

"""simple docstring"""


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__					(					__SCREAMING_SNAKE_CASE				)			->			List[Any]:
		__lowerCAmelCase: Tuple					   =    F"pretrained_models/{model_name}"
		if not os.path.isfile(__SCREAMING_SNAKE_CASE				):
				os.makedirs("pretrained_models"      ,				exist_ok=__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: str					   =    F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
				download_url(__SCREAMING_SNAKE_CASE      ,				"pretrained_models"				)
		__lowerCAmelCase: List[str]					   =    torch.load(__SCREAMING_SNAKE_CASE      ,				map_location=lambda __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				: storage				)
		return model






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Dict:
		__lowerCAmelCase: int					   =    download_model(pretrained_models[args.image_size]				)

		__lowerCAmelCase: Tuple					   =    state_dict["x_embedder.proj.weight"]
		__lowerCAmelCase: str					   =    state_dict["x_embedder.proj.bias"]
		state_dict.pop("x_embedder.proj.weight"				)
		state_dict.pop("x_embedder.proj.bias"				)

		for depth in range(2_8				):
				__lowerCAmelCase: Tuple					   =    state_dict[
				    "t_embedder.mlp.0.weight"
				]
				__lowerCAmelCase: Optional[Any]					   =    state_dict[
				    "t_embedder.mlp.0.bias"
				]
				__lowerCAmelCase: List[Any]					   =    state_dict[
				    "t_embedder.mlp.2.weight"
				]
				__lowerCAmelCase: Tuple					   =    state_dict[
				    "t_embedder.mlp.2.bias"
				]
				__lowerCAmelCase: int					   =    state_dict[
				    "y_embedder.embedding_table.weight"
				]

				__lowerCAmelCase: Optional[int]					   =    state_dict[
				    F"blocks.{depth}.adaLN_modulation.1.weight"
				]
				__lowerCAmelCase: Dict					   =    state_dict[
				    F"blocks.{depth}.adaLN_modulation.1.bias"
				]

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

				__lowerCAmelCase: Any					   =    q
				__lowerCAmelCase: Optional[Any]					   =    q_bias
				__lowerCAmelCase: Union[str, Any]					   =    k
				__lowerCAmelCase: Dict					   =    k_bias
				__lowerCAmelCase: str					   =    v
				__lowerCAmelCase: Optional[int]					   =    v_bias

				__lowerCAmelCase: int					   =    state_dict[
				    F"blocks.{depth}.attn.proj.weight"
				]
				__lowerCAmelCase: Any					   =    state_dict[F"blocks.{depth}.attn.proj.bias"]

				__lowerCAmelCase: int					   =    state_dict[F"blocks.{depth}.mlp.fc1.weight"]
				__lowerCAmelCase: Optional[Any]					   =    state_dict[F"blocks.{depth}.mlp.fc1.bias"]
				__lowerCAmelCase: Optional[Any]					   =    state_dict[F"blocks.{depth}.mlp.fc2.weight"]
				__lowerCAmelCase: Tuple					   =    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"				)

		__lowerCAmelCase: Optional[Any]					   =    state_dict["final_layer.adaLN_modulation.1.weight"]
		__lowerCAmelCase: Any					   =    state_dict["final_layer.adaLN_modulation.1.bias"]
		__lowerCAmelCase: Union[str, Any]					   =    state_dict["final_layer.linear.weight"]
		__lowerCAmelCase: Tuple					   =    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
		__lowerCAmelCase: Tuple					   =    TransformeraDModel(
		    sample_size=args.image_size // 8      ,				num_layers=2_8      ,				attention_head_dim=7_2      ,				in_channels=4      ,				out_channels=8      ,				patch_size=2      ,				attention_bias=__SCREAMING_SNAKE_CASE      ,				num_attention_heads=1_6      ,				activation_fn="gelu-approximate"      ,				num_embeds_ada_norm=1_0_0_0      ,				norm_type="ada_norm_zero"      ,				norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,				)
		transformer.load_state_dict(__SCREAMING_SNAKE_CASE      ,				strict=__SCREAMING_SNAKE_CASE				)

		__lowerCAmelCase: str					   =    DDIMScheduler(
		    num_train_timesteps=1_0_0_0      ,				beta_schedule="linear"      ,				prediction_type="epsilon"      ,				clip_sample=__SCREAMING_SNAKE_CASE      ,				)

		__lowerCAmelCase: Union[str, Any]					   =    AutoencoderKL.from_pretrained(args.vae_model				)

		__lowerCAmelCase: Dict					   =    DiTPipeline(transformer=__SCREAMING_SNAKE_CASE      ,				vae=__SCREAMING_SNAKE_CASE      ,				scheduler=__SCREAMING_SNAKE_CASE				)

		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 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[float, float]:
		assert (
		    c % greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) == 0
		)  # greatest_common_divisor(a,b) function implemented below
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # extended_gcd(a,b) function implemented below
		__lowerCAmelCase: Optional[int]					   =    c / d
		return (r * x, r * y)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 2				)			->			None:
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)): Optional[Any]					   =    diophantine(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # Initial value
		__lowerCAmelCase: Optional[Any]					   =    greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Any					   =    a // d
		__lowerCAmelCase: str					   =    b // d

		for i in range(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: Dict					   =    xa + i * q
				__lowerCAmelCase: List[str]					   =    ya - i * p
				print(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		if a < b:
				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    b, a

		while a % b != 0:
				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    b, a % b

		return b






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[int, int, int]:
		assert a >= 0 and b >= 0

		if b == 0:
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    a, 1, 0
		else:
				((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				a % b				)
				__lowerCAmelCase: Dict					   =    q
				__lowerCAmelCase: int					   =    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)


