






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase_					:		Union[str, Any]           = {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple   )						->     Optional[Any]:






	"""simple docstring"""



	UpperCamelCase	:Optional[Any]		=      f"""pretrained_models/{model_name}"""
	if not os.path.isfile(__magic_name__   ):
		os.makedirs("""pretrained_models"""      ,     exist_ok=__magic_name__   )
		UpperCamelCase	:List[Any]		=      f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
		download_url(__magic_name__      ,     """pretrained_models"""   )
	UpperCamelCase	:Tuple		=      torch.load(__magic_name__      ,     map_location=lambda __magic_name__      ,     __magic_name__   : storage   )
	return model

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]   )						->     str:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      download_model(pretrained_models[args.image_size]   )

	UpperCamelCase	:List[Any]		=      state_dict["""x_embedder.proj.weight"""]
	UpperCamelCase	:Optional[Any]		=      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	:int		=      state_dict[
		    """t_embedder.mlp.0.weight"""
		]
		UpperCamelCase	:Union[str, Any]		=      state_dict[
		    """t_embedder.mlp.0.bias"""
		]
		UpperCamelCase	:int		=      state_dict[
		    """t_embedder.mlp.2.weight"""
		]
		UpperCamelCase	:List[Any]		=      state_dict[
		    """t_embedder.mlp.2.bias"""
		]
		UpperCamelCase	:Dict		=      state_dict[
		    """y_embedder.embedding_table.weight"""
		]

		UpperCamelCase	:str		=      state_dict[
		    f"""blocks.{depth}.adaLN_modulation.1.weight"""
		]
		UpperCamelCase	:int		=      state_dict[
		    f"""blocks.{depth}.adaLN_modulation.1.bias"""
		]

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

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

		UpperCamelCase	:Tuple		=      state_dict[
		    f"""blocks.{depth}.attn.proj.weight"""
		]
		UpperCamelCase	:str		=      state_dict[f"""blocks.{depth}.attn.proj.bias"""]

		UpperCamelCase	:List[Any]		=      state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
		UpperCamelCase	:Union[str, Any]		=      state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
		UpperCamelCase	:Tuple		=      state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
		UpperCamelCase	:Any		=      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	:Any		=      state_dict["""final_layer.adaLN_modulation.1.weight"""]
	UpperCamelCase	:Any		=      state_dict["""final_layer.adaLN_modulation.1.bias"""]
	UpperCamelCase	:Optional[int]		=      state_dict["""final_layer.linear.weight"""]
	UpperCamelCase	:Dict		=      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	:Union[str, Any]		=      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=__magic_name__      ,     num_attention_heads=16      ,     activation_fn="""gelu-approximate"""      ,     num_embeds_ada_norm=1000      ,     norm_type="""ada_norm_zero"""      ,     norm_elementwise_affine=__magic_name__      ,     )
	transformer.load_state_dict(__magic_name__      ,     strict=__magic_name__   )

	UpperCamelCase	:Optional[int]		=      DDIMScheduler(
	    num_train_timesteps=1000      ,     beta_schedule="""linear"""      ,     prediction_type="""epsilon"""      ,     clip_sample=__magic_name__      ,     )

	UpperCamelCase	:Optional[int]		=      AutoencoderKL.from_pretrained(args.vae_model   )

	UpperCamelCase	:Union[str, Any]		=      DiTPipeline(transformer=__magic_name__      ,     vae=__magic_name__      ,     scheduler=__magic_name__   )

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


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

	UpperCAmelCase_					:		List[str]           = parser.parse_args()
	main(args)








from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase_					:		Union[str, Any]           = 6.6_743E-11  # unit of G : m^3 * kg^-1 * s^-2

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float   )						->     dict[str, float]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      mass_a * mass_a

	if (force, mass_a, mass_a, distance).count(0   ) != 1:
		raise ValueError("""One and only one argument must be 0"""   )
	if force < 0:
		raise ValueError("""Gravitational force can not be negative"""   )
	if distance < 0:
		raise ValueError("""Distance can not be negative"""   )
	if mass_a < 0 or mass_a < 0:
		raise ValueError("""Mass can not be negative"""   )
	if force == 0:
		UpperCamelCase	:Optional[int]		=      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
		return {"force": force}
	elif mass_a == 0:
		UpperCamelCase	:Any		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_1": mass_a}
	elif mass_a == 0:
		UpperCamelCase	:Optional[int]		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_2": mass_a}
	elif distance == 0:
		UpperCamelCase	:str		=      (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
		return {"distance": distance}
	raise ValueError("""One and only one argument must be 0"""   )


# Run doctest
if __name__ == "__main__":
	import doctest

	doctest.testmod()
