




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_      (	lowerCamelCase:							Dict    )       ->      Optional[int]:


      '''simple docstring'''




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






class       A      (		snake_case__		):
     """simple docstring"""

     def __init__(     self	: List[str]						,	A__	: List[str]						,	A__	: int=None						,	A__	: List[Any]=None      )					->							Tuple:
           lowerCamelCase__   	=					file_names
           lowerCamelCase__   	=					image_transform
           lowerCamelCase__   	=					label_to_id




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




     def __getitem__(     self	: Optional[int]						,	A__	: Any      )					->							int:
           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_      (	lowerCamelCase:							int  ,					lowerCamelCase:							Tuple    )       ->      int:


      '''simple docstring'''




      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(_UpperCAmelCase  ,					(list, tuple)    ):
            lowerCamelCase__   	=					(image_size, image_size)

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

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

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

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

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

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

      # Instantiate dataloaders.
      lowerCamelCase__   	=					DataLoader(_UpperCAmelCase  ,					shuffle=_UpperCAmelCase  ,					batch_size=_UpperCAmelCase  ,					num_workers=4    )
      lowerCamelCase__   	=					DataLoader(_UpperCAmelCase  ,					shuffle=_UpperCAmelCase  ,					batch_size=_UpperCAmelCase  ,					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=_UpperCAmelCase  ,					num_classes=len(_UpperCAmelCase    )    )

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      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=_UpperCAmelCase  ,					max_lr=_UpperCAmelCase  ,					epochs=_UpperCAmelCase  ,					steps_per_epoch=len(_UpperCAmelCase    )    )

      # 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__   	=					accelerator.prepare(
          _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase    )

      # Now we train the model
      for epoch in range(_UpperCAmelCase    ):
            model.train()
            for step, batch in enumerate(_UpperCAmelCase    ):
                  # 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(_UpperCAmelCase    )
                  lowerCamelCase__   	=					torch.nn.functional.cross_entropy(_UpperCAmelCase  ,					batch['label']    )
                  accelerator.backward(_UpperCAmelCase    )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()

            model.eval()
            lowerCamelCase__   	=					0
            lowerCamelCase__   	=					0
            for _, batch in enumerate(_UpperCAmelCase    ):
                  # 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(_UpperCAmelCase    )
                  lowerCamelCase__   	=					outputs.argmax(dim=-1    )
                  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}: {1_00 * eval_metric:.2f}"""    )

def       lowerCAmelCase_      (	)       ->      Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					argparse.ArgumentParser(description='Simple example of training script.'    )
      parser.add_argument('--data_dir'  ,					required=_UpperCAmelCase  ,					help='The data folder on disk.'    )
      parser.add_argument(
          '--mixed_precision'  ,					type=_UpperCAmelCase  ,					default=_UpperCAmelCase  ,					choices=['no', 'fp16', 'bf16', 'fp8']  ,					help='Whether to use mixed precision. Choose'
          'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
          'and an Nvidia Ampere GPU.'  ,					)
      parser.add_argument(
          '--checkpointing_steps'  ,					type=_UpperCAmelCase  ,					default=_UpperCAmelCase  ,					help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'  ,					)
      parser.add_argument('--cpu'  ,					action='store_true'  ,					help='If passed, will train on the CPU.'    )
      lowerCamelCase__   	=					parser.parse_args()
      lowerCamelCase__   	=					{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
      training_function(_UpperCAmelCase  ,					_UpperCAmelCase    )


if __name__ == "__main__":
      main()






'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	42
					SCREAMING_SNAKE_CASE							=	42






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0.999,       _UpperCAmelCase="cosine",       ):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_UpperCAmelCase  ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(_UpperCAmelCase  ):
						return math.exp(t * -12.0  )

		else:
				raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"  )

		__UpperCAmelCase	:							Union[str, Any]     =     []
		for i in range(_UpperCAmelCase  ):
				__UpperCAmelCase	:							int     =     i / num_diffusion_timesteps
				__UpperCAmelCase	:							Any     =     (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase  ) / alpha_bar_fn(_UpperCAmelCase  ),       _UpperCAmelCase  )  )
		return torch.tensor(_UpperCAmelCase,       dtype=torch.floataa  )






class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	1
					@register_to_config
					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      int = 1_000		,	UpperCAmelCase_       :      float = 0.0001		,	UpperCAmelCase_       :      float = 0.02		,	UpperCAmelCase_       :      str = "linear"		,	UpperCAmelCase_       :      float = 0.0		,	UpperCAmelCase_       :      Optional[np.ndarray] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							if trained_betas is not None:
									__UpperCAmelCase	:							Tuple     =     torch.from_numpy(UpperCAmelCase_		)
							elif beta_schedule == "linear":
									__UpperCAmelCase	:							Dict     =     torch.linspace(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	dtype=torch.floataa		)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__UpperCAmelCase	:							int     =     (
									    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	UpperCAmelCase_		,	dtype=torch.floataa		) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__UpperCAmelCase	:							str     =     betas_for_alpha_bar(UpperCAmelCase_		)
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									__UpperCAmelCase	:							Optional[Any]     =     torch.linspace(-6		,	6		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[int]     =     torch.sigmoid(UpperCAmelCase_		) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

							__UpperCAmelCase	:							List[str]     =     1.0 - self.betas
							__UpperCAmelCase	:							List[str]     =     torch.cumprod(self.alphas		,	dim=0		)
							__UpperCAmelCase	:							List[Any]     =     torch.tensor(1.0		)

							__UpperCAmelCase	:							str     =     torch.tensor(1.0		)

							# standard deviation of the initial noise distribution
							__UpperCAmelCase	:							Optional[Any]     =     1.0

							# setable values
							__UpperCAmelCase	:							Optional[Any]     =     None
							__UpperCAmelCase	:							Optional[Any]     =     torch.from_numpy(np.arange(0		,	UpperCAmelCase_		)[::-1].copy()		)

							__UpperCAmelCase	:							Tuple     =     eta
					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[int] = None		):







							"""simple docstring"""
							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      Union[str, torch.device] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     min(self.config.num_train_timesteps		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[Any]     =     num_inference_steps

							__UpperCAmelCase	:							Dict     =     []

							__UpperCAmelCase	:							int     =     {}
							for j in range(0		,	num_inference_steps - jump_length		,	UpperCAmelCase_		):
									__UpperCAmelCase	:							int     =     jump_n_sample - 1

							__UpperCAmelCase	:							Optional[int]     =     num_inference_steps
							while t >= 1:
									__UpperCAmelCase	:							int     =     t - 1
									timesteps.append(UpperCAmelCase_		)

									if jumps.get(UpperCAmelCase_		,	0		) > 0:
											__UpperCAmelCase	:							Any     =     jumps[t] - 1
											for _ in range(UpperCAmelCase_		):
													__UpperCAmelCase	:							Optional[Any]     =     t + 1
													timesteps.append(UpperCAmelCase_		)

							__UpperCAmelCase	:							Optional[Any]     =     np.array(UpperCAmelCase_		) * (self.config.num_train_timesteps // self.num_inference_steps)
							__UpperCAmelCase	:							str     =     torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     t - self.config.num_train_timesteps // self.num_inference_steps

							__UpperCAmelCase	:							str     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							int     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Dict     =     1 - alpha_prod_t
							__UpperCAmelCase	:							Optional[Any]     =     1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							__UpperCAmelCase	:							Dict     =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance
					def 		lowerCamelCase_   (   self       :      Any		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[torch.Generator] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     timestep
							__UpperCAmelCase	:							Optional[Any]     =     timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							__UpperCAmelCase	:							Any     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							Optional[int]     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Optional[Any]     =     1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							__UpperCAmelCase	:							List[Any]     =     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									__UpperCAmelCase	:							int     =     torch.clamp(UpperCAmelCase_		,	-1		,	1		)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							__UpperCAmelCase	:							Optional[Any]     =     model_output.device
							__UpperCAmelCase	:							Optional[int]     =     randn_tensor(model_output.shape		,	generator=UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=model_output.dtype		)
							__UpperCAmelCase	:							List[Any]     =     self.eta * self._get_variance(UpperCAmelCase_		) ** 0.5

							__UpperCAmelCase	:							str     =     0
							if t > 0 and self.eta > 0:
									__UpperCAmelCase	:							Union[str, Any]     =     std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							int     =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							Any     =     alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							__UpperCAmelCase	:							str     =     (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							__UpperCAmelCase	:							Union[str, Any]     =     mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

							return RePaintSchedulerOutput(prev_sample=UpperCAmelCase_		,	pred_original_sample=UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      List[str]=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.config.num_train_timesteps // self.num_inference_steps

							for i in range(UpperCAmelCase_		):
									__UpperCAmelCase	:							Any     =     self.betas[timestep + i]
									if sample.device.type == "mps":
											# randn does not work reproducibly on mps
											__UpperCAmelCase	:							Tuple     =     randn_tensor(sample.shape		,	dtype=sample.dtype		,	generator=UpperCAmelCase_		)
											__UpperCAmelCase	:							str     =     noise.to(sample.device		)
									else:
											__UpperCAmelCase	:							str     =     randn_tensor(sample.shape		,	generator=UpperCAmelCase_		,	device=sample.device		,	dtype=sample.dtype		)

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									__UpperCAmelCase	:							Any     =     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.IntTensor		,	):







							"""simple docstring"""
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)






					def __len__(   self       :      List[str]		):







							"""simple docstring"""
							return self.config.num_train_timesteps




