






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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


lowerCamelCase			:					Union[str, Any]         =	logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Optional[int]:
           super().__init__()

           if safety_checker is None:
                logger.warning(
                    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
                    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."	)

           self.register_modules(
               speech_model=__UpperCamelCase					,						speech_processor=__UpperCamelCase					,						vae=__UpperCamelCase					,						text_encoder=__UpperCamelCase					,						tokenizer=__UpperCamelCase					,						unet=__UpperCamelCase					,						scheduler=__UpperCamelCase					,						feature_extractor=__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase = "auto"	)->	List[str]:
           if slice_size == "auto":
                __lowerCAmelCase								=					self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           self.enable_attention_slicing(__UpperCamelCase	)


      @torch.no_grad()
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase=1_6_0_0_0					,						__UpperCamelCase = 5_1_2					,						__UpperCamelCase = 5_1_2					,						__UpperCamelCase = 5_0					,						__UpperCamelCase = 7.5					,						__UpperCamelCase = None					,						__UpperCamelCase = 1					,						__UpperCamelCase = 0.0					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = "pil"					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						__UpperCamelCase = 1					,						**__UpperCamelCase					,						)->	str:
           __lowerCAmelCase								=					self.speech_processor.feature_extractor(
               __UpperCamelCase					,						return_tensors="pt"					,						sampling_rate=__UpperCamelCase	).input_features.to(self.device	)
           __lowerCAmelCase								=					self.speech_model.generate(__UpperCamelCase					,						max_length=4_8_0_0_0_0	)

           __lowerCAmelCase								=					self.speech_processor.tokenizer.batch_decode(__UpperCamelCase					,						skip_special_tokens=__UpperCamelCase					,						normalize=__UpperCamelCase	)[
               0
           ]

           if isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                __lowerCAmelCase								=					1
           elif isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                __lowerCAmelCase								=					len(__UpperCamelCase	)
           else:
                raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase	)}"""	)

           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(__UpperCamelCase					,						__UpperCamelCase	) or callback_steps <= 0)
           ):
                raise ValueError(
                    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                    F""" {type(__UpperCamelCase	)}."""	)

           # get prompt text embeddings
           __lowerCAmelCase								=					self.tokenizer(
               __UpperCamelCase					,						padding="max_length"					,						max_length=self.tokenizer.model_max_length					,						return_tensors="pt"					,						)
           __lowerCAmelCase								=					text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                __lowerCAmelCase								=					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}"""	)
                __lowerCAmelCase								=					text_input_ids[:, : self.tokenizer.model_max_length]
           __lowerCAmelCase								=					self.text_encoder(text_input_ids.to(self.device	)	)[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					text_embeddings.shape
           __lowerCAmelCase								=					text_embeddings.repeat(1					,						__UpperCamelCase					,						1	)
           __lowerCAmelCase								=					text_embeddings.view(bs_embed * num_images_per_prompt					,						__UpperCamelCase					,						-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.
           __lowerCAmelCase								=					guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                __lowerCAmelCase								=					42
                if negative_prompt is None:
                     __lowerCAmelCase								=					[""] * batch_size
                elif type(__UpperCamelCase	) is not type(__UpperCamelCase	):
                     raise TypeError(
                         F"""`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCamelCase	)} !="""
                         F""" {type(__UpperCamelCase	)}."""	)
                elif isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                     __lowerCAmelCase								=					[negative_prompt]
                elif batch_size != len(__UpperCamelCase	):
                     raise ValueError(
                         F"""`negative_prompt`: {negative_prompt} has batch size {len(__UpperCamelCase	)}, but `prompt`:"""
                         F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                         " the batch size of `prompt`."	)
                else:
                     __lowerCAmelCase								=					negative_prompt

                __lowerCAmelCase								=					text_input_ids.shape[-1]
                __lowerCAmelCase								=					self.tokenizer(
                    __UpperCamelCase					,						padding="max_length"					,						max_length=__UpperCamelCase					,						truncation=__UpperCamelCase					,						return_tensors="pt"					,						)
                __lowerCAmelCase								=					self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                __lowerCAmelCase								=					uncond_embeddings.shape[1]
                __lowerCAmelCase								=					uncond_embeddings.repeat(1					,						__UpperCamelCase					,						1	)
                __lowerCAmelCase								=					uncond_embeddings.view(batch_size * num_images_per_prompt					,						__UpperCamelCase					,						-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
                __lowerCAmelCase								=					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`.
           __lowerCAmelCase								=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           __lowerCAmelCase								=					text_embeddings.dtype
           if latents is None:
                if self.device.type == "mps":
                     # randn does not exist on mps
                     __lowerCAmelCase								=					torch.randn(__UpperCamelCase					,						generator=__UpperCamelCase					,						device="cpu"					,						dtype=__UpperCamelCase	).to(
                         self.device	)
                else:
                     __lowerCAmelCase								=					torch.randn(__UpperCamelCase					,						generator=__UpperCamelCase					,						device=self.device					,						dtype=__UpperCamelCase	)
           else:
                if latents.shape != latents_shape:
                     raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
                __lowerCAmelCase								=					latents.to(self.device	)

           # set timesteps
           self.scheduler.set_timesteps(__UpperCamelCase	)

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           __lowerCAmelCase								=					self.scheduler.timesteps.to(self.device	)

           # scale the initial noise by the standard deviation required by the scheduler
           __lowerCAmelCase								=					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]
           __lowerCAmelCase								=					"eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
           __lowerCAmelCase								=					{}
           if accepts_eta:
                __lowerCAmelCase								=					eta

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

                # predict the noise residual
                __lowerCAmelCase								=					self.unet(__UpperCamelCase					,						__UpperCamelCase					,						encoder_hidden_states=__UpperCamelCase	).sample

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

                # compute the previous noisy sample x_t -> x_t-1
                __lowerCAmelCase								=					self.scheduler.step(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase	).prev_sample

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

           __lowerCAmelCase								=					1 / 0.1_8_2_1_5 * latents
           __lowerCAmelCase								=					self.vae.decode(__UpperCamelCase	).sample

           __lowerCAmelCase								=					(image / 2 + 0.5).clamp(0					,						1	)

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

           if output_type == "pil":
                __lowerCAmelCase								=					self.numpy_to_pil(__UpperCamelCase	)

           if not return_dict:
                return image

           return StableDiffusionPipelineOutput(images=__UpperCamelCase					,						nsfw_content_detected=__UpperCamelCase	)














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       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					fname.split(os.path.sep       )[-1]
     return re.search(r"^(.*)_\d+\.jpg$"					, __snake_case       ).groups()[0]



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase=None					,						__UpperCamelCase=None	)->	Dict:
           __lowerCAmelCase								=					file_names
           __lowerCAmelCase								=					image_transform
           __lowerCAmelCase								=					label_to_id


      def __len__(	self	)->	Optional[int]:
           return len(self.file_names	)


      def __getitem__(	self					,						__UpperCamelCase	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.file_names[idx]
           __lowerCAmelCase								=					PIL.Image.open(__UpperCamelCase	)
           __lowerCAmelCase								=					raw_image.convert("RGB"	)
           if self.image_transform is not None:
                __lowerCAmelCase								=					self.image_transform(__UpperCamelCase	)
           __lowerCAmelCase								=					extract_label(__UpperCamelCase	)
           if self.label_to_id is not None:
                __lowerCAmelCase								=					self.label_to_id[label]
           return {"image": image, "label": label}





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     # Initialize accelerator
     if args.with_tracking:
          __lowerCAmelCase								=					Accelerator(
              cpu=args.cpu					, mixed_precision=args.mixed_precision					, log_with="all"					, project_dir=args.project_dir       )
     else:
          __lowerCAmelCase								=					Accelerator(cpu=args.cpu					, mixed_precision=args.mixed_precision       )

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowerCAmelCase								=					config["lr"]
     __lowerCAmelCase								=					int(config["num_epochs"]       )
     __lowerCAmelCase								=					int(config["seed"]       )
     __lowerCAmelCase								=					int(config["batch_size"]       )
     __lowerCAmelCase								=					config["image_size"]
     if not isinstance(__snake_case					, (list, tuple)       ):
          __lowerCAmelCase								=					(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":
               __lowerCAmelCase								=					args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __lowerCAmelCase								=					int(args.checkpointing_steps       )
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
     else:
          __lowerCAmelCase								=					None

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

     # Grab all the image filenames
     __lowerCAmelCase								=					[os.path.join(args.data_dir					, __snake_case       ) for fname in os.listdir(args.data_dir       ) if fname.endswith(".jpg"       )]

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

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

     # Split our filenames between train and validation
     __lowerCAmelCase								=					np.random.permutation(len(__snake_case       )       )
     __lowerCAmelCase								=					int(0.8 * len(__snake_case       )       )
     __lowerCAmelCase								=					random_perm[:cut]
     __lowerCAmelCase								=					random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     __lowerCAmelCase								=					Compose([RandomResizedCrop(__snake_case					, scale=(0.5, 1.0)       ), ToTensor()]       )
     __lowerCAmelCase								=					PetsDataset(
         [file_names[i] for i in train_split]					, image_transform=__snake_case					, label_to_id=__snake_case       )

     # For evaluation, we use a deterministic Resize
     __lowerCAmelCase								=					Compose([Resize(__snake_case       ), ToTensor()]       )
     __lowerCAmelCase								=					PetsDataset([file_names[i] for i in eval_split]					, image_transform=__snake_case					, label_to_id=__snake_case       )

     # Instantiate dataloaders.
     __lowerCAmelCase								=					DataLoader(__snake_case					, shuffle=__snake_case					, batch_size=__snake_case					, num_workers=4       )
     __lowerCAmelCase								=					DataLoader(__snake_case					, shuffle=__snake_case					, batch_size=__snake_case					, num_workers=4       )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowerCAmelCase								=					create_model("resnet50d"					, pretrained=__snake_case					, num_classes=len(__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).
     __lowerCAmelCase								=					model.to(accelerator.device       )

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

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

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

     # Instantiate learning rate scheduler
     __lowerCAmelCase								=					OneCycleLR(optimizer=__snake_case					, max_lr=__snake_case					, epochs=__snake_case					, steps_per_epoch=len(__snake_case       )       )

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

          if "epoch" in training_difference:
               __lowerCAmelCase								=					int(training_difference.replace("epoch_"					, ""       )       ) + 1
               __lowerCAmelCase								=					None
          else:
               __lowerCAmelCase								=					int(training_difference.replace("step_"					, ""       )       )
               __lowerCAmelCase								=					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:
               __lowerCAmelCase								=					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
               __lowerCAmelCase								=					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
               __lowerCAmelCase								=					train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __lowerCAmelCase								=					{k: v.to(accelerator.device       ) for k, v in batch.items()}
               __lowerCAmelCase								=					(batch["image"] - mean) / std
               __lowerCAmelCase								=					model(__snake_case       )
               __lowerCAmelCase								=					torch.nn.functional.cross_entropy(__snake_case					, batch["label"]       )
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__snake_case       )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(__snake_case					, __snake_case       ):
                    __lowerCAmelCase								=					F"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __lowerCAmelCase								=					os.path.join(args.output_dir					, __snake_case       )
                         accelerator.save_state(__snake_case       )
          model.eval()
          __lowerCAmelCase								=					0
          __lowerCAmelCase								=					0
          for step, batch in enumerate(__snake_case       ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __lowerCAmelCase								=					{k: v.to(accelerator.device       ) for k, v in batch.items()}
               __lowerCAmelCase								=					(batch["image"] - mean) / std
               with torch.no_grad():
                    __lowerCAmelCase								=					model(__snake_case       )
               __lowerCAmelCase								=					outputs.argmax(dim=-1       )
               __lowerCAmelCase			, __lowerCAmelCase								=					accelerator.gather_for_metrics((predictions, batch["label"])       )
               __lowerCAmelCase								=					predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

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

     if args.with_tracking:
          accelerator.end_training()





def       __lowerCAmelCase      (						):
     __lowerCAmelCase								=					argparse.ArgumentParser(description="Simple example of training script."       )
     parser.add_argument("--data_dir"					, required=__snake_case					, 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=__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(
         "--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(
         "--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(
         "--project_dir"					, type=__snake_case					, default="logs"					, help="Location on where to store experiment tracking logs` and relevent project information"					, )
     __lowerCAmelCase								=					parser.parse_args()
     __lowerCAmelCase								=					{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
     training_function(__snake_case					, __snake_case       )


if __name__ == "__main__":
 main()






