




from __future__ import annotations


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						assert (
						    c % greatest_common_divisor(snake_case_			,					snake_case_		) == 0
						)  # greatest_common_divisor(a,b) function implemented below
						((_UpperCAmelCase)							,				(_UpperCAmelCase)							,				(_UpperCAmelCase))									=  extended_gcd(snake_case_			,					snake_case_		)  # extended_gcd(a,b) function implemented below
						_UpperCAmelCase									=  c / d
						return (r * x, r * y)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_ = 2		):

						'''simple docstring'''


						((_UpperCAmelCase)							,				(_UpperCAmelCase))									=  diophantine(snake_case_			,					snake_case_			,					snake_case_		)  # Initial value
						_UpperCAmelCase									=  greatest_common_divisor(snake_case_			,					snake_case_		)
						_UpperCAmelCase									=  a // d
						_UpperCAmelCase									=  b // d

						for i in range(snake_case_		):
												_UpperCAmelCase									=  xa + i * q
												_UpperCAmelCase									=  ya - i * p
												print(snake_case_			,					snake_case_		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


						if a < b:
												_UpperCAmelCase							,				_UpperCAmelCase									=  b, a

						while a % b != 0:
												_UpperCAmelCase							,				_UpperCAmelCase									=  b, a % b

						return b


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


						assert a >= 0 and b >= 0

						if b == 0:
												_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase									=  a, 1, 0
						else:
												((_UpperCAmelCase)							,				(_UpperCAmelCase)							,				(_UpperCAmelCase))									=  extended_gcd(snake_case_			,					a % b		)
												_UpperCAmelCase									=  q
												_UpperCAmelCase									=  p - q * (a // b)

						assert a % d == 0 and b % d == 0
						assert d == a * x + b * y

						return (d, x, y)


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

				testmod(name='diophantine', verbose=True)
				testmod(name='diophantine_all_soln', verbose=True)
				testmod(name='extended_gcd', verbose=True)
				testmod(name='greatest_common_divisor', verbose=True)










import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowercase_			:       Tuple						       =				{5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  download_model(pretrained_models[args.image_size]		)

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

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

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

												_UpperCAmelCase									=  q
												_UpperCAmelCase									=  q_bias
												_UpperCAmelCase									=  k
												_UpperCAmelCase									=  k_bias
												_UpperCAmelCase									=  v
												_UpperCAmelCase									=  v_bias

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

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

						_UpperCAmelCase									=  state_dict["final_layer.adaLN_modulation.1.weight"]
						_UpperCAmelCase									=  state_dict["final_layer.adaLN_modulation.1.bias"]
						_UpperCAmelCase									=  state_dict["final_layer.linear.weight"]
						_UpperCAmelCase									=  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
						_UpperCAmelCase									=  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=snake_case_			,					num_attention_heads=16			,					activation_fn="gelu-approximate"			,					num_embeds_ada_norm=1000			,					norm_type="ada_norm_zero"			,					norm_elementwise_affine=snake_case_			,					)
						transformer.load_state_dict(snake_case_			,					strict=snake_case_		)

						_UpperCAmelCase									=  DDIMScheduler(
						    num_train_timesteps=1000			,					beta_schedule="linear"			,					prediction_type="epsilon"			,					clip_sample=snake_case_			,					)

						_UpperCAmelCase									=  AutoencoderKL.from_pretrained(args.vae_model		)

						_UpperCAmelCase									=  DiTPipeline(transformer=snake_case_			,					vae=snake_case_			,					scheduler=snake_case_		)

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


if __name__ == "__main__":
				lowercase_			:       Union[str, Any]						       =				argparse.ArgumentParser()

				parser.add_argument(
				    '--image_size',
				    default=2_56,
				    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.'
				)

				lowercase_			:       Tuple						       =				parser.parse_args()
				main(args)




