


"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class snake_case   (	__UpperCAmelCase):
		__UpperCamelCase							     =      (DPMSolverMultistepScheduler,)
		__UpperCamelCase							     =      (("""num_inference_steps""", 25),)







		def 			a_			(       self   :							Optional[int]     , **a__   :							Any		)							->    Union[str, Any]:





						'''simple docstring'''







						_A       	=      {
						    '''num_train_timesteps''': 10_00,
						    '''beta_start''': 0.0_0_0_1,
						    '''beta_end''': 0.0_2,
						    '''beta_schedule''': '''linear''',
						    '''solver_order''': 2,
						    '''prediction_type''': '''epsilon''',
						    '''thresholding''': False,
						    '''sample_max_value''': 1.0,
						    '''algorithm_type''': '''dpmsolver++''',
						    '''solver_type''': '''midpoint''',
						    '''lower_order_final''': False,
						    '''lambda_min_clipped''': -float("inf"		),
						    '''variance_type''': None,
						}

						config.update(**_lowerCamelCase		)
						return config







		def 			a_			(       self   :							int     , a__   :							List[str]=0     , **a__   :							Union[str, Any]		)							->    str:





						'''simple docstring'''







						_A       	=      dict(self.forward_default_kwargs		)
						_A       	=      kwargs.pop("num_inference_steps"     , _lowerCamelCase		)
						_A       	=      self.dummy_sample
						_A       	=      0.1 * sample
						_A       	=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
										_A       	=      self.get_scheduler_config(**_lowerCamelCase		)
										_A       	=      scheduler_class(**_lowerCamelCase		)
										scheduler.set_timesteps(_lowerCamelCase		)
										# copy over dummy past residuals
										_A       	=      dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_lowerCamelCase		)
														_A       	=      scheduler_class.from_pretrained(_lowerCamelCase		)
														new_scheduler.set_timesteps(_lowerCamelCase		)
														# copy over dummy past residuals
														_A       	=      dummy_past_residuals[: new_scheduler.config.solver_order]

										_A       	=      sample, sample
										for t in range(_lowerCamelCase     , time_step + scheduler.config.solver_order + 1		):
														_A       	=      scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase		).prev_sample
														_A       	=      new_scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







		def 			a_			(       self   :							Union[str, Any]		)							->    List[str]:





						'''simple docstring'''







						pass







		def 			a_			(       self   :							int     , a__   :							Any=0     , **a__   :							List[str]		)							->    int:





						'''simple docstring'''







						_A       	=      dict(self.forward_default_kwargs		)
						_A       	=      kwargs.pop("num_inference_steps"     , _lowerCamelCase		)
						_A       	=      self.dummy_sample
						_A       	=      0.1 * sample
						_A       	=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
										_A       	=      self.get_scheduler_config()
										_A       	=      scheduler_class(**_lowerCamelCase		)
										scheduler.set_timesteps(_lowerCamelCase		)

										# copy over dummy past residuals (must be after setting timesteps)
										_A       	=      dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_lowerCamelCase		)
														_A       	=      scheduler_class.from_pretrained(_lowerCamelCase		)
														# copy over dummy past residuals
														new_scheduler.set_timesteps(_lowerCamelCase		)

														# copy over dummy past residual (must be after setting timesteps)
														_A       	=      dummy_past_residuals[: new_scheduler.config.solver_order]

										_A       	=      scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase		).prev_sample
										_A       	=      new_scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







		def 			a_			(       self   :							Any     , a__   :							Optional[int]=None     , **a__   :							Optional[int]		)							->    Tuple:





						'''simple docstring'''







						if scheduler is None:
										_A       	=      self.scheduler_classes[0]
										_A       	=      self.get_scheduler_config(**_lowerCamelCase		)
										_A       	=      scheduler_class(**_lowerCamelCase		)

						_A       	=      10
						_A       	=      self.dummy_model()
						_A       	=      self.dummy_sample_deter
						scheduler.set_timesteps(_lowerCamelCase		)

						for i, t in enumerate(scheduler.timesteps		):
										_A       	=      model(_lowerCamelCase     , _lowerCamelCase		)
										_A       	=      scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase		).prev_sample

						return sample







		def 			a_			(       self   :							Dict		)							->    Tuple:





						'''simple docstring'''







						_A       	=      dict(self.forward_default_kwargs		)

						_A       	=      kwargs.pop("num_inference_steps"     , _lowerCamelCase		)

						for scheduler_class in self.scheduler_classes:
										_A       	=      self.get_scheduler_config()
										_A       	=      scheduler_class(**_lowerCamelCase		)

										_A       	=      self.dummy_sample
										_A       	=      0.1 * sample

										if num_inference_steps is not None and hasattr(_lowerCamelCase     , "set_timesteps"		):
														scheduler.set_timesteps(_lowerCamelCase		)
										elif num_inference_steps is not None and not hasattr(_lowerCamelCase     , "set_timesteps"		):
														_A       	=      num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										_A       	=      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
										_A       	=      dummy_past_residuals[: scheduler.config.solver_order]

										_A       	=      scheduler.timesteps[5]
										_A       	=      scheduler.timesteps[6]

										_A       	=      scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase		).prev_sample
										_A       	=      scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase		).prev_sample

										self.assertEqual(output_a.shape     , sample.shape		)
										self.assertEqual(output_a.shape     , output_a.shape		)







		def 			a_			(       self   :							Optional[Any]		)							->    Tuple:





						'''simple docstring'''







						for timesteps in [25, 50, 1_00, 9_99, 10_00]:
										self.check_over_configs(num_train_timesteps=_lowerCamelCase		)







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





						'''simple docstring'''







						self.check_over_configs(thresholding=_lowerCamelCase		)
						for order in [1, 2, 3]:
										for solver_type in ["midpoint", "heun"]:
														for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    thresholding=_lowerCamelCase     , prediction_type=_lowerCamelCase     , sample_max_value=_lowerCamelCase     , algorithm_type="dpmsolver++"     , solver_order=_lowerCamelCase     , solver_type=_lowerCamelCase     , )







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





						'''simple docstring'''







						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_lowerCamelCase		)







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





						'''simple docstring'''







						for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
										for solver_type in ["midpoint", "heun"]:
														for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																						if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																										if order == 3:
																														continue
																						else:
																										self.check_over_configs(
																										    solver_order=_lowerCamelCase     , solver_type=_lowerCamelCase     , prediction_type=_lowerCamelCase     , algorithm_type=_lowerCamelCase     , )
																						_A       	=      self.full_loop(
																						    solver_order=_lowerCamelCase     , solver_type=_lowerCamelCase     , prediction_type=_lowerCamelCase     , algorithm_type=_lowerCamelCase     , )
																						assert not torch.isnan(_lowerCamelCase		).any(), "Samples have nan numbers"







		def 			a_			(       self   :							Union[str, Any]		)							->    List[Any]:





						'''simple docstring'''







						self.check_over_configs(lower_order_final=_lowerCamelCase		)
						self.check_over_configs(lower_order_final=_lowerCamelCase		)







		def 			a_			(       self   :							Tuple		)							->    Any:





						'''simple docstring'''







						self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
						self.check_over_configs(lambda_min_clipped=-5.1		)







		def 			a_			(       self   :							Optional[Any]		)							->    Optional[int]:





						'''simple docstring'''







						self.check_over_configs(variance_type=_lowerCamelCase		)
						self.check_over_configs(variance_type="learned_range"		)







		def 			a_			(       self   :							Optional[int]		)							->    str:





						'''simple docstring'''







						for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
										self.check_over_forward(num_inference_steps=_lowerCamelCase     , time_step=0		)







		def 			a_			(       self   :							int		)							->    Any:





						'''simple docstring'''







						_A       	=      self.full_loop()
						_A       	=      torch.mean(torch.abs(_lowerCamelCase		)		)

						assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3







		def 			a_			(       self   :							Tuple		)							->    str:





						'''simple docstring'''







						_A       	=      self.full_loop(thresholding=_lowerCamelCase     , dynamic_thresholding_ratio=0.8_7     , sample_max_value=0.5		)
						_A       	=      torch.mean(torch.abs(_lowerCamelCase		)		)

						assert abs(result_mean.item() - 1.1_3_6_4		) < 1E-3







		def 			a_			(       self   :							Optional[int]		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      self.full_loop(prediction_type="v_prediction"		)
						_A       	=      torch.mean(torch.abs(_lowerCamelCase		)		)

						assert abs(result_mean.item() - 0.2_2_5_1		) < 1E-3







		def 			a_			(       self   :							Tuple		)							->    Dict:





						'''simple docstring'''







						_A       	=      self.full_loop(prediction_type="v_prediction"     , use_karras_sigmas=_lowerCamelCase		)
						_A       	=      torch.mean(torch.abs(_lowerCamelCase		)		)

						assert abs(result_mean.item() - 0.2_0_9_6		) < 1E-3







		def 			a_			(       self   :							Any		)							->    Dict:





						'''simple docstring'''







						_A       	=      DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
						_A       	=      self.full_loop(scheduler=_lowerCamelCase		)
						_A       	=      torch.mean(torch.abs(_lowerCamelCase		)		)

						assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3

						_A       	=      DPMSolverSinglestepScheduler.from_config(scheduler.config		)
						_A       	=      UniPCMultistepScheduler.from_config(scheduler.config		)
						_A       	=      DEISMultistepScheduler.from_config(scheduler.config		)
						_A       	=      DPMSolverMultistepScheduler.from_config(scheduler.config		)

						_A       	=      self.full_loop(scheduler=_lowerCamelCase		)
						_A       	=      torch.mean(torch.abs(_lowerCamelCase		)		)

						assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3







		def 			a_			(       self   :							List[str]		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      self.scheduler_classes[0]
						_A       	=      self.get_scheduler_config(thresholding=_lowerCamelCase     , dynamic_thresholding_ratio=0		)
						_A       	=      scheduler_class(**_lowerCamelCase		)

						_A       	=      10
						_A       	=      self.dummy_model()
						_A       	=      self.dummy_sample_deter.half()
						scheduler.set_timesteps(_lowerCamelCase		)

						for i, t in enumerate(scheduler.timesteps		):
										_A       	=      model(_lowerCamelCase     , _lowerCamelCase		)
										_A       	=      scheduler.step(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase		).prev_sample

						assert sample.dtype == torch.floataa







		def 			a_			(       self   :							Optional[int]     , **a__   :							Union[str, Any]		)							->    Optional[Any]:





						'''simple docstring'''







						for scheduler_class in self.scheduler_classes:
										_A       	=      self.get_scheduler_config(**_lowerCamelCase		)
										_A       	=      scheduler_class(**_lowerCamelCase		)

										scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
										assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps





"""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
#
########################################################################


_lowerCamelCase	:      Dict   =							16
_lowerCamelCase	:      Union[str, Any]   =							32

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






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

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

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

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

	def tokenize_function(_UpperCAmelCase						):
		# max_length=None => use the model max length (it's actually the default)
		A_	:					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():
		A_	:					List[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
	A_	:					Any						 =		tokenized_datasets.rename_column('''label'''	,   '''labels'''						)

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

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

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

	# Instantiate dataloaders.
	A_	:					List[Any]						 =		DataLoader(
	    tokenized_datasets['''train''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)
	A_	:					Optional[int]						 =		DataLoader(
	    tokenized_datasets['''validation''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)

	set_seed(_UpperCAmelCase						)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_	:					Optional[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).
	A_	:					str						 =		model.to(accelerator.device						)

	# Instantiate optimizer
	A_	:					str						 =		AdamW(params=model.parameters()	,   lr=_UpperCAmelCase						)

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

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

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

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

			overall_step += 1

			if isinstance(_UpperCAmelCase	,   _UpperCAmelCase						):
				A_	:					List[Any]						 =		f"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						A_	:					Tuple						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
					accelerator.save_state(_UpperCAmelCase						)

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

		A_	:					Union[str, Any]						 =		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,   _UpperCAmelCase						)
		if args.with_tracking:
			accelerator.log(
			    {
			        '''accuracy''': eval_metric['''accuracy'''],
			        '''f1''': eval_metric['''f1'''],
			        '''train_loss''': total_loss.item() / len(_UpperCAmelCase						),
			        '''epoch''': epoch,
			    }	,   step=_UpperCAmelCase	,   )

		if checkpointing_steps == "epoch":
			A_	:					str						 =		f"""epoch_{epoch}"""
			if args.output_dir is not None:
				A_	:					int						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
			accelerator.save_state(_UpperCAmelCase						)

	if args.with_tracking:
		accelerator.end_training()

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		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.'''	,   )
	parser.add_argument('''--cpu'''	,   action='''store_true'''	,   help='''If passed, will train on the CPU.'''						)
	parser.add_argument(
	    '''--checkpointing_steps'''	,   type=_UpperCAmelCase	,   default=_UpperCAmelCase	,   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=_UpperCAmelCase	,   default=_UpperCAmelCase	,   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=_UpperCAmelCase	,   default='''.'''	,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,   )
	parser.add_argument(
	    '''--project_dir'''	,   type=_UpperCAmelCase	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
	A_	:					Any						 =		parser.parse_args()
	A_	:					List[Any]						 =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_UpperCAmelCase	,   _UpperCAmelCase						)


if __name__ == "__main__":
		main()





