





'''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 lowerCamelCase						(					__lowerCamelCase			:   int   )						->int:
    _SCREAMING_SNAKE_CASE     =      fname.split(os.path.sep   )[-1]
    return re.search(R"""^(.*)_\d+\.jpg$""" ,	__lowerCamelCase   ).groups()[0]


class  a_					(    snake_case_	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A=None			,		A=None	)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      file_names
        _SCREAMING_SNAKE_CASE     =      image_transform
        _SCREAMING_SNAKE_CASE     =      label_to_id


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





    def __getitem__(		self			,		A	)		->       str:
        _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						(					__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   Union[str, Any]   )						->int:
    # Initialize accelerator
    _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(__lowerCamelCase ,	(list, tuple)   ):
        _SCREAMING_SNAKE_CASE     =      (image_size, image_size)

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

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

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

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

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

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

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE     =      DataLoader(__lowerCamelCase ,	shuffle=__lowerCamelCase ,	batch_size=__lowerCamelCase ,	num_workers=4   )
    _SCREAMING_SNAKE_CASE     =      DataLoader(__lowerCamelCase ,	shuffle=__lowerCamelCase ,	batch_size=__lowerCamelCase ,	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=__lowerCamelCase ,	num_classes=len(__lowerCamelCase   )   )

    # 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=__lowerCamelCase ,	max_lr=__lowerCamelCase ,	epochs=__lowerCamelCase ,	steps_per_epoch=len(__lowerCamelCase   )   )

    # 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(
        __lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )

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

        model.eval()
        _SCREAMING_SNAKE_CASE     =      0
        _SCREAMING_SNAKE_CASE     =      0
        for _, batch in enumerate(__lowerCamelCase   ):
            # 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(__lowerCamelCase   )
            _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}: {100 * eval_metric:.2f}'   )





def lowerCamelCase						(					)						->List[Any]:
    _SCREAMING_SNAKE_CASE     =      argparse.ArgumentParser(description="""Simple example of training script."""   )
    parser.add_argument("""--data_dir""" ,	required=__lowerCamelCase ,	help="""The data folder on disk."""   )
    parser.add_argument(
        """--mixed_precision""" ,	type=__lowerCamelCase ,	default=__lowerCamelCase ,	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=__lowerCamelCase ,	default=__lowerCamelCase ,	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."""   )
    _SCREAMING_SNAKE_CASE     =      parser.parse_args()
    _SCREAMING_SNAKE_CASE     =      {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
    training_function(__lowerCamelCase ,	__lowerCamelCase   )


if __name__ == "__main__":
      main()










'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

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

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

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

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



