



"""simple docstring"""




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


      lowercase  :      Optional[Any]					=   UNetaDModel(**lowerCamelCase__				)
      lowercase  :      List[Any]					=   checkpoint['all_modules.0.W'].data
      lowercase  :      Optional[Any]					=   checkpoint['all_modules.0.W'].data
      lowercase  :      List[str]					=   checkpoint['all_modules.1.weight'].data
      lowercase  :      List[Any]					=   checkpoint['all_modules.1.bias'].data

      lowercase  :      Optional[int]					=   checkpoint['all_modules.2.weight'].data
      lowercase  :      int					=   checkpoint['all_modules.2.bias'].data

      lowercase  :      Optional[int]					=   checkpoint['all_modules.3.weight'].data
      lowercase  :      Dict					=   checkpoint['all_modules.3.bias'].data

      lowercase  :      int					=   checkpoint[list(checkpoint.keys()				)[-4]].data
      lowercase  :      Union[str, Any]					=   checkpoint[list(checkpoint.keys()				)[-3]].data
      lowercase  :      Tuple					=   checkpoint[list(checkpoint.keys()				)[-2]].data
      lowercase  :      List[Any]					=   checkpoint[list(checkpoint.keys()				)[-1]].data

      lowercase  :      int					=   4

      def set_attention_weights(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				):
            lowercase  :      List[Any]					=   old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
            lowercase  :      List[str]					=   old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
            lowercase  :      Tuple					=   old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

            lowercase  :      Tuple					=   old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
            lowercase  :      Optional[Any]					=   old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
            lowercase  :      List[Any]					=   old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

            lowercase  :      Union[str, Any]					=   old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
            lowercase  :      List[str]					=   old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

            lowercase  :      Dict					=   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
            lowercase  :      Dict					=   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

      def set_resnet_weights(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				):
            lowercase  :      str					=   old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
            lowercase  :      List[Any]					=   old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
            lowercase  :      int					=   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
            lowercase  :      Optional[int]					=   old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

            lowercase  :      Optional[int]					=   old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
            lowercase  :      Dict					=   old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
            lowercase  :      Union[str, Any]					=   old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
            lowercase  :      int					=   old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

            lowercase  :      Union[str, Any]					=   old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
            lowercase  :      Union[str, Any]					=   old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

            if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                  lowercase  :      List[Any]					=   old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
                  lowercase  :      Dict					=   old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

      for i, block in enumerate(new_model_architecture.downsample_blocks				):
            lowercase  :      str					=   hasattr(lowerCamelCase__	,      'attentions'				)
            for j in range(len(block.resnets				)				):
                  set_resnet_weights(block.resnets[j]	,      lowerCamelCase__	,      lowerCamelCase__				)
                  module_index += 1
                  if has_attentions:
                        set_attention_weights(block.attentions[j]	,      lowerCamelCase__	,      lowerCamelCase__				)
                        module_index += 1

            if hasattr(lowerCamelCase__	,      'downsamplers'				) and block.downsamplers is not None:
                  set_resnet_weights(block.resnet_down	,      lowerCamelCase__	,      lowerCamelCase__				)
                  module_index += 1
                  lowercase  :      Optional[int]					=   checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
                  lowercase  :      List[str]					=   checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
                  module_index += 1

      set_resnet_weights(new_model_architecture.mid_block.resnets[0]	,      lowerCamelCase__	,      lowerCamelCase__				)
      module_index += 1
      set_attention_weights(new_model_architecture.mid_block.attentions[0]	,      lowerCamelCase__	,      lowerCamelCase__				)
      module_index += 1
      set_resnet_weights(new_model_architecture.mid_block.resnets[1]	,      lowerCamelCase__	,      lowerCamelCase__				)
      module_index += 1

      for i, block in enumerate(new_model_architecture.up_blocks				):
            lowercase  :      List[Any]					=   hasattr(lowerCamelCase__	,      'attentions'				)
            for j in range(len(block.resnets				)				):
                  set_resnet_weights(block.resnets[j]	,      lowerCamelCase__	,      lowerCamelCase__				)
                  module_index += 1
            if has_attentions:
                  set_attention_weights(
                      block.attentions[0]	,      lowerCamelCase__	,      lowerCamelCase__				)  # why can there only be a single attention layer for up?
                  module_index += 1

            if hasattr(lowerCamelCase__	,      'resnet_up'				) and block.resnet_up is not None:
                  lowercase  :      int					=   checkpoint[f'''all_modules.{module_index}.weight'''].data
                  lowercase  :      str					=   checkpoint[f'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  lowercase  :      Any					=   checkpoint[f'''all_modules.{module_index}.weight'''].data
                  lowercase  :      List[Any]					=   checkpoint[f'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up	,      lowerCamelCase__	,      lowerCamelCase__				)
                  module_index += 1

      lowercase  :      Tuple					=   checkpoint[f'''all_modules.{module_index}.weight'''].data
      lowercase  :      List[Any]					=   checkpoint[f'''all_modules.{module_index}.bias'''].data
      module_index += 1
      lowercase  :      List[Any]					=   checkpoint[f'''all_modules.{module_index}.weight'''].data
      lowercase  :      List[Any]					=   checkpoint[f'''all_modules.{module_index}.bias'''].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
     _UpperCamelCase    							=  argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path',
         default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
         type=str,
         required=False,
         help='Path to the checkpoint to convert.',
     )

     parser.add_argument(
         '--config_file',
         default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
         type=str,
         required=False,
         help='The config json file corresponding to the architecture.',
     )

     parser.add_argument(
         '--dump_path',
         default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
         type=str,
         required=False,
         help='Path to the output model.',
     )

     _UpperCamelCase    							=  parser.parse_args()

     _UpperCamelCase    							=  torch.load(args.checkpoint_path, map_location='cpu')

     with open(args.config_file) as f:
          _UpperCamelCase    							=  json.loads(f.read())

     _UpperCamelCase    							=  convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

     if "sde" in config:
          del config["sde"]

     _UpperCamelCase    							=  UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          _UpperCamelCase    							=  ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

          _UpperCamelCase    							=  ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)





"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

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

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

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

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2
