






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       f"""pretrained_models/{model_name}"""
					if not os.path.isfile(_lowercase       ):
										os.makedirs('''pretrained_models'''    ,	exist_ok=_lowercase       )
										_A									=       f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
										download_url(_lowercase    ,	'''pretrained_models'''       )
					_A									=       torch.load(_lowercase    ,	map_location=lambda _lowercase    ,	_lowercase       : storage       )
					return model




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       download_model(pretrained_models[args.image_size]       )

					_A									=       state_dict['''x_embedder.proj.weight''']
					_A									=       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       ):
										_A									=       state_dict[
										    '''t_embedder.mlp.0.weight'''
										]
										_A									=       state_dict[
										    '''t_embedder.mlp.0.bias'''
										]
										_A									=       state_dict[
										    '''t_embedder.mlp.2.weight'''
										]
										_A									=       state_dict[
										    '''t_embedder.mlp.2.bias'''
										]
										_A									=       state_dict[
										    '''y_embedder.embedding_table.weight'''
										]

										_A									=       state_dict[
										    f"""blocks.{depth}.adaLN_modulation.1.weight"""
										]
										_A									=       state_dict[
										    f"""blocks.{depth}.adaLN_modulation.1.bias"""
										]

										_A							,_A							,_A									=       torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]    ,	3    ,	dim=0       )
										_A							,_A							,_A									=       torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]    ,	3    ,	dim=0       )

										_A									=       q
										_A									=       q_bias
										_A									=       k
										_A									=       k_bias
										_A									=       v
										_A									=       v_bias

										_A									=       state_dict[
										    f"""blocks.{depth}.attn.proj.weight"""
										]
										_A									=       state_dict[f"""blocks.{depth}.attn.proj.bias"""]

										_A									=       state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
										_A									=       state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
										_A									=       state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
										_A									=       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'''       )

					_A									=       state_dict['''final_layer.adaLN_modulation.1.weight''']
					_A									=       state_dict['''final_layer.adaLN_modulation.1.bias''']
					_A									=       state_dict['''final_layer.linear.weight''']
					_A									=       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
					_A									=       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=_lowercase    ,	num_attention_heads=16    ,	activation_fn='''gelu-approximate'''    ,	num_embeds_ada_norm=10_00    ,	norm_type='''ada_norm_zero'''    ,	norm_elementwise_affine=_lowercase    ,	)
					transformer.load_state_dict(_lowercase    ,	strict=_lowercase       )

					_A									=       DDIMScheduler(
					    num_train_timesteps=10_00    ,	beta_schedule='''linear'''    ,	prediction_type='''epsilon'''    ,	clip_sample=_lowercase    ,	)

					_A									=       AutoencoderKL.from_pretrained(args.vae_model       )

					_A									=       DiTPipeline(transformer=_lowercase    ,	vae=_lowercase    ,	scheduler=_lowercase       )

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


if __name__ == "__main__":
				__A          = 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.'
				)

				__A          = parser.parse_args()
				main(args)








'''simple docstring'''

from __future__ import annotations






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						list[int]							,		_SCREAMING_SNAKE_CASE     :						int							)			->						list[int]:







	"""simple docstring"""
	lowerCAmelCase    =	{}
	for index, val in enumerate(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase    =	target - val
		if compl in chk_map:
			return [chk_map[compl], index]
		lowerCAmelCase    =	index
	return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F'''{two_sum([2, 7, 11, 15], 9) = }''')