

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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase__       = logging.get_logger(__name__)  # pylint: disable=invalid-name






class 						__magic_name__    (       _snake_case	,						_snake_case    ):


 @register_to_config
 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: bool			,						lowerCAmelCase__		: Optional[int] = None			,						lowerCAmelCase__		: Optional[int] = None    )    ->      Optional[int]:
   super().__init__()

   UpperCAmelCase					      =  learnable

   if self.learnable:
     assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
     assert length is not None, "learnable=True requires `length` to be set"

     UpperCAmelCase					      =  torch.zeros(lowerCAmelCase__			,						lowerCAmelCase__    )
   else:
     UpperCAmelCase					      =  None

   UpperCAmelCase					      =  torch.nn.Parameter(lowerCAmelCase__    )






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42


 def __init__(						self		: str			,						lowerCAmelCase__		: VQModel			,						lowerCAmelCase__		: CLIPTextModel			,						lowerCAmelCase__		: CLIPTokenizer			,						lowerCAmelCase__		: TransformeraDModel			,						lowerCAmelCase__		: VQDiffusionScheduler			,						lowerCAmelCase__		: LearnedClassifierFreeSamplingEmbeddings			,						)    ->      int:
   super().__init__()

   self.register_modules(
       vqvae=lowerCAmelCase__			,						transformer=lowerCAmelCase__			,						text_encoder=lowerCAmelCase__			,						tokenizer=lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						learned_classifier_free_sampling_embeddings=lowerCAmelCase__			,						)


 def _UpperCamelCase	(						self		: List[Any]			,						lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: int    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  len(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) else 1

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

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

   # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
   # While CLIP does normalize the pooled output of the text transformer when combining
   # the image and text embeddings, CLIP does not directly normalize the last hidden state.
   #
   # CLIP normalizing the pooled output.
   # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
   UpperCAmelCase					      =  prompt_embeds / prompt_embeds.norm(dim=-1			,						keepdim=lowerCAmelCase__    )

   # duplicate text embeddings for each generation per prompt
   UpperCAmelCase					      =  prompt_embeds.repeat_interleave(lowerCAmelCase__			,						dim=0    )

   if do_classifier_free_guidance:
     if self.learned_classifier_free_sampling_embeddings.learnable:
       UpperCAmelCase					      =  self.learned_classifier_free_sampling_embeddings.embeddings
       UpperCAmelCase					      =  negative_prompt_embeds.unsqueeze(0    ).repeat(lowerCAmelCase__			,						1			,						1    )
     else:
       UpperCAmelCase					      =  [""] * batch_size

       UpperCAmelCase					      =  text_input_ids.shape[-1]
       UpperCAmelCase					      =  self.tokenizer(
           lowerCAmelCase__			,						padding="max_length"			,						max_length=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						return_tensors="pt"			,						)
       UpperCAmelCase					      =  self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
       # See comment for normalizing text embeddings
       UpperCAmelCase					      =  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1			,						keepdim=lowerCAmelCase__    )

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     UpperCAmelCase					      =  negative_prompt_embeds.shape[1]
     UpperCAmelCase					      =  negative_prompt_embeds.repeat(1			,						lowerCAmelCase__			,						1    )
     UpperCAmelCase					      =  negative_prompt_embeds.view(batch_size * num_images_per_prompt			,						lowerCAmelCase__			,						-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
     UpperCAmelCase					      =  torch.cat([negative_prompt_embeds, prompt_embeds]    )

   return prompt_embeds


 @torch.no_grad()
 def __call__(						self		: Optional[Any]			,						lowerCAmelCase__		: Union[str, List[str]]			,						lowerCAmelCase__		: int = 1_0_0			,						lowerCAmelCase__		: float = 5.0			,						lowerCAmelCase__		: float = 1.0			,						lowerCAmelCase__		: int = 1			,						lowerCAmelCase__		: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						lowerCAmelCase__		: Optional[torch.FloatTensor] = None			,						lowerCAmelCase__		: Optional[str] = "pil"			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,						lowerCAmelCase__		: int = 1			,						)    ->      Union[ImagePipelineOutput, Tuple]:
   if isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ):
     UpperCAmelCase					      =  1
   elif isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ):
     UpperCAmelCase					      =  len(lowerCAmelCase__    )
   else:
     raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__    )}"    )

   UpperCAmelCase					      =  batch_size * num_images_per_prompt

   UpperCAmelCase					      =  guidance_scale > 1.0

   UpperCAmelCase					      =  self._encode_prompt(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

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

   # get the initial completely masked latents unless the user supplied it

   UpperCAmelCase					      =  (batch_size, self.transformer.num_latent_pixels)
   if latents is None:
     UpperCAmelCase					      =  self.transformer.num_vector_embeds - 1
     UpperCAmelCase					      =  torch.full(lowerCAmelCase__			,						lowerCAmelCase__    ).to(self.device    )
   else:
     if latents.shape != latents_shape:
       raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"    )
     if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
       raise ValueError(
           "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
           f" {self.transformer.num_vector_embeds - 1} (inclusive)."    )
     UpperCAmelCase					      =  latents.to(self.device    )

   # set timesteps
   self.scheduler.set_timesteps(lowerCAmelCase__			,						device=self.device    )

   UpperCAmelCase					      =  self.scheduler.timesteps.to(self.device    )

   UpperCAmelCase					      =  latents

   for i, t in enumerate(self.progress_bar(lowerCAmelCase__    )    ):
     # expand the sample if we are doing classifier free guidance
     UpperCAmelCase					      =  torch.cat([sample] * 2    ) if do_classifier_free_guidance else sample

     # predict the un-noised image
     # model_output == `log_p_x_0`
     UpperCAmelCase					      =  self.transformer(lowerCAmelCase__			,						encoder_hidden_states=lowerCAmelCase__			,						timestep=lowerCAmelCase__    ).sample

     if do_classifier_free_guidance:
       UpperCAmelCase      ,	UpperCAmelCase					      =  model_output.chunk(2    )
       UpperCAmelCase					      =  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
       model_output -= torch.logsumexp(lowerCAmelCase__			,						dim=1			,						keepdim=lowerCAmelCase__    )

     UpperCAmelCase					      =  self.truncate(lowerCAmelCase__			,						lowerCAmelCase__    )

     # remove `log(0)`'s (`-inf`s)
     UpperCAmelCase					      =  model_output.clamp(-7_0    )

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase					      =  self.scheduler.step(lowerCAmelCase__			,						timestep=lowerCAmelCase__			,						sample=lowerCAmelCase__			,						generator=lowerCAmelCase__    ).prev_sample

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

   UpperCAmelCase					      =  self.vqvae.config.vq_embed_dim
   UpperCAmelCase					      =  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
   UpperCAmelCase					      =  self.vqvae.quantize.get_codebook_entry(lowerCAmelCase__			,						shape=lowerCAmelCase__    )
   UpperCAmelCase					      =  self.vqvae.decode(lowerCAmelCase__			,						force_not_quantize=lowerCAmelCase__    ).sample

   UpperCAmelCase					      =  (image / 2 + 0.5).clamp(0			,						1    )
   UpperCAmelCase					      =  image.cpu().permute(0			,						2			,						3			,						1    ).numpy()

   if output_type == "pil":
     UpperCAmelCase					      =  self.numpy_to_pil(lowerCAmelCase__    )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: torch.FloatTensor			,						lowerCAmelCase__		: float    )    ->      torch.FloatTensor:
   UpperCAmelCase      ,	UpperCAmelCase					      =  torch.sort(lowerCAmelCase__			,						1			,						descending=lowerCAmelCase__    )
   UpperCAmelCase					      =  torch.exp(lowerCAmelCase__    )
   UpperCAmelCase					      =  sorted_p_x_0.cumsum(dim=1    ) < truncation_rate

   # Ensure that at least the largest probability is not zeroed out
   UpperCAmelCase					      =  torch.full_like(keep_mask[:, 0:1, :]			,						lowerCAmelCase__    )
   UpperCAmelCase					      =  torch.cat((all_true, keep_mask)			,						dim=1    )
   UpperCAmelCase					      =  keep_mask[:, :-1, :]

   UpperCAmelCase					      =  keep_mask.gather(1			,						indices.argsort(1    )    )

   UpperCAmelCase					      =  log_p_x_0.clone()

   UpperCAmelCase					      =  -torch.inf  # -inf = log(0)

   return rv






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






class 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: Any			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Union[str, Any]=None			,						lowerCAmelCase__		: int=None    )    ->      Optional[Any]:
   UpperCAmelCase					      =  file_names
   UpperCAmelCase					      =  image_transform
   UpperCAmelCase					      =  label_to_id


 def __len__(						self		: Tuple    )    ->      List[str]:
   return len(self.file_names    )


 def __getitem__(						self		: Optional[int]			,						lowerCAmelCase__		: Tuple    )    ->      Dict:
   UpperCAmelCase					      =  self.file_names[idx]
   UpperCAmelCase					      =  PIL.Image.open(lowerCAmelCase__    )
   UpperCAmelCase					      =  raw_image.convert("RGB"    )
   if self.image_transform is not None:
     UpperCAmelCase					      =  self.image_transform(lowerCAmelCase__    )
   UpperCAmelCase					      =  extract_label(lowerCAmelCase__    )
   if self.label_to_id is not None:
     UpperCAmelCase					      =  self.label_to_id[label]
   return {"image": image, "label": label}

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

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

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

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

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

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

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

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

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

  # Instantiate dataloaders.
  UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)
  UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)

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

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

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

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

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

  # Instantiate learning rate scheduler
  UpperCAmelCase					      =  OneCycleLR(optimizer=__A   ,       max_lr=__A   ,       epochs=__A   ,       steps_per_epoch=len(__A			)			)

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

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

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

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

  if args.with_tracking:
    accelerator.end_training()
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  argparse.ArgumentParser(description="Simple example of training script."			)
  parser.add_argument("--data_dir"   ,       required=__A   ,       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=__A   ,       default=__A   ,       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=__A   ,       default=__A   ,       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=__A   ,       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=__A   ,       default=__A   ,       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=__A   ,       default="logs"   ,       help="Location on where to store experiment tracking logs` and relevent project information"   ,       )
  UpperCAmelCase					      =  parser.parse_args()
  UpperCAmelCase					      =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(__A   ,       __A			)


if __name__ == "__main__":
      main()



