






"""simple docstring"""
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCAmelCase__							= {512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						f"""pretrained_models/{model_name}"""
				if not os.path.isfile(lowerCamelCase					):
								os.makedirs('pretrained_models',	exist_ok=lowerCamelCase					)
								_SCREAMING_SNAKE_CASE      :		int							=						f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
								download_url(lowerCamelCase,	'pretrained_models'					)
				_SCREAMING_SNAKE_CASE      :		int							=						torch.load(lowerCamelCase,	map_location=lambda lowerCamelCase,	lowerCamelCase					: storage					)
				return model





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						download_model(pretrained_models[args.image_size]					)

				_SCREAMING_SNAKE_CASE      :		List[str]							=						state_dict['x_embedder.proj.weight']
				_SCREAMING_SNAKE_CASE      :		Tuple							=						state_dict['x_embedder.proj.bias']
				state_dict.pop('x_embedder.proj.weight'					)
				state_dict.pop('x_embedder.proj.bias'					)

				for depth in range(28					):
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						state_dict[
								    't_embedder.mlp.0.weight'
								]
								_SCREAMING_SNAKE_CASE      :		Any							=						state_dict[
								    't_embedder.mlp.0.bias'
								]
								_SCREAMING_SNAKE_CASE      :		Dict							=						state_dict[
								    't_embedder.mlp.2.weight'
								]
								_SCREAMING_SNAKE_CASE      :		int							=						state_dict[
								    't_embedder.mlp.2.bias'
								]
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						state_dict[
								    'y_embedder.embedding_table.weight'
								]

								_SCREAMING_SNAKE_CASE      :		Dict							=						state_dict[
								    f"""blocks.{depth}.adaLN_modulation.1.weight"""
								]
								_SCREAMING_SNAKE_CASE      :		str							=						state_dict[
								    f"""blocks.{depth}.adaLN_modulation.1.bias"""
								]

								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""],	3,	dim=0					)
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""],	3,	dim=0					)

								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						q
								_SCREAMING_SNAKE_CASE      :		Tuple							=						q_bias
								_SCREAMING_SNAKE_CASE      :		Dict							=						k
								_SCREAMING_SNAKE_CASE      :		List[Any]							=						k_bias
								_SCREAMING_SNAKE_CASE      :		Any							=						v
								_SCREAMING_SNAKE_CASE      :		Tuple							=						v_bias

								_SCREAMING_SNAKE_CASE      :		List[str]							=						state_dict[
								    f"""blocks.{depth}.attn.proj.weight"""
								]
								_SCREAMING_SNAKE_CASE      :		List[str]							=						state_dict[f"""blocks.{depth}.attn.proj.bias"""]

								_SCREAMING_SNAKE_CASE      :		Dict							=						state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
								_SCREAMING_SNAKE_CASE      :		Any							=						state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
								_SCREAMING_SNAKE_CASE      :		int							=						state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						state_dict[f"""blocks.{depth}.mlp.fc2.bias"""]

								state_dict.pop(f"""blocks.{depth}.attn.qkv.weight"""					)
								state_dict.pop(f"""blocks.{depth}.attn.qkv.bias"""					)
								state_dict.pop(f"""blocks.{depth}.attn.proj.weight"""					)
								state_dict.pop(f"""blocks.{depth}.attn.proj.bias"""					)
								state_dict.pop(f"""blocks.{depth}.mlp.fc1.weight"""					)
								state_dict.pop(f"""blocks.{depth}.mlp.fc1.bias"""					)
								state_dict.pop(f"""blocks.{depth}.mlp.fc2.weight"""					)
								state_dict.pop(f"""blocks.{depth}.mlp.fc2.bias"""					)
								state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.weight"""					)
								state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.bias"""					)

				state_dict.pop('t_embedder.mlp.0.weight'					)
				state_dict.pop('t_embedder.mlp.0.bias'					)
				state_dict.pop('t_embedder.mlp.2.weight'					)
				state_dict.pop('t_embedder.mlp.2.bias'					)
				state_dict.pop('y_embedder.embedding_table.weight'					)

				_SCREAMING_SNAKE_CASE      :		Tuple							=						state_dict['final_layer.adaLN_modulation.1.weight']
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						state_dict['final_layer.adaLN_modulation.1.bias']
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						state_dict['final_layer.linear.weight']
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						state_dict['final_layer.linear.bias']

				state_dict.pop('final_layer.linear.weight'					)
				state_dict.pop('final_layer.linear.bias'					)
				state_dict.pop('final_layer.adaLN_modulation.1.weight'					)
				state_dict.pop('final_layer.adaLN_modulation.1.bias'					)

				# DiT XL/2
				_SCREAMING_SNAKE_CASE      :		List[str]							=						TransformeraDModel(
				    sample_size=args.image_size // 8,	num_layers=28,	attention_head_dim=72,	in_channels=4,	out_channels=8,	patch_size=2,	attention_bias=lowerCamelCase,	num_attention_heads=16,	activation_fn='gelu-approximate',	num_embeds_ada_norm=1_000,	norm_type='ada_norm_zero',	norm_elementwise_affine=lowerCamelCase,	)
				transformer.load_state_dict(lowerCamelCase,	strict=lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						DDIMScheduler(
				    num_train_timesteps=1_000,	beta_schedule='linear',	prediction_type='epsilon',	clip_sample=lowerCamelCase,	)

				_SCREAMING_SNAKE_CASE      :		Dict							=						AutoencoderKL.from_pretrained(args.vae_model					)

				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						DiTPipeline(transformer=lowerCamelCase,	vae=lowerCamelCase,	scheduler=lowerCamelCase					)

				if args.save:
								pipeline.save_pretrained(args.checkpoint_path					)


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

			parser.add_argument(
			    '--image_size',
			    default=256,
			    type=int,
			    required=False,
			    help='Image size of pretrained model, either 256 or 512.',
			)
			parser.add_argument(
			    '--vae_model',
			    default='stabilityai/sd-vae-ft-ema',
			    type=str,
			    required=False,
			    help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
			)
			parser.add_argument(
			    '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
			)
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
			)

			lowerCAmelCase__							= parser.parse_args()
			main(args)









"""simple docstring"""
def 							lowercase__		(				lowerCamelCase					):
				return -num if num < 0 else num





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_min() arg is an empty sequence'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						x[0]
				for i in x:
								if abs_val(lowerCamelCase					) < abs_val(lowerCamelCase					):
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						i
				return j





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_max() arg is an empty sequence'					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						x[0]
				for i in x:
								if abs(lowerCamelCase					) > abs(lowerCamelCase					):
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						i
				return j





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_max_sort() arg is an empty sequence'					)
				return sorted(lowerCamelCase,	key=lowerCamelCase					)[-1]





def 							lowercase__		(				):
				assert abs_val(0					) == 0
				assert abs_val(34					) == 34
				assert abs_val(-100_000_000_000					) == 100_000_000_000

				_SCREAMING_SNAKE_CASE      :		Tuple							=						[-3, -1, 2, -11]
				assert abs_max(lowerCamelCase					) == -11
				assert abs_max_sort(lowerCamelCase					) == -11
				assert abs_min(lowerCamelCase					) == -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			test_abs_val()
			print(abs_val(-34))  # --> 34

