"""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				)							->					Dict:




						'''simple docstring'''



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






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=None	, _lowerCamelCase=None      ):
										lowerCamelCase__					=file_names
										lowerCamelCase__					=image_transform
										lowerCamelCase__					=label_to_id

				def __len__(      self      ):
										return len(self.file_names      )






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





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




						'''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(__lowerCAmelCase   ,    (list, tuple)				):
												lowerCamelCase__					=(image_size, image_size)

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

						# Build the label correspondences
						lowerCamelCase__					=[extract_label(__lowerCAmelCase				) for fname in file_names]
						lowerCamelCase__					=list(set(__lowerCAmelCase				)				)
						id_to_label.sort()
						lowerCamelCase__					={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
						lowerCamelCase__					=np.random.permutation(len(__lowerCAmelCase				)				)
						lowerCamelCase__					=int(0.8 * len(__lowerCAmelCase				)				)
						lowerCamelCase__					=random_perm[:cut]
						lowerCamelCase__					=random_perm[cut:]

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

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

						# Instantiate dataloaders.
						lowerCamelCase__					=DataLoader(__lowerCAmelCase   ,    shuffle=__lowerCAmelCase   ,    batch_size=__lowerCAmelCase   ,    num_workers=4				)
						lowerCamelCase__					=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)
						lowerCamelCase__					=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).
						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=__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.
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=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`.
																		lowerCamelCase__					={k: v.to(accelerator.device				) for k, v in batch.items()}
																		lowerCamelCase__					=(batch["image"] - mean) / std
																		lowerCamelCase__					=model(__lowerCAmelCase				)
																		lowerCamelCase__					=torch.nn.functional.cross_entropy(__lowerCAmelCase   ,    batch["label"]				)
																		accelerator.backward(__lowerCAmelCase				)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												lowerCamelCase__					=0
												lowerCamelCase__					=0
												for _, batch in enumerate(__lowerCAmelCase				):
																		# 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(__lowerCAmelCase				)
																		lowerCamelCase__					=outputs.argmax(dim=-1				)
																		lowerCamelCase__					,      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}: {100 * eval_metric:.2f}'''				)





def 			lowerCamelCase_ (					)							->					Any:




						'''simple docstring'''



						lowerCamelCase__					=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."				)
						lowerCamelCase__					=parser.parse_args()
						lowerCamelCase__					={"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
						training_function(__lowerCAmelCase   ,    __lowerCAmelCase				)


if __name__ == "__main__":
	main()



"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, ):
										super().__init__()

										self.register_modules(vae=_lowerCamelCase	, text_encoder=_lowerCamelCase	, tokenizer=_lowerCamelCase	, unet=_lowerCamelCase	, scheduler=_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

										lowerCamelCase__					=self.tokenizer(
										    ""	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

										return torch.cat([uncond_embedding, text_embedding]      )






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					=img.size
										lowerCamelCase__					=self.encode(_lowerCamelCase      )

										torch.manual_seed(_lowerCamelCase      )
										lowerCamelCase__					=torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)	, ).to(self.device      )

										lowerCamelCase__					=self.scheduler.add_noise(
										    _lowerCamelCase	, _lowerCamelCase	, timesteps=self.scheduler.timesteps[tmax]	, )

										lowerCamelCase__					=torch.cat([latents] * 2      )

										lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, self.scheduler.timesteps[tmax]      )

										with torch.no_grad():
																lowerCamelCase__					=self.unet(
																    _lowerCamelCase	, self.scheduler.timesteps[tmax]	, encoder_hidden_states=_lowerCamelCase	, ).sample

										lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
										lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

										lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, self.scheduler.timesteps[tmax]	, _lowerCamelCase      ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
																if i > tmax:
																						if i < tmin:  # layout generation phase
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

																												lowerCamelCase__					=(mix_factor * latents) + (
																												    1 - mix_factor
																												) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																												lowerCamelCase__					=torch.cat([input] * 2      )

																						else:  # content generation phase
																												lowerCamelCase__					=torch.cat([latents] * 2      )

																						lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, _lowerCamelCase      )

																						with torch.no_grad():
																												lowerCamelCase__					=self.unet(
																												    _lowerCamelCase	, _lowerCamelCase	, encoder_hidden_states=_lowerCamelCase	, ).sample

																						lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
																						lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																						lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ).prev_sample

										return self.decode(_lowerCamelCase      )


