





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


a__      :     int         =		False
a__      :     str         =		True
a__      :     Any         =		False


if __name__ == "__main__":
      a__      :     List[Any]         =		argparse.ArgumentParser()

      parser.add_argument(
          '--repo_path',
          default=None,
          type=str,
          required=True,
          help='The config json file corresponding to the architecture.',
      )

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      a__      :     Dict         =		parser.parse_args()

      a__      :     Optional[int]         =		{
          'image_size': 'sample_size',
          'num_res_blocks': 'layers_per_block',
          'block_channels': 'block_out_channels',
          'down_blocks': 'down_block_types',
          'up_blocks': 'up_block_types',
          'downscale_freq_shift': 'freq_shift',
          'resnet_num_groups': 'norm_num_groups',
          'resnet_act_fn': 'act_fn',
          'resnet_eps': 'norm_eps',
          'num_head_channels': 'attention_head_dim',
      }

      a__      :     Optional[int]         =		{
          'time_steps': 'time_proj',
          'mid': 'mid_block',
          'downsample_blocks': 'down_blocks',
          'upsample_blocks': 'up_blocks',
      }

      a__      :     Any         =		'' if has_file(args.repo_path, 'config.json') else 'unet'

      with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
            a__      :     List[Any]         =		reader.read()
            a__      :     List[str]         =		json.loads(text)

      if do_only_config:
            for key in config_parameters_to_change.keys():
                  config.pop(key, None)

      if has_file(args.repo_path, 'config.json'):
            a__      :     Union[str, Any]         =		UNetaDModel(**config)
      else:
            a__      :     Optional[Any]         =		UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
            a__      :     Any         =		class_name(**config)

      if do_only_config:
            model.save_config(os.path.join(args.repo_path, subfolder))

      a__      :     List[str]         =		dict(model.config)

      if do_only_renaming:
            for key, value in config_parameters_to_change.items():
                  if key in config:
                        a__      :     Optional[int]         =		config[key]
                        del config[key]

            a__      :     str         =		[k.replace('UNetRes', '') for k in config['down_block_types']]
            a__      :     List[Any]         =		[k.replace('UNetRes', '') for k in config['up_block_types']]

      if do_only_weights:
            a__      :     int         =		torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

            a__      :     int         =		{}
            for param_key, param_value in state_dict.items():
                  if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
                        continue
                  a__      :     List[str]         =		False
                  for key, new_key in key_parameters_to_change.items():
                        if not has_changed and param_key.split('.')[0] == key:
                              a__      :     List[Any]         =		param_value
                              a__      :     List[str]         =		True
                  if not has_changed:
                        a__      :     Optional[int]         =		param_value

            model.load_state_dict(new_state_dict)
            model.save_pretrained(os.path.join(args.repo_path, subfolder))










import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





@slow
@require_torch_gpu
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

       A_          =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       A_          =       torch.manual_seed(0 )
       A_          =       pipe(
           image=__snake_case ,generator=__snake_case ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

       A_          =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       A_          =       np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



