





"""simple docstring"""





import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard




@slow
@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      Any ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()







      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png''' )
        __UpperCAmelCase    :						Optional[int]       =						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

        __UpperCAmelCase    :						List[Any]       =						'''xvjiarui/stable-diffusion-2-inpainting'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						FlaxStableDiffusionInpaintPipeline.from_pretrained(__lowercase						,				safety_checker=__lowercase )

        __UpperCAmelCase    :						str       =						'''Face of a yellow cat, high resolution, sitting on a park bench'''

        __UpperCAmelCase    :						List[Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Optional[Any]       =						50

        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Any       =						num_samples * [prompt]
        __UpperCAmelCase    :						Union[str, Any]       =						num_samples * [init_image]
        __UpperCAmelCase    :						Union[str, Any]       =						num_samples * [mask_image]
        __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						pipeline.prepare_inputs(__lowercase						,				__lowercase						,				__lowercase )

        # shard inputs and rng
        __UpperCAmelCase    :						Optional[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Any       =						jax.random.split(__lowercase						,				jax.device_count() )
        __UpperCAmelCase    :						Tuple       =						shard(__lowercase )
        __UpperCAmelCase    :						Dict       =						shard(__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Any       =						pipeline(
            __lowercase						,				__lowercase						,				__lowercase						,				__lowercase						,				__lowercase						,				__lowercase						,				jit=__lowercase )

        __UpperCAmelCase    :						int       =						output.images.reshape(__lowercase						,				512						,				512						,				3 )

        __UpperCAmelCase    :						Dict       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						List[Any]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Tuple       =						jnp.array(
            [0.3_6_1_1_3_0_7, 0.3_7_6_4_9_7_3_6, 0.3_7_5_7_4_0_8, 0.3_8_2_1_3_9_5_3, 0.3_9_2_9_5_1_6_7, 0.3_8_4_1_6_3_1, 0.4_1_5_5_4_9_7_8, 0.4_1_3_7_4_7_5, 0.4_2_1_7_0_8_4] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						DDIMScheduler(
      timesteps=config.model.params.timesteps  ,			beta_schedule='''scaled_linear'''  ,			beta_start=config.model.params.linear_start  ,			beta_end=config.model.params.linear_end  ,			clip_sample=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  lowercase__				:Dict				   =       parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)