






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)











class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Any      )			-> None:
												# numbermap keys are the number and its values are lists of indexes sorted
												# in ascending order
												_A									=       {}
												# indexmap keys are an index and it's values are the number at that index
												_A									=       {}

							def 					__A       (     self:      Any						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_delete() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == item:
																						array.pop(__A      )
																						return array
																	elif array[mid] < item:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1
												raise ValueError(
												    '''Either the item is not in the array or the array was unsorted'''      )

							def 					__A       (     self:      int						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_insert() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == index:
																						# If the item already exists in the array,
																						# insert it after the existing item
																						array.insert(mid + 1						,   __A      )
																						return array
																	elif array[mid] < index:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
												array.insert(__A						,   __A      )
												return array

							def 					__A       (     self:      Union[str, Any]						,   __A:      int						,   __A:      int      )			-> None:
												# Remove previous index
												if index in self.indexmap:
																	_A									=       self.indexmap[index]
																	if len(self.numbermap[n]      ) == 1:
																						del self.numbermap[n]
																	else:
																						_A									=       self.binary_search_delete(self.numbermap[n]						,   __A      )

        # Set new index
												_A									=       number

												# Number not seen before or empty so insert number value
												if number not in self.numbermap:
																	_A									=       [index]

												# Here we need to perform a binary search insertion in order to insert
												# The item in the correct place
												else:
																	_A									=       self.binary_search_insert(
																	    self.numbermap[number]						,   __A      )



							def 					__A       (     self:      List[Any]						,   __A:      int      )			-> int:
												return self.numbermap.get(__A						,   [-1]      )[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



