






"""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 			snake_case  (A_      :str      ):







  '''simple docstring'''

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






class 						snake_case  (  UpperCAmelCase       ):





    def __init__(							self	:					Optional[Any]				,		A	:					str				,		A	:					Tuple=None				,		A	:					List[str]=None			):


      '''simple docstring'''




      a     :			Optional[Any]				=   file_names
      a     :			Any				=   image_transform
      a     :			Optional[Any]				=   label_to_id





    def __len__(							self	:					List[Any]			):


      '''simple docstring'''




      return len(self.file_names			)





    def __getitem__(							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




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




def 			snake_case  (A_      :Union[str, Any]	,			A_      :int      ):







  '''simple docstring'''

  a     :			int				=   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     :			int				=   int(config['num_epochs']      )
  a     :			Optional[int]				=   int(config['seed']      )
  a     :			str				=   int(config['batch_size']      )
  a     :			Union[str, Any]				=   config['image_size']
  if not isinstance(A_	,			(list, tuple)      ):
    a     :			List[str]				=   (image_size, image_size)

  # Grab all the image filenames
  a     :			List[str]				=   [os.path.join(args.data_dir	,			A_      ) for fname in os.listdir(args.data_dir      ) if fname.endswith('.jpg'      )]

  # Build the label correspondences
  a     :			Tuple				=   [extract_label(A_      ) for fname in file_names]
  a     :			Optional[int]				=   list(set(A_      )      )
  id_to_label.sort()
  a     :			str				=   {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
  a     :			List[str]				=   np.random.permutation(len(A_      )      )
  a     :			str				=   int(0.8 * len(A_      )      )
  a     :			str				=   random_perm[:cut]
  a     :			Any				=   random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  a     :			Optional[Any]				=   Compose([RandomResizedCrop(A_	,			scale=(0.5, 1.0)      ), ToTensor()]      )
  a     :			Tuple				=   PetsDataset(
      [file_names[i] for i in train_split]	,			image_transform=A_	,			label_to_id=A_      )

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

  # Instantiate dataloaders.
  a     :			Optional[int]				=   DataLoader(A_	,			shuffle=A_	,			batch_size=A_	,			num_workers=4      )
  a     :			Any				=   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)
  a     :			List[str]				=   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).
  a     :			Any				=   model.to(accelerator.device      )

  # Freezing the base model
  for param in model.parameters():
    a     :			List[str]				=   False
  for param in model.get_classifier().parameters():
    a     :			List[str]				=   True

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

  # Instantiate optimizer
  a     :			List[str]				=   torch.optim.Adam(params=model.parameters()	,			lr=lr / 2_5      )

  # Instantiate learning rate scheduler
  a     :			Optional[int]				=   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.
  a,     a,     a,     a,     a     :			Union[str, Any]				=   accelerator.prepare(
      A_	,			A_	,			A_	,			A_	,			A_      )

  # Now we train the model
  for epoch in range(A_      ):
    model.train()
    for step, batch in enumerate(A_      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      a     :			Tuple				=   {k: v.to(accelerator.device      ) for k, v in batch.items()}
      a     :			List[Any]				=   (batch['image'] - mean) / std
      a     :			List[str]				=   model(A_      )
      a     :			List[str]				=   torch.nn.functional.cross_entropy(A_	,			batch['label']      )
      accelerator.backward(A_      )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

    model.eval()
    a     :			str				=   0
    a     :			Dict				=   0
    for _, batch in enumerate(A_      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      a     :			Tuple				=   {k: v.to(accelerator.device      ) for k, v in batch.items()}
      a     :			Union[str, Any]				=   (batch['image'] - mean) / std
      with torch.no_grad():
        a     :			str				=   model(A_      )
      a     :			Tuple				=   outputs.argmax(dim=-1      )
      a,     a     :			Optional[int]				=   accelerator.gather_for_metrics((predictions, batch['label'])      )
      a     :			Tuple				=   predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    a     :			Any				=   accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}: {1_0_0 * eval_metric:.2f}'''      )




def 			snake_case  ():







  '''simple docstring'''

  a     :			Optional[Any]				=   argparse.ArgumentParser(description='Simple example of training script.'      )
  parser.add_argument('--data_dir'	,			required=A_	,			help='The data folder on disk.'      )
  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(
      '--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('--cpu'	,			action='store_true'	,			help='If passed, will train on the CPU.'      )
  a     :			int				=   parser.parse_args()
  a     :			List[str]				=   {'lr': 3E-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
  training_function(A_	,			A_      )


if __name__ == "__main__":
   main()












"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (LMSDiscreteScheduler,)
    __magic_name__         =     10





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Optional[Any]				=   {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]			):
        self.check_over_configs(beta_start=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=A			)





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			List[Any]				=   self.dummy_model()
      a     :			str				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			int				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Union[str, Any]				=   output.prev_sample

      a     :			Any				=   torch.sum(torch.abs(A			)			)
      a     :			List[str]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config(prediction_type='v_prediction'			)
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			Tuple				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Any				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Tuple				=   output.prev_sample

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Optional[int]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 0.00_17			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Union[str, Any]				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a     :			Union[str, Any]				=   sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Optional[int]				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			str				=   output.prev_sample

      a     :			Dict				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			Optional[Any]				=   scheduler_class(**A				,		use_karras_sigmas=A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Any				=   self.dummy_model()
      a     :			Union[str, Any]				=   self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      a     :			Any				=   sample.to(A			)

      for t in scheduler.timesteps:
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			Tuple				=   model(A				,		A			)

        a     :			List[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			int				=   output.prev_sample

      a     :			Tuple				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 38_12.99_27			) < 2E-2
      assert abs(result_mean.item() - 4.96_48			) < 1E-3




