





'''simple docstring'''







import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


snake_case_  :							Optional[int]            =	{5_12: """DiT-XL-2-512x512.pt""", 2_56: """DiT-XL-2-256x256.pt"""}





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any )			->						Union[str, Any]:
						UpperCAmelCase_      :   str      =	F"""pretrained_models/{model_name}"""
						if not os.path.isfile(__a ):
												os.makedirs('''pretrained_models''',      exist_ok=__a )
												UpperCAmelCase_      :   str      =	F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
												download_url(__a,      '''pretrained_models''' )
						UpperCAmelCase_      :   List[Any]      =	torch.load(__a,      map_location=lambda SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ : storage )
						return model





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Dict:
						UpperCAmelCase_      :   Union[str, Any]      =	download_model(pretrained_models[args.image_size] )

						UpperCAmelCase_      :   int      =	state_dict["""x_embedder.proj.weight"""]
						UpperCAmelCase_      :   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(28 ):
												UpperCAmelCase_      :   str      =	state_dict[
												    """t_embedder.mlp.0.weight"""
												]
												UpperCAmelCase_      :   List[str]      =	state_dict[
												    """t_embedder.mlp.0.bias"""
												]
												UpperCAmelCase_      :   int      =	state_dict[
												    """t_embedder.mlp.2.weight"""
												]
												UpperCAmelCase_      :   Any      =	state_dict[
												    """t_embedder.mlp.2.bias"""
												]
												UpperCAmelCase_      :   Any      =	state_dict[
												    """y_embedder.embedding_table.weight"""
												]

												UpperCAmelCase_      :   List[str]      =	state_dict[
												    F"""blocks.{depth}.adaLN_modulation.1.weight"""
												]
												UpperCAmelCase_      :   Optional[Any]      =	state_dict[
												    F"""blocks.{depth}.adaLN_modulation.1.bias"""
												]

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

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

												UpperCAmelCase_      :   List[str]      =	state_dict[
												    F"""blocks.{depth}.attn.proj.weight"""
												]
												UpperCAmelCase_      :   Union[str, Any]      =	state_dict[F"""blocks.{depth}.attn.proj.bias"""]

												UpperCAmelCase_      :   Union[str, Any]      =	state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
												UpperCAmelCase_      :   str      =	state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
												UpperCAmelCase_      :   List[Any]      =	state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
												UpperCAmelCase_      :   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''' )

						UpperCAmelCase_      :   Any      =	state_dict["""final_layer.adaLN_modulation.1.weight"""]
						UpperCAmelCase_      :   Optional[Any]      =	state_dict["""final_layer.adaLN_modulation.1.bias"""]
						UpperCAmelCase_      :   Dict      =	state_dict["""final_layer.linear.weight"""]
						UpperCAmelCase_      :   Union[str, Any]      =	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_      :   List[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=__a,      num_attention_heads=16,      activation_fn='''gelu-approximate''',      num_embeds_ada_norm=1000,      norm_type='''ada_norm_zero''',      norm_elementwise_affine=__a,      )
						transformer.load_state_dict(__a,      strict=__a )

						UpperCAmelCase_      :   Union[str, Any]      =	DDIMScheduler(
						    num_train_timesteps=1000,      beta_schedule='''linear''',      prediction_type='''epsilon''',      clip_sample=__a,      )

						UpperCAmelCase_      :   Tuple      =	AutoencoderKL.from_pretrained(args.vae_model )

						UpperCAmelCase_      :   Any      =	DiTPipeline(transformer=__a,      vae=__a,      scheduler=__a )

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


if __name__ == "__main__":
					snake_case_  :							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."
					)

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






'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__a			,      -1      )



def  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







     if not balanced_parentheses(__a      ):
          raise ValueError("""Mismatched parentheses"""      )
     _UpperCamelCase  :Stack[str]       =Stack()
     _UpperCamelCase  :Optional[int]       =[]
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__a      )
          elif char == "(":
               stack.push(__a      )
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()      )
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__a      ) <= precedence(stack.peek()      ):
                    postfix.append(stack.pop()      )
               stack.push(__a      )
     while not stack.is_empty():
          postfix.append(stack.pop()      )
     return " ".join(__a      )


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

      testmod()
      _lowerCamelCase :				Tuple														= """a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))