




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
   lowerCamelCase__					:  Dict      							=				argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )
   # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
   parser.add_argument(
       '--original_config_file',
       default=None,
       type=str,
       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(
       '--scheduler_type',
       default='pndm',
       type=str,
       help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
   )
   parser.add_argument(
       '--pipeline_type',
       default=None,
       type=str,
       help=(
           'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
           '. If `None` pipeline will be automatically inferred.'
       ),
   )
   parser.add_argument(
       '--image_size',
       default=None,
       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(
       '--prediction_type',
       default=None,
       type=str,
       help=(
           'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
           ' Diffusion v2 Base. Use \'v_prediction\' 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.)')
   parser.add_argument(
       '--stable_unclip',
       type=str,
       default=None,
       required=False,
       help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
   )
   parser.add_argument(
       '--stable_unclip_prior',
       type=str,
       default=None,
       required=False,
       help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
   )
   parser.add_argument(
       '--clip_stats_path',
       type=str,
       help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
       required=False,
   )
   parser.add_argument(
       '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
   )
   parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
   parser.add_argument(
       '--vae_path',
       type=str,
       default=None,
       required=False,
       help='Set to a path, hub id to an already converted vae to not convert it again.',
   )
   lowerCamelCase__					:  List[Any]      							=				parser.parse_args()

   lowerCamelCase__					:  Union[str, Any]      							=				download_from_original_stable_diffusion_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       prediction_type=args.prediction_type,
       model_type=args.pipeline_type,
       extract_ema=args.extract_ema,
       scheduler_type=args.scheduler_type,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       stable_unclip=args.stable_unclip,
       stable_unclip_prior=args.stable_unclip_prior,
       clip_stats_path=args.clip_stats_path,
       controlnet=args.controlnet,
       vae_path=args.vae_path,
   )

   if args.half:
      pipe.to(torch_dtype=torch.floataa)

   if args.controlnet:
      # only save the controlnet model
      pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
   else:
      pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax



@require_flax
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= FlaxAutoencoderKL





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						jax.random.PRNGKey(0      )
				SCREAMING_SNAKE_CASE_								=						jax.random.uniform(SCREAMING_SNAKE_CASE      ,     ((batch_size, num_channels) + sizes)      )

				return {"sample": image, "prng_key": prng_key}





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict
