'''simple docstring'''
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


a=	{5_1_2: '''DiT-XL-2-512x512.pt''', 2_5_6: '''DiT-XL-2-256x256.pt'''}







def 			_UpperCamelCase			(  _a						:						Any      ):




    """simple docstring"""



    __UpperCamelCase						:						List[str]		     =	f"""pretrained_models/{model_name}"""
    if not os.path.isfile(A__      ):
        os.makedirs('pretrained_models'					,  exist_ok=A__      )
        __UpperCamelCase						:						Any		     =	f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(A__					,  'pretrained_models'      )
    __UpperCamelCase						:						Union[str, Any]		     =	torch.load(A__					,  map_location=lambda _a					,  _a      : storage      )
    return model







def 			_UpperCamelCase			(  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Any		     =	download_model(pretrained_models[args.image_size]      )

    __UpperCamelCase						:						Any		     =	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(2_8      ):
        __UpperCamelCase						:						Union[str, Any]		     =	state_dict[
            't_embedder.mlp.0.weight'
        ]
        __UpperCamelCase						:						Optional[Any]		     =	state_dict[
            't_embedder.mlp.0.bias'
        ]
        __UpperCamelCase						:						Optional[int]		     =	state_dict[
            't_embedder.mlp.2.weight'
        ]
        __UpperCamelCase						:						Union[str, Any]		     =	state_dict[
            't_embedder.mlp.2.bias'
        ]
        __UpperCamelCase						:						List[str]		     =	state_dict[
            'y_embedder.embedding_table.weight'
        ]

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

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

        __UpperCamelCase						:						Optional[int]		     =	q
        __UpperCamelCase						:						Dict		     =	q_bias
        __UpperCamelCase						:						Optional[int]		     =	k
        __UpperCamelCase						:						Optional[Any]		     =	k_bias
        __UpperCamelCase						:						int		     =	v
        __UpperCamelCase						:						Any		     =	v_bias

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

        __UpperCamelCase						:						Tuple		     =	state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
        __UpperCamelCase						:						Optional[int]		     =	state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
        __UpperCamelCase						:						List[Any]		     =	state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
        __UpperCamelCase						:						List[str]		     =	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						:						str		     =	state_dict['final_layer.adaLN_modulation.1.weight']
    __UpperCamelCase						:						Optional[int]		     =	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						:						int		     =	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=A__					,  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=A__					,  )
    transformer.load_state_dict(A__					,  strict=A__      )

    __UpperCamelCase						:						List[str]		     =	DDIMScheduler(
        num_train_timesteps=1_0_0_0					,  beta_schedule='linear'					,  prediction_type='epsilon'					,  clip_sample=A__					,  )

    __UpperCamelCase						:						str		     =	AutoencoderKL.from_pretrained(args.vae_model      )

    __UpperCamelCase						:						List[Any]		     =	DiTPipeline(transformer=A__					,  vae=A__					,  scheduler=A__      )

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


if __name__ == "__main__":
  a=	argparse.ArgumentParser()

  parser.add_argument(
      '''--image_size''',
      default=2_5_6,
      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)





_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

