



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     a_						:     str	     =		argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )
     parser.add_argument(
         '--original_config_file',
         type=str,
         required=True,
         help='The YAML config file corresponding to the original architecture.',
     )
     parser.add_argument(
         '--num_in_channels',
         default=None,
         type=int,
         help='The number of input channels. If `None` number of input channels will be automatically inferred.',
     )
     parser.add_argument(
         '--image_size',
         default=5_1_2,
         type=int,
         help=(
             'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
             ' Base. Use 768 for Stable Diffusion v2.'
         ),
     )
     parser.add_argument(
         '--extract_ema',
         action='store_true',
         help=(
             'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
             ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
             ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
         ),
     )
     parser.add_argument(
         '--upcast_attention',
         action='store_true',
         help=(
             'Whether the attention computation should always be upcasted. This is necessary when running stable'
             ' diffusion 2.1.'
         ),
     )
     parser.add_argument(
         '--from_safetensors',
         action='store_true',
         help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
     )
     parser.add_argument(
         '--to_safetensors',
         action='store_true',
         help='Whether to store pipeline in safetensors format or not.',
     )
     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
     parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

     def     __lowercase(	UpperCAmelCase__ ):


           """simple docstring"""



           if string == "True":
                 return True
           elif string == "False":
                 return False
           else:
                 raise ValueError(F"""could not parse string as bool {string}""" )

     parser.add_argument(
         '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
     )

     parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

     a_						:     Union[str, Any]	     =		parser.parse_args()

     a_						:     Union[str, Any]	     =		download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )






     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




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    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-inpaint/init_image.png"    )
           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )

           lowerCamelCase								=		"xvjiarui/stable-diffusion-2-inpainting"
           lowerCamelCase			,							lowerCamelCase								=		FlaxStableDiffusionInpaintPipeline.from_pretrained(__a				,			safety_checker=__a    )

           lowerCamelCase								=		"Face of a yellow cat, high resolution, sitting on a park bench"

           lowerCamelCase								=		jax.random.PRNGKey(0    )
           lowerCamelCase								=		50

           lowerCamelCase								=		jax.device_count()
           lowerCamelCase								=		num_samples * [prompt]
           lowerCamelCase								=		num_samples * [init_image]
           lowerCamelCase								=		num_samples * [mask_image]
           lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		pipeline.prepare_inputs(__a				,			__a				,			__a    )

           # shard inputs and rng
           lowerCamelCase								=		replicate(__a    )
           lowerCamelCase								=		jax.random.split(__a				,			jax.device_count()    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )

           lowerCamelCase								=		pipeline(
               __a				,			__a				,			__a				,			__a				,			__a				,			__a				,			jit=__a    )

           lowerCamelCase								=		output.images.reshape(__a				,			5_12				,			5_12				,			3    )

           lowerCamelCase								=		images[0, 2_53:2_56, 2_53:2_56, -1]

           lowerCamelCase								=		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
           lowerCamelCase								=		jnp.array(
               [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]    )
           print(F"""output_slice: {output_slice}"""    )
           assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2