


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_snake_case					=					{5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

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

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

    lowerCamelCase			: str   =    state_dict[
        f"""blocks.{depth}.adaLN_modulation.1.weight"""
    ]
    lowerCamelCase			: Union[str, Any]   =    state_dict[
        f"""blocks.{depth}.adaLN_modulation.1.bias"""
    ]

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

    lowerCamelCase			: str   =    q
    lowerCamelCase			: Dict   =    q_bias
    lowerCamelCase			: Tuple   =    k
    lowerCamelCase			: Optional[Any]   =    k_bias
    lowerCamelCase			: Tuple   =    v
    lowerCamelCase			: int   =    v_bias

    lowerCamelCase			: Optional[Any]   =    state_dict[
        f"""blocks.{depth}.attn.proj.weight"""
    ]
    lowerCamelCase			: str   =    state_dict[f"""blocks.{depth}.attn.proj.bias"""]

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

  lowerCamelCase			: Optional[Any]   =    DDIMScheduler(
      num_train_timesteps=1000				,						beta_schedule="linear"				,						prediction_type="epsilon"				,						clip_sample=SCREAMING_SNAKE_CASE_				,						)

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

  lowerCamelCase			: str   =    DiTPipeline(transformer=SCREAMING_SNAKE_CASE_				,						vae=SCREAMING_SNAKE_CASE_				,						scheduler=SCREAMING_SNAKE_CASE_     )

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


if __name__ == "__main__":
   _snake_case					=					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					=					parser.parse_args()
   main(args)







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image
class      UpperCAmelCase_ ( unittest.TestCase		,			UpperCamelCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    load_tool("image-segmentation"			)
     self.tool.setup()
     lowerCamelCase			: Optional[Any]   =    load_tool("image-segmentation"						,    remote=__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: int   =    self.remote_tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Optional[Any]   =    self.tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.remote_tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)



