



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__     (						_UpperCamelCase     :	Tuple							):
   __lowerCamelCase  							=      F"""pretrained_models/{model_name}"""
   if not os.path.isfile(_UpperCamelCase							):
      os.makedirs('''pretrained_models'''	,exist_ok=_UpperCamelCase							)
      __lowerCamelCase  							=      F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
      download_url(_UpperCamelCase	,'''pretrained_models'''							)
   __lowerCamelCase  							=      torch.load(_UpperCamelCase	,map_location=lambda _UpperCamelCase	,_UpperCamelCase							: storage							)
   return model





def 						a__     (						_UpperCamelCase     :	Dict							):
   __lowerCamelCase  							=      download_model(pretrained_models[args.image_size]							)

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

      __lowerCamelCase  							=      state_dict[
          F"""blocks.{depth}.adaLN_modulation.1.weight"""
      ]
      __lowerCamelCase  							=      state_dict[
          F"""blocks.{depth}.adaLN_modulation.1.bias"""
      ]

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

      __lowerCamelCase  							=      q
      __lowerCamelCase  							=      q_bias
      __lowerCamelCase  							=      k
      __lowerCamelCase  							=      k_bias
      __lowerCamelCase  							=      v
      __lowerCamelCase  							=      v_bias

      __lowerCamelCase  							=      state_dict[
          F"""blocks.{depth}.attn.proj.weight"""
      ]
      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.attn.proj.bias"""]

      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
      __lowerCamelCase  							=      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  							=      state_dict['''final_layer.adaLN_modulation.1.weight''']
   __lowerCamelCase  							=      state_dict['''final_layer.adaLN_modulation.1.bias''']
   __lowerCamelCase  							=      state_dict['''final_layer.linear.weight''']
   __lowerCamelCase  							=      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  							=      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=_UpperCamelCase	,num_attention_heads=16	,activation_fn='''gelu-approximate'''	,num_embeds_ada_norm=10_00	,norm_type='''ada_norm_zero'''	,norm_elementwise_affine=_UpperCamelCase	,)
   transformer.load_state_dict(_UpperCamelCase	,strict=_UpperCamelCase							)

   __lowerCamelCase  							=      DDIMScheduler(
       num_train_timesteps=10_00	,beta_schedule='''linear'''	,prediction_type='''epsilon'''	,clip_sample=_UpperCamelCase	,)

   __lowerCamelCase  							=      AutoencoderKL.from_pretrained(args.vae_model							)

   __lowerCamelCase  							=      DiTPipeline(transformer=_UpperCamelCase	,vae=_UpperCamelCase	,scheduler=_UpperCamelCase							)

   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)











import math



class 	__lowerCAmelCase       :




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      x
    __lowerCamelCase  							=      y
    __lowerCamelCase  							=      step_size
    __lowerCamelCase  							=      function_to_optimize




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.function(self.x		,			self.y			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.step_size
    return [
        SearchProblem(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			self.function			)
        for x, y in (
            (self.x - step_size, self.y - step_size),
            (self.x - step_size, self.y),
            (self.x - step_size, self.y + step_size),
            (self.x, self.y - step_size),
            (self.x, self.y + step_size),
            (self.x + step_size, self.y - step_size),
            (self.x + step_size, self.y),
            (self.x + step_size, self.y + step_size),
        )
    ]




 def __hash__(  self			):



    '''simple docstring'''
    return hash(str(self			)			)




 def __eq__(  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    if isinstance(__UpperCAmelCase		,			__UpperCAmelCase			):
       return hash(str(self			)			) == hash(str(__UpperCAmelCase			)			)
    return False




 def __str__(  self			):



    '''simple docstring'''
    return F"""x: {self.x} y: {self.y}"""





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	bool = True	,_UpperCamelCase     :	float = math.inf	,_UpperCamelCase     :	float = -math.inf	,_UpperCamelCase     :	float = math.inf	,_UpperCamelCase     :	float = -math.inf	,_UpperCamelCase     :	bool = False	,_UpperCamelCase     :	int = 1_00_00	,):
   __lowerCamelCase  							=      search_prob
   __lowerCamelCase  							=      []  # list to store the current score at each iteration
   __lowerCamelCase  							=      0
   __lowerCamelCase  							=      False
   __lowerCamelCase  							=      set()
   while not solution_found and iterations < max_iter:
      visited.add(_UpperCamelCase							)
      iterations += 1
      __lowerCamelCase  							=      current_state.score()
      scores.append(_UpperCamelCase							)
      __lowerCamelCase  							=      current_state.get_neighbors()
      __lowerCamelCase  							=      -math.inf
      __lowerCamelCase  							=      math.inf
      __lowerCamelCase  							=      None  # to hold the next best neighbor
      for neighbor in neighbors:
         if neighbor in visited:
            continue  # do not want to visit the same state again
         if (
             neighbor.x > max_x
             or neighbor.x < min_x
             or neighbor.y > max_y
             or neighbor.y < min_y
         ):
            continue  # neighbor outside our bounds
         __lowerCamelCase  							=      neighbor.score() - current_score
         if find_max:  # finding max
            # going to direction with greatest ascent
            if change > max_change and change > 0:
               __lowerCamelCase  							=      change
               __lowerCamelCase  							=      neighbor
         else:  # finding min
            # to direction with greatest descent
            if change < min_change and change < 0:
               __lowerCamelCase  							=      change
               __lowerCamelCase  							=      neighbor
      if next_state is not None:
         # we found at least one neighbor which improved the current state
         __lowerCamelCase  							=      next_state
      else:
         # since we have no neighbor that improves the solution we stop the search
         __lowerCamelCase  							=      True

   if visualization:
      from matplotlib import pyplot as plt

      plt.plot(range(_UpperCamelCase							)	,_UpperCamelCase							)
      plt.xlabel('''Iterations'''							)
      plt.ylabel('''Function values'''							)
      plt.show()

   return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()





  def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	Union[str, Any]							):
     return (x**2) + (y**2)

  # starting the problem with initial coordinates (3, 4)
  a_								=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(prob, find_max=False)
  print(
      """The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: """
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  a_								=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(
      prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
      f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
  )





  def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	Dict							):
     return (3 * x**2) - (6 * y)

  a_								=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(prob, find_max=True)





  print(
      """The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: """
      f"{local_min.score()}"
  )






