


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     int:







 """simple docstring"""


 _lowercase				 =UNetaDModel(**__snake_case		)
 _lowercase				 =checkpoint['''all_modules.0.W'''].data
 _lowercase				 =checkpoint['''all_modules.0.W'''].data
 _lowercase				 =checkpoint['''all_modules.1.weight'''].data
 _lowercase				 =checkpoint['''all_modules.1.bias'''].data

 _lowercase				 =checkpoint['''all_modules.2.weight'''].data
 _lowercase				 =checkpoint['''all_modules.2.bias'''].data

 _lowercase				 =checkpoint['''all_modules.3.weight'''].data
 _lowercase				 =checkpoint['''all_modules.3.bias'''].data

 _lowercase				 =checkpoint[list(checkpoint.keys()		)[-4]].data
 _lowercase				 =checkpoint[list(checkpoint.keys()		)[-3]].data
 _lowercase				 =checkpoint[list(checkpoint.keys()		)[-2]].data
 _lowercase				 =checkpoint[list(checkpoint.keys()		)[-1]].data

 _lowercase				 =4

 def set_attention_weights(__snake_case	,		__snake_case	,		__snake_case		):
  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
  _lowercase				 =old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

  _lowercase				 =old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

 def set_resnet_weights(__snake_case	,		__snake_case	,		__snake_case		):
  _lowercase				 =old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

  _lowercase				 =old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
  _lowercase				 =old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

  _lowercase				 =old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
  _lowercase				 =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				 =old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
   _lowercase				 =old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

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

  if hasattr(__snake_case	,		'''downsamplers'''		) and block.downsamplers is not None:
   set_resnet_weights(block.resnet_down	,		__snake_case	,		__snake_case		)
   module_index += 1
   _lowercase				 =checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
   _lowercase				 =checkpoint[F"all_modules.{module_index}.Conv_0.bias"].data
   module_index += 1

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

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

  if hasattr(__snake_case	,		'''resnet_up'''		) and block.resnet_up is not None:
   _lowercase				 =checkpoint[F"all_modules.{module_index}.weight"].data
   _lowercase				 =checkpoint[F"all_modules.{module_index}.bias"].data
   module_index += 1
   _lowercase				 =checkpoint[F"all_modules.{module_index}.weight"].data
   _lowercase				 =checkpoint[F"all_modules.{module_index}.bias"].data
   module_index += 1
   set_resnet_weights(block.resnet_up	,		__snake_case	,		__snake_case		)
   module_index += 1

 _lowercase				 =checkpoint[F"all_modules.{module_index}.weight"].data
 _lowercase				 =checkpoint[F"all_modules.{module_index}.bias"].data
 module_index += 1
 _lowercase				 =checkpoint[F"all_modules.{module_index}.weight"].data
 _lowercase				 =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)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

      self.register_modules(unet=UpperCAmelCase						,	scheduler=UpperCAmelCase			)





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

      self.scheduler.set_timesteps(UpperCAmelCase			)
      for t in self.progress_bar(self.scheduler.timesteps			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

      _lowercase				 =(image / 2 + 0.5).clamp(0						,	1			)
      _lowercase				 =image.cpu().permute(0						,	2						,	3						,	1			).numpy()
      if output_type == "pil":
       _lowercase				 =self.numpy_to_pil(UpperCAmelCase			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

