelCase     ).to(device=_lowerCAmelCase  ,			dtype=_lowerCAmelCase     )
											__magic_name__ 		=			{
											    'image': init_image,
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 5_0,
											    'guidance_scale': 7.5,
											    'output_type': 'numpy',
											}
											return inputs
					def   a (	self					:	Union[str, Any]     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'     )
											sd_pipe.to(_lowerCAmelCase     )
											sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

											__magic_name__ 		=			self.get_inputs(_lowerCAmelCase     )
											__magic_name__ 		=			sd_pipe(**_lowerCAmelCase     ).images[0]

											__magic_name__ 		=			load_numpy(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
											    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'     )
											__magic_name__ 		=			np.abs(expected_image - image     ).max()
											assert max_diff < 1E-3







					def   a (	self					:	str     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'     )
											__magic_name__ 		=			DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config     )
											sd_pipe.to(_lowerCAmelCase     )
											sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

											__magic_name__ 		=			self.get_inputs(_lowerCAmelCase     )
											__magic_name__ 		=			2_5
											__magic_name__ 		=			sd_pipe(**_lowerCAmelCase     ).images[0]

											__magic_name__ 		=			load_numpy(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
											    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'     )
											__magic_name__ 		=			np.abs(expected_image - image     ).max()
											assert max_diff < 1E-3





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
#
########################################################################


UpperCamelCase :				List[Any]						    =       16
UpperCamelCase :				Dict						    =       32
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Tuple     )->      List[Any]:




						'''simple docstring'''
						# Initialize accelerator
						if args.with_tracking:
												__magic_name__ 		=			Accelerator(
												    cpu=args.cpu	,			mixed_precision=args.mixed_precision	,			log_with='all'	,			project_dir=args.project_dir     )
						else:
												__magic_name__ 		=			Accelerator(cpu=args.cpu	,			mixed_precision=args.mixed_precision     )

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

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

						__magic_name__ 		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
						__magic_name__ 		=			load_dataset('glue'	,			'mrpc'     )
						__magic_name__ 		=			evaluate.load('glue'	,			'mrpc'     )

						def tokenize_function(__snake_case			:						Dict     ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__ 		=			tokenizer(examples['sentence1']	,			examples['sentence2']	,			truncation=__snake_case	,			max_length=__snake_case     )
												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():
												__magic_name__ 		=			datasets.map(
												    __snake_case	,			batched=__snake_case	,			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
						__magic_name__ 		=			tokenized_datasets.rename_column('label'	,			'labels'     )

						# If the batch size is too big we use gradient accumulation
						__magic_name__ 		=			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__magic_name__ 		=			batch_size // MAX_GPU_BATCH_SIZE
												__magic_name__ 		=			MAX_GPU_BATCH_SIZE

						def collate_fn(__snake_case			:						Tuple     ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__ 		=			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":
																		__magic_name__ 		=			16
												elif accelerator.mixed_precision != "no":
																		__magic_name__ 		=			8
												else:
																		__magic_name__ 		=			None

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

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

						set_seed(__snake_case     )

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

						# 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).
						__magic_name__ 		=			model.to(accelerator.device     )

						# Instantiate optimizer
						__magic_name__ 		=			AdamW(params=model.parameters()	,			lr=__snake_case     )

						# Instantiate scheduler
						__magic_name__ 		=			get_linear_schedule_with_warmup(
						    optimizer=__snake_case	,			num_warmup_steps=1_00	,			num_training_steps=(len(__snake_case     ) * 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.
						__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__ 		=			accelerator.prepare(
						    __snake_case	,			__snake_case	,			__snake_case	,			__snake_case	,			__snake_case     )

						# We need to keep track of how many total steps we have iterated over
						__magic_name__ 		=			0
						# We also need to keep track of the stating epoch so files are named properly
						__magic_name__ 		=			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     )
																		__magic_name__ 		=			os.path.basename(args.resume_from_checkpoint     )
												else:
																		# Get the most recent checkpoint
																		__magic_name__ 		=			[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime     )
																		__magic_name__ 		=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__magic_name__ 		=			os.path.splitext(__snake_case     )[0]

												if "epoch" in training_difference:
																		__magic_name__ 		=			int(training_difference.replace('epoch_'	,			''     )     ) + 1
																		__magic_name__ 		=			None
												else:
																		__magic_name__ 		=			int(training_difference.replace('step_'	,			''     )     )
																		__magic_name__ 		=			resume_step // len(__snake_case     )
																		resume_step -= starting_epoch * len(__snake_case     )

    # Now we train the model
						for epoch in range(__snake_case	,			__snake_case     ):
												model.train()
												if args.with_tracking:
																		__magic_name__ 		=			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
																		__magic_name__ 		=			accelerator.skip_first_batches(__snake_case	,			__snake_case     )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__magic_name__ 		=			train_dataloader
												for step, batch in enumerate(__snake_case     ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device     )
																		__magic_name__ 		=			model(**__snake_case     )
																		__magic_name__ 		=			outputs.loss
																		__magic_name__ 		=			loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(__snake_case     )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(__snake_case	,			__snake_case     ):
																								__magic_name__ 		=			F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																														accelerator.save_state(__snake_case     )

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

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

												if checkpointing_steps == "epoch":
																		__magic_name__ 		=			F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																		accelerator.save_state(__snake_case     )

						if args.with_tracking:
												accelerator.end_training()
def 							UpperCamelCase_  (				)->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			argparse.ArgumentParser(description='Simple example of training script.'     )
						parser.add_argument(
						    '--mixed_precision'	,			type=__snake_case	,			default=__snake_case	,			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=__snake_case	,			default=__snake_case	,			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=__snake_case	,			default=__snake_case	,			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=__snake_case	,			default='.'	,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,			)
						parser.add_argument(
						    '--project_dir'	,			type=__snake_case	,			default='logs'	,			help='Location on where to store experiment tracking logs` and relevent project information'	,			)
						__magic_name__ 		=			parser.parse_args()
						__magic_name__ 		=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__snake_case	,			__snake_case     )


if __name__ == "__main__":
					main()

