




"""simple docstring"""




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class 							lowerCamelCase		( unittest.TestCase				):







   @property
   def       a_							(							self ):
       UpperCamelCase				:   Optional[Any]          =							1
       UpperCamelCase				:   Union[str, Any]          =							3
       UpperCamelCase				:   Any          =							8
       UpperCamelCase				:   Optional[Any]          =							8

       UpperCamelCase				:   Union[str, Any]          =							torch.rand((batch_size, num_channels, height, width) )

       return sample







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       if isinstance(image[0]    ,      PIL.Image.Image ):
           return np.stack([np.array(SCREAMING_SNAKE_CASE_ ) for i in image]    ,      axis=0 )
       elif isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
           return image.cpu().numpy().transpose(0    ,      2    ,      3    ,      1 )
       return image







   def       a_							(							self ):
       UpperCamelCase				:   str          =							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   str          =							self.dummy_sample
       UpperCamelCase				:   Dict          =							self.to_np(SCREAMING_SNAKE_CASE_ )

       for output_type in ["pt", "np", "pil"]:
           UpperCamelCase				:   Dict          =							image_processor.postprocess(
               image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type=SCREAMING_SNAKE_CASE_    ,      )
           UpperCamelCase				:   Union[str, Any]          =							self.to_np(SCREAMING_SNAKE_CASE_ )
           UpperCamelCase				:   Union[str, Any]          =							(input_np * 255).round() if output_type == """pil""" else input_np
           assert (
               np.abs(in_np - out_np ).max() < 1e-6
           ), f'decoded output does not match input for output_type {output_type}'







   def       a_							(							self ):
       UpperCamelCase				:   int          =							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   Any          =							self.dummy_sample.cpu().numpy().transpose(0    ,      2    ,      3    ,      1 )

       for output_type in ["pt", "np", "pil"]:
           UpperCamelCase				:   int          =							image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type=SCREAMING_SNAKE_CASE_ )

           UpperCamelCase				:   Tuple          =							self.to_np(SCREAMING_SNAKE_CASE_ )
           UpperCamelCase				:   Any          =							(input_np * 255).round() if output_type == """pil""" else input_np
           assert (
               np.abs(in_np - out_np ).max() < 1e-6
           ), f'decoded output does not match input for output_type {output_type}'







   def       a_							(							self ):
       UpperCamelCase				:   Dict          =							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   str          =							self.dummy_sample.cpu().numpy().transpose(0    ,      2    ,      3    ,      1 )
       UpperCamelCase				:   Dict          =							image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE_ )

       for output_type in ["pt", "np", "pil"]:
           UpperCamelCase				:   Dict          =							image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type=SCREAMING_SNAKE_CASE_ )
           for i, o in zip(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
               UpperCamelCase				:   str          =							np.array(SCREAMING_SNAKE_CASE_ )
               UpperCamelCase				:   int          =							self.to_np(SCREAMING_SNAKE_CASE_ ) if output_type == """pil""" else (self.to_np(SCREAMING_SNAKE_CASE_ ) * 255).round()
               assert (
                   np.abs(in_np - out_np ).max() < 1e-6
               ), f'decoded output does not match input for output_type {output_type}'







   def       a_							(							self ):
       UpperCamelCase				:   Optional[int]          =							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Any          =							self.dummy_sample
       UpperCamelCase				:   Dict          =							input_pt_ad.squeeze(0 )

       UpperCamelCase				:   Optional[Any]          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )
       UpperCamelCase				:   Optional[int]          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )

       UpperCamelCase				:   Optional[int]          =							self.to_np(self.dummy_sample )
       UpperCamelCase				:   Tuple          =							input_np_ad.squeeze(0 )

       UpperCamelCase				:   int          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )
       UpperCamelCase				:   Any          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )

       assert np.abs(out_pt_ad - out_pt_ad ).max() < 1e-6
       assert np.abs(out_np_ad - out_np_ad ).max() < 1e-6







   def       a_							(							self ):
       UpperCamelCase				:   Union[str, Any]          =							VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   List[str]          =							self.dummy_sample
       UpperCamelCase				:   List[str]          =							list(SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Optional[int]          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )

       UpperCamelCase				:   str          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )

       UpperCamelCase				:   List[str]          =							self.to_np(self.dummy_sample )
       list(SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Optional[Any]          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )

       UpperCamelCase				:   Tuple          =							image_processor.postprocess(
           image_processor.preprocess(SCREAMING_SNAKE_CASE_ )    ,      output_type="""np"""    ,      )

       assert np.abs(out_pt_ad - out_pt_list ).max() < 1e-6
       assert np.abs(out_np_ad - out_np_list ).max() < 1e-6










"""simple docstring"""




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
def 					A_			(		snake_case_     :       Dict    ):







    '''simple docstring'''
    UpperCamelCase				:   Any          =							f'pretrained_models/{model_name}'
    if not os.path.isfile(snake_case_    ):
        os.makedirs("""pretrained_models"""						,exist_ok=snake_case_    )
        UpperCamelCase				:   Dict          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
        download_url(snake_case_						,"""pretrained_models"""    )
    UpperCamelCase				:   List[Any]          =							torch.load(snake_case_						,map_location=lambda snake_case_						,snake_case_    : storage    )
    return model
def 					A_			(		snake_case_     :       List[str]    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )

    UpperCamelCase				:   str          =							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(2_8    ):
        UpperCamelCase				:   Optional[int]          =							state_dict[
            """t_embedder.mlp.0.weight"""
        ]
        UpperCamelCase				:   Optional[Any]          =							state_dict[
            """t_embedder.mlp.0.bias"""
        ]
        UpperCamelCase				:   str          =							state_dict[
            """t_embedder.mlp.2.weight"""
        ]
        UpperCamelCase				:   Optional[int]          =							state_dict[
            """t_embedder.mlp.2.bias"""
        ]
        UpperCamelCase				:   int          =							state_dict[
            """y_embedder.embedding_table.weight"""
        ]

        UpperCamelCase				:   List[Any]          =							state_dict[
            f'blocks.{depth}.adaLN_modulation.1.weight'
        ]
        UpperCamelCase				:   Any          =							state_dict[
            f'blocks.{depth}.adaLN_modulation.1.bias'
        ]

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

        UpperCamelCase				:   int          =							q
        UpperCamelCase				:   Tuple          =							q_bias
        UpperCamelCase				:   List[Any]          =							k
        UpperCamelCase				:   int          =							k_bias
        UpperCamelCase				:   List[str]          =							v
        UpperCamelCase				:   str          =							v_bias

        UpperCamelCase				:   Any          =							state_dict[
            f'blocks.{depth}.attn.proj.weight'
        ]
        UpperCamelCase				:   Optional[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']

        UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
        UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
        UpperCamelCase				:   str          =							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				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
    UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
    UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
    UpperCamelCase				:   List[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=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=snake_case_						,)
    transformer.load_state_dict(snake_case_						,strict=snake_case_    )

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

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

    UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )

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


if __name__ == "__main__":
  __A :		Any      							=    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 :		Dict      							=    parser.parse_args()
  main(args)




