



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       _UpperCAmelCase     ):
     UpperCamelCase__		    =				(EulerDiscreteScheduler,)
     UpperCamelCase__		    =				10




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=A_)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
             self.check_over_configs(beta_start=A_ ,						beta_end=A_)




     def 		snake_case_		(				self):
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=A_)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A_)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**A_)

         scheduler.set_timesteps(self.num_inference_steps)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma
         A__		 =					sample.to(A_)

         for i, t in enumerate(scheduler.timesteps):
             A__		 =					scheduler.scale_model_input(A_ ,						A_)

             A__		 =					model(A_ ,						A_)

             A__		 =					scheduler.step(A_ ,						A_ ,						A_ ,						generator=A_)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(A_))
         A__		 =					torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**A_)

         scheduler.set_timesteps(self.num_inference_steps)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma
         A__		 =					sample.to(A_)

         for i, t in enumerate(scheduler.timesteps):
             A__		 =					scheduler.scale_model_input(A_ ,						A_)

             A__		 =					model(A_ ,						A_)

             A__		 =					scheduler.step(A_ ,						A_ ,						A_ ,						generator=A_)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(A_))
         A__		 =					torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 0.0_0_0_2) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**A_)

         scheduler.set_timesteps(self.num_inference_steps ,						device=A_)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__		 =					sample.to(A_)

         for t in scheduler.timesteps:
             A__		 =					scheduler.scale_model_input(A_ ,						A_)

             A__		 =					model(A_ ,						A_)

             A__		 =					scheduler.step(A_ ,						A_ ,						A_ ,						generator=A_)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(A_))
         A__		 =					torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**A_ ,						use_karras_sigmas=A_)

         scheduler.set_timesteps(self.num_inference_steps ,						device=A_)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__		 =					sample.to(A_)

         for t in scheduler.timesteps:
             A__		 =					scheduler.scale_model_input(A_ ,						A_)

             A__		 =					model(A_ ,						A_)

             A__		 =					scheduler.step(A_ ,						A_ ,						A_ ,						generator=A_)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(A_))
         A__		 =					torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1e-3





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		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=512,
       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 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       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)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		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)
