

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    (       _UpperCamelCase		:  Dict	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			fname.split(os.path.sep	)[-1]
 return re.search(R'^(.*)_\d+\.jpg$'		,							_lowercase	).groups()[0]




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple=None							,    snake_case__ :      List[str]=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			file_names
     SCREAMING_SNAKE_CASE								=			image_transform
     SCREAMING_SNAKE_CASE								=			label_to_id

    def __len__(			self :      Dict    ):

     """simple docstring"""






     return len(self.file_names    )




    def __getitem__(			self :      Union[str, Any]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.file_names[idx]
     SCREAMING_SNAKE_CASE								=			PIL.Image.open(__A    )
     SCREAMING_SNAKE_CASE								=			raw_image.convert('RGB'    )
     if self.image_transform is not None:
      SCREAMING_SNAKE_CASE								=			self.image_transform(__A    )
     SCREAMING_SNAKE_CASE								=			extract_label(__A    )
     if self.label_to_id is not None:
      SCREAMING_SNAKE_CASE								=			self.label_to_id[label]
     return {"image": image, "label": label}


def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  List[Any]	)		->							List[Any]:
 '''simple docstring'''


 if args.with_tracking:
  SCREAMING_SNAKE_CASE								=			Accelerator(
      cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with='all'		,							project_dir=args.project_dir	)
 else:
  SCREAMING_SNAKE_CASE								=			Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision	)

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

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

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

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

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

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

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

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

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE								=			DataLoader(_lowercase		,							shuffle=_lowercase		,							batch_size=_lowercase		,							num_workers=4	)
 SCREAMING_SNAKE_CASE								=			DataLoader(_lowercase		,							shuffle=_lowercase		,							batch_size=_lowercase		,							num_workers=4	)

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

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

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

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

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

 # Instantiate learning rate scheduler
 SCREAMING_SNAKE_CASE								=			OneCycleLR(optimizer=_lowercase		,							max_lr=_lowercase		,							epochs=_lowercase		,							steps_per_epoch=len(_lowercase	)	)

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

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

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

  SCREAMING_SNAKE_CASE								=			accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"""epoch {epoch}: {1_00 * eval_metric:.2f}"""	)
  if args.with_tracking:
   accelerator.log(
       {
           'accuracy': 1_00 * eval_metric,
           'train_loss': total_loss.item() / len(_lowercase	),
           'epoch': epoch,
       }		,							step=_lowercase		,							)
  if checkpointing_steps == "epoch":
   SCREAMING_SNAKE_CASE								=			f"""epoch_{epoch}"""
   if args.output_dir is not None:
    SCREAMING_SNAKE_CASE								=			os.path.join(args.output_dir		,							_lowercase	)
   accelerator.save_state(_lowercase	)

 if args.with_tracking:
  accelerator.end_training()


def        __lowerCAmelCase    (       )		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			argparse.ArgumentParser(description='Simple example of training script.'	)
 parser.add_argument('--data_dir'		,							required=_lowercase		,							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=_lowercase		,							default=_lowercase		,							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=_lowercase		,							default=_lowercase		,							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=_lowercase		,							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=_lowercase		,							default=_lowercase		,							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=_lowercase		,							default='logs'		,							help='Location on where to store experiment tracking logs` and relevent project information'		,							)
 SCREAMING_SNAKE_CASE								=			parser.parse_args()
 SCREAMING_SNAKE_CASE								=			{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
 training_function(_lowercase		,							_lowercase	)


if __name__ == "__main__":
      main()




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

