"""simple docstring"""



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      ):







										'''simple docstring'''


										super().__init__()
										self.register_modules(
										    vae=snake_case_   ,      text_encoder=snake_case_   ,      tokenizer=snake_case_   ,      unet=snake_case_   ,      scheduler=snake_case_   ,      safety_checker=snake_case_   ,      feature_extractor=snake_case_   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_ = "auto"							):







										'''simple docstring'''


										if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	A__       :     List[str]								=			self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.enable_attention_slicing(snake_case_							)





			@torch.no_grad()
			def __call__(			self   ,      snake_case_   ,      snake_case_ = 512   ,      snake_case_ = 512   ,      snake_case_ = 50   ,      snake_case_ = 7.5   ,      snake_case_ = None   ,      snake_case_ = 1   ,      snake_case_ = 0.0   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = "pil"   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = 1   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										if isinstance(snake_case_   ,      snake_case_							):
																	A__       :     Optional[int]								=			1
										elif isinstance(snake_case_   ,      snake_case_							):
																	A__       :     List[Any]								=			len(snake_case_							)
										else:
																	raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case_							)}'''							)

										if height % 8 != 0 or width % 8 != 0:
																	raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(snake_case_   ,      snake_case_							) or callback_steps <= 0)
										):
																	raise ValueError(
																	    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																	    F''' {type(snake_case_							)}.'''							)

										# get prompt text embeddings
										A__       :     List[str]								=			self.tokenizer(
										    snake_case_   ,      padding="""max_length"""   ,      max_length=self.tokenizer.model_max_length   ,      return_tensors="""pt"""   ,      )
										A__       :     List[Any]								=			text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	A__       :     int								=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
																	logger.warning(
																	    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																	    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)
																	A__       :     Tuple								=			text_input_ids[:, : self.tokenizer.model_max_length]

										if text_embeddings is None:
																	A__       :     Any								=			self.text_encoder(text_input_ids.to(self.device							)							)[0]

										# duplicate text embeddings for each generation per prompt, using mps friendly method
										A__				,							A__				,							A__       :     str								=			text_embeddings.shape
										A__       :     List[str]								=			text_embeddings.repeat(1   ,      snake_case_   ,      1							)
										A__       :     int								=			text_embeddings.view(bs_embed * num_images_per_prompt   ,      snake_case_   ,      -1							)

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										A__       :     str								=			guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																	A__       :     List[str]
																	if negative_prompt is None:
																								A__       :     List[str]								=			[""""""]
																	elif type(snake_case_							) is not type(snake_case_							):
																								raise TypeError(
																								    F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_							)} !='''
																								    F''' {type(snake_case_							)}.'''							)
																	elif isinstance(snake_case_   ,      snake_case_							):
																								A__       :     int								=			[negative_prompt]
																	elif batch_size != len(snake_case_							):
																								raise ValueError(
																								    F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case_							)}, but `prompt`:'''
																								    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																								    """ the batch size of `prompt`."""							)
																	else:
																								A__       :     Tuple								=			negative_prompt

																	A__       :     Any								=			text_input_ids.shape[-1]
																	A__       :     Any								=			self.tokenizer(
																	    snake_case_   ,      padding="""max_length"""   ,      max_length=snake_case_   ,      truncation=snake_case_   ,      return_tensors="""pt"""   ,      )
																	A__       :     Optional[Any]								=			self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	A__       :     Union[str, Any]								=			uncond_embeddings.shape[1]
																	A__       :     List[str]								=			uncond_embeddings.repeat(snake_case_   ,      snake_case_   ,      1							)
																	A__       :     Optional[Any]								=			uncond_embeddings.view(batch_size * num_images_per_prompt   ,      snake_case_   ,      -1							)

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	A__       :     Tuple								=			torch.cat([uncond_embeddings, text_embeddings]							)

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										A__       :     Dict								=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										A__       :     Union[str, Any]								=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
										A__       :     Union[str, Any]								=			text_embeddings.dtype
										if latents is None:
																	if self.device.type == "mps":
																								# randn does not exist on mps
																								A__       :     str								=			torch.randn(
																								    snake_case_   ,      generator=snake_case_   ,      device="""cpu"""   ,      dtype=snake_case_							).to(self.device							)
																								A__       :     Optional[int]								=			torch.randn(snake_case_   ,      generator=snake_case_   ,      device="""cpu"""   ,      dtype=snake_case_							).to(
																								    self.device							)
																	else:
																								A__       :     Dict								=			torch.randn(
																								    snake_case_   ,      generator=snake_case_   ,      device=self.device   ,      dtype=snake_case_							)
																								A__       :     Optional[int]								=			torch.randn(snake_case_   ,      generator=snake_case_   ,      device=self.device   ,      dtype=snake_case_							)
										else:
																	if latents_reference.shape != latents_shape:
																								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
																	A__       :     List[str]								=			latents_reference.to(self.device							)
																	A__       :     Union[str, Any]								=			latents.to(self.device							)

										# This is the key part of the pipeline where we
										# try to ensure that the generated images w/ the same seed
										# but different sizes actually result in similar images
										A__       :     int								=			(latents_shape[3] - latents_shape_reference[3]) // 2
										A__       :     List[Any]								=			(latents_shape[2] - latents_shape_reference[2]) // 2
										A__       :     Dict								=			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
										A__       :     int								=			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
										A__       :     Optional[int]								=			0 if dx < 0 else dx
										A__       :     List[str]								=			0 if dy < 0 else dy
										A__       :     List[Any]								=			max(-dx   ,      0							)
										A__       :     Any								=			max(-dy   ,      0							)
										# import pdb
										# pdb.set_trace()
										A__       :     int								=			latents_reference[:, :, dy : dy + h, dx : dx + w]

										# set timesteps
										self.scheduler.set_timesteps(snake_case_							)

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										A__       :     Optional[Any]								=			self.scheduler.timesteps.to(self.device							)

										# scale the initial noise by the standard deviation required by the scheduler
										A__       :     int								=			latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										A__       :     int								=			"""eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
										A__       :     Optional[int]								=			{}
										if accepts_eta:
																	A__       :     Union[str, Any]								=			eta

										for i, t in enumerate(self.progress_bar(snake_case_							)							):
																	# expand the latents if we are doing classifier free guidance
																	A__       :     List[Any]								=			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
																	A__       :     Optional[int]								=			self.scheduler.scale_model_input(snake_case_   ,      snake_case_							)

																	# predict the noise residual
																	A__       :     Any								=			self.unet(snake_case_   ,      snake_case_   ,      encoder_hidden_states=snake_case_							).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																								A__				,							A__       :     int								=			noise_pred.chunk(2							)
																								A__       :     int								=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	A__       :     Optional[int]								=			self.scheduler.step(snake_case_   ,      snake_case_   ,      snake_case_   ,      **snake_case_							).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																								callback(snake_case_   ,      snake_case_   ,      snake_case_							)

										A__       :     Optional[Any]								=			1 / 0.1_82_15 * latents
										A__       :     Union[str, Any]								=			self.vae.decode(snake_case_							).sample

										A__       :     Tuple								=			(image / 2 + 0.5).clamp(0   ,      1							)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										A__       :     Dict								=			image.cpu().permute(0   ,      2   ,      3   ,      1							).float().numpy()

										if self.safety_checker is not None:
																	A__       :     Dict								=			self.feature_extractor(self.numpy_to_pil(snake_case_							)   ,      return_tensors="""pt"""							).to(
																	    self.device							)
																	A__				,							A__       :     Union[str, Any]								=			self.safety_checker(
																	    images=snake_case_   ,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
										else:
																	A__       :     Dict								=			None

										if output_type == "pil":
																	A__       :     Any								=			self.numpy_to_pil(snake_case_							)

										if not return_dict:
																	return (image, has_nsfw_concept)

										return StableDiffusionPipelineOutput(images=snake_case_   ,      nsfw_content_detected=snake_case_							)






"""simple docstring"""



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def  _A(		lowerCAmelCase							):
							A__       :     int								=			fname.split(os.path.sep							)[-1]
							return re.search(r"""^(.*)_\d+\.jpg$"""			,  lowerCAmelCase							).groups()[0]
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=None   ,      snake_case_=None							):







										'''simple docstring'''


										A__       :     Dict								=			file_names
										A__       :     str								=			image_transform
										A__       :     Dict								=			label_to_id





			def __len__(			self							):







										'''simple docstring'''


										return len(self.file_names							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.file_names[idx]
										A__       :     Optional[Any]								=			PIL.Image.open(snake_case_							)
										A__       :     str								=			raw_image.convert("""RGB"""							)
										if self.image_transform is not None:
																	A__       :     Optional[int]								=			self.image_transform(snake_case_							)
										A__       :     Dict								=			extract_label(snake_case_							)
										if self.label_to_id is not None:
																	A__       :     List[Any]								=			self.label_to_id[label]
										return {"image": image, "label": label}





def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							# Initialize accelerator
							if args.with_tracking:
														A__       :     List[Any]								=			Accelerator(
														    cpu=args.cpu			,  mixed_precision=args.mixed_precision			,  log_with="""all"""			,  project_dir=args.project_dir							)
							else:
														A__       :     Optional[Any]								=			Accelerator(cpu=args.cpu			,  mixed_precision=args.mixed_precision							)

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A__       :     List[Any]								=			config["""lr"""]
							A__       :     Any								=			int(config["""num_epochs"""]							)
							A__       :     List[Any]								=			int(config["""seed"""]							)
							A__       :     Tuple								=			int(config["""batch_size"""]							)
							A__       :     List[str]								=			config["""image_size"""]
							if not isinstance(lowerCAmelCase			,  (list, tuple)							):
														A__       :     List[str]								=			(image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps			,  """isdigit"""							):
														if args.checkpointing_steps == "epoch":
																					A__       :     List[Any]								=			args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A__       :     int								=			int(args.checkpointing_steps							)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
							else:
														A__       :     Any								=			None

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

							# Grab all the image filenames
							A__       :     Union[str, Any]								=			[os.path.join(args.data_dir			,  lowerCAmelCase							) for fname in os.listdir(args.data_dir							) if fname.endswith(""".jpg"""							)]

							# Build the label correspondences
							A__       :     int								=			[extract_label(lowerCAmelCase							) for fname in file_names]
							A__       :     Dict								=			list(set(lowerCAmelCase							)							)
							id_to_label.sort()
							A__       :     int								=			{lbl: i for i, lbl in enumerate(lowerCAmelCase							)}

							# Set the seed before splitting the data.
							np.random.seed(lowerCAmelCase							)
							torch.manual_seed(lowerCAmelCase							)
							torch.cuda.manual_seed_all(lowerCAmelCase							)

							# Split our filenames between train and validation
							A__       :     str								=			np.random.permutation(len(lowerCAmelCase							)							)
							A__       :     Optional[int]								=			int(0.8 * len(lowerCAmelCase							)							)
							A__       :     Union[str, Any]								=			random_perm[:cut]
							A__       :     Optional[int]								=			random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							A__       :     Union[str, Any]								=			Compose([RandomResizedCrop(lowerCAmelCase			,  scale=(0.5, 1.0)							), ToTensor()]							)
							A__       :     Dict								=			PetsDataset(
							    [file_names[i] for i in train_split]			,  image_transform=lowerCAmelCase			,  label_to_id=lowerCAmelCase							)

							# For evaluation, we use a deterministic Resize
							A__       :     Optional[int]								=			Compose([Resize(lowerCAmelCase							), ToTensor()]							)
							A__       :     Optional[Any]								=			PetsDataset([file_names[i] for i in eval_split]			,  image_transform=lowerCAmelCase			,  label_to_id=lowerCAmelCase							)

							# Instantiate dataloaders.
							A__       :     List[Any]								=			DataLoader(lowerCAmelCase			,  shuffle=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  num_workers=4							)
							A__       :     str								=			DataLoader(lowerCAmelCase			,  shuffle=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  num_workers=4							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__       :     Dict								=			create_model("""resnet50d"""			,  pretrained=lowerCAmelCase			,  num_classes=len(lowerCAmelCase							)							)

							# 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__       :     Tuple								=			model.to(accelerator.device							)

							# Freezing the base model
							for param in model.parameters():
														A__       :     Any								=			False
							for param in model.get_classifier().parameters():
														A__       :     Dict								=			True

							# We normalize the batches of images to be a bit faster.
							A__       :     List[Any]								=			torch.tensor(model.default_cfg["""mean"""]							)[None, :, None, None].to(accelerator.device							)
							A__       :     int								=			torch.tensor(model.default_cfg["""std"""]							)[None, :, None, None].to(accelerator.device							)

							# Instantiate optimizer
							A__       :     str								=			torch.optim.Adam(params=model.parameters()			,  lr=lr / 25							)

							# Instantiate learning rate scheduler
							A__       :     Optional[int]								=			OneCycleLR(optimizer=lowerCAmelCase			,  max_lr=lowerCAmelCase			,  epochs=lowerCAmelCase			,  steps_per_epoch=len(lowerCAmelCase							)							)

							# 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__       :     Dict								=			accelerator.prepare(
							    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							# We need to keep track of how many total steps we have iterated over
							A__       :     str								=			0
							# We also need to keep track of the starting 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__       :     Dict								=			os.path.basename(args.resume_from_checkpoint							)
														else:
																					# Get the most recent checkpoint
																					A__       :     Tuple								=			[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
																					dirs.sort(key=os.path.getctime							)
																					A__       :     Optional[Any]								=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A__       :     Optional[Any]								=			os.path.splitext(lowerCAmelCase							)[0]

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

    # Now we train the model
							for epoch in range(lowerCAmelCase			,  lowerCAmelCase							):
														model.train()
														if args.with_tracking:
																					A__       :     List[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(lowerCAmelCase			,  lowerCAmelCase							)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A__       :     Optional[int]								=			train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     Union[str, Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					A__       :     Tuple								=			model(lowerCAmelCase							)
																					A__       :     int								=			torch.nn.functional.cross_entropy(lowerCAmelCase			,  batch["""label"""]							)
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(lowerCAmelCase							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(lowerCAmelCase			,  lowerCAmelCase							):
																												A__       :     Union[str, Any]								=			F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A__       :     List[str]								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																																			accelerator.save_state(lowerCAmelCase							)
														model.eval()
														A__       :     Any								=			0
														A__       :     Tuple								=			0
														for step, batch in enumerate(lowerCAmelCase							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     List[Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					with torch.no_grad():
																												A__       :     Optional[int]								=			model(lowerCAmelCase							)
																					A__       :     Tuple								=			outputs.argmax(dim=-1							)
																					A__				,							A__       :     Tuple								=			accelerator.gather_for_metrics((predictions, batch["""label"""])							)
																					A__       :     int								=			predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														A__       :     List[Any]								=			accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''							)
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        """accuracy""": 100 * eval_metric,
																					        """train_loss""": total_loss.item() / len(lowerCAmelCase							),
																					        """epoch""": epoch,
																					    }			,  step=lowerCAmelCase			,  )
														if checkpointing_steps == "epoch":
																					A__       :     Any								=			F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												A__       :     Any								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																					accelerator.save_state(lowerCAmelCase							)

							if args.with_tracking:
														accelerator.end_training()

def  _A(		):
							A__       :     Union[str, Any]								=			argparse.ArgumentParser(description="""Simple example of training script."""							)
							parser.add_argument("""--data_dir"""			,  required=lowerCAmelCase			,  help="""The data folder on disk."""							)
							parser.add_argument("""--fp16"""			,  action="""store_true"""			,  help="""If passed, will use FP16 training."""							)
							parser.add_argument(
							    """--mixed_precision"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  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=lowerCAmelCase			,  default=lowerCAmelCase			,  help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,  )
							parser.add_argument(
							    """--output_dir"""			,  type=lowerCAmelCase			,  default="""."""			,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,  )
							parser.add_argument(
							    """--resume_from_checkpoint"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  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(
							    """--project_dir"""			,  type=lowerCAmelCase			,  default="""logs"""			,  help="""Location on where to store experiment tracking logs` and relevent project information"""			,  )
							A__       :     Optional[Any]								=			parser.parse_args()
							A__       :     Any								=			{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(lowerCAmelCase			,  lowerCAmelCase							)


if __name__ == "__main__":
				main()





