
"""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(__UpperCamelCase )       ->				List[str]:
	_lowerCAmelCase			=fname.split(os.path.sep )[-1]
	return re.search(R"""^(.*)_\d+\.jpg$"""					,				__UpperCamelCase ).groups()[0]







class 	lowerCamelCase__      (      a__       ):





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=None						)				->  List[str]:
				_lowerCAmelCase			=file_names
				_lowerCAmelCase			=image_transform
				_lowerCAmelCase			=label_to_id



			def __len__(							self						)				->  Dict:
				return len(self.file_names						)



			def __getitem__(							self  ,  __UpperCAmelCase						)				->  List[str]:
				_lowerCAmelCase			=self.file_names[idx]
				_lowerCAmelCase			=PIL.Image.open(__A						)
				_lowerCAmelCase			=raw_image.convert("""RGB"""						)
				if self.image_transform is not None:
					_lowerCAmelCase			=self.image_transform(__A						)
				_lowerCAmelCase			=extract_label(__A						)
				if self.label_to_id is not None:
					_lowerCAmelCase			=self.label_to_id[label]
				return {"image": image, "label": label}







def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Union[str, Any]:
	_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}: {100 * eval_metric:.2f}''' )



def 					_lowerCamelCase()       ->				Dict:
	_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": 224}
	training_function(__UpperCamelCase					,				__UpperCamelCase )


if __name__ == "__main__":
			main()



"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=UNetaDModel(**__UpperCamelCase )
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.2.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.2.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.3.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.3.bias"""].data

	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-4]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-3]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-2]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-1]].data

	_lowerCAmelCase			=4

	def set_attention_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

	def set_resnet_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

	for i, block in enumerate(new_model_architecture.downsample_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]					,				__UpperCamelCase					,				__UpperCamelCase )
				module_index += 1

		if hasattr(__UpperCamelCase					,				"""downsamplers""" ) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(__UpperCamelCase					,				"""resnet_up""" ) and block.resnet_up is not None:
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			set_resnet_weights(block.resnet_up					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1

	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
	module_index += 1
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
			    type=str,
			    required=False,
			    help='Path to the checkpoint to convert.',
			)

			parser.add_argument(
			    '--config_file',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
			    type=str,
			    required=False,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument(
			    '--dump_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
			    type=str,
			    required=False,
			    help='Path to the output model.',
			)

			__A 		= parser.parse_args()

			__A 		= torch.load(args.checkpoint_path, map_location='cpu')

			with open(args.config_file) as f:
						__A 		= json.loads(f.read())

			__A 		= convert_ncsnpp_checkpoint(
			    checkpoint,
			    config,
			)

			if "sde" in config:
						del config["sde"]

			__A 		= UNetaDModel(**config)
			model.load_state_dict(converted_checkpoint)

			try:
						__A 		= ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

						__A 		= ScoreSdeVePipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)
			except:  # noqa: E722
						model.save_pretrained(args.dump_path)



