






"""simple docstring"""







import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__a							:						List[Any]   							=			{512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}





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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					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=lowerCamelCase_			,  num_attention_heads=16			,  activation_fn='''gelu-approximate'''			,  num_embeds_ada_norm=1000			,  norm_type='''ada_norm_zero'''			,  norm_elementwise_affine=lowerCamelCase_			,  )
					transformer.load_state_dict(lowerCamelCase_			,  strict=lowerCamelCase_)

					a__ 							=	DDIMScheduler(
					    num_train_timesteps=1000			,  beta_schedule='''linear'''			,  prediction_type='''epsilon'''			,  clip_sample=lowerCamelCase_			,  )

					a__ 							=	AutoencoderKL.from_pretrained(args.vae_model)

					a__ 							=	DiTPipeline(transformer=lowerCamelCase_			,  vae=lowerCamelCase_			,  scheduler=lowerCamelCase_)

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


if __name__ == "__main__":
	__a							:						List[str]   							=			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							:						Union[str, Any]   							=			parser.parse_args()
	main(args)










"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Optional[int]					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	data
												a__ 							=	None





							def __str__( self:  str     ):

												'''simple docstring'''

												return F'{self.data}'







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  int     ):

												'''simple docstring'''

												a__ 							=	None
												a__ 							=	None




							def __iter__( self:  Tuple     ):

												'''simple docstring'''

												a__ 							=	self.front
												while node:
																	yield node.data
																	a__ 							=	node.next




							def __len__( self:  Union[str, Any]     ):

												'''simple docstring'''

												return len(tuple(iter(self     )     )     )




							def __str__( self:  Any     ):

												'''simple docstring'''

												return " <- ".join(str(__A     ) for item in self     )




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												return len(self     ) == 0




							def 			lowercase		( self:  Tuple					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	Node(__A     )
												if self.is_empty():
																	a__ 							=	a__ 							=	node
												else:
																	assert isinstance(self.rear					,		__A     )
																	a__ 							=	node
																	a__ 							=	node




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												if self.is_empty():
																	raise IndexError('''dequeue from empty queue'''     )
												assert isinstance(self.front					,		__A     )
												a__ 							=	self.front
												a__ 							=	self.front.next
												if self.front is None:
																	a__ 							=	None
												return node.data





							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												a__ 							=	a__ 							=	None


if __name__ == "__main__":
	from doctest import testmod

	testmod()


