






'''simple docstring'''





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[int]     =						UNetaDModel(**lowerCamelCase__	)
  __UpperCAmelCase							:			List[Any]     =						checkpoint["""all_modules.0.W"""].data
  __UpperCAmelCase							:			Tuple     =						checkpoint["""all_modules.0.W"""].data
  __UpperCAmelCase							:			List[Any]     =						checkpoint["""all_modules.1.weight"""].data
  __UpperCAmelCase							:			str     =						checkpoint["""all_modules.1.bias"""].data

  __UpperCAmelCase							:			List[Any]     =						checkpoint["""all_modules.2.weight"""].data
  __UpperCAmelCase							:			List[str]     =						checkpoint["""all_modules.2.bias"""].data

  __UpperCAmelCase							:			List[Any]     =						checkpoint["""all_modules.3.weight"""].data
  __UpperCAmelCase							:			List[str]     =						checkpoint["""all_modules.3.bias"""].data

  __UpperCAmelCase							:			Tuple     =						checkpoint[list(checkpoint.keys()	)[-4]].data
  __UpperCAmelCase							:			str     =						checkpoint[list(checkpoint.keys()	)[-3]].data
  __UpperCAmelCase							:			Dict     =						checkpoint[list(checkpoint.keys()	)[-2]].data
  __UpperCAmelCase							:			List[str]     =						checkpoint[list(checkpoint.keys()	)[-1]].data

  __UpperCAmelCase							:			Optional[int]     =						4

  def set_attention_weights(__lowerCamelCase    :  str						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  Optional[Any]	):
    __UpperCAmelCase							:			Optional[Any]     =						old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
    __UpperCAmelCase							:			Any     =						old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
    __UpperCAmelCase							:			List[str]     =						old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

    __UpperCAmelCase							:			List[str]     =						old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
    __UpperCAmelCase							:			str     =						old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
    __UpperCAmelCase							:			int     =						old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

    __UpperCAmelCase							:			Tuple     =						old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
    __UpperCAmelCase							:			Optional[Any]     =						old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

    __UpperCAmelCase							:			Any     =						old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
    __UpperCAmelCase							:			Any     =						old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

  def set_resnet_weights(__lowerCamelCase    :  List[str]						,	__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  str	):
    __UpperCAmelCase							:			Dict     =						old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
    __UpperCAmelCase							:			List[Any]     =						old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
    __UpperCAmelCase							:			str     =						old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
    __UpperCAmelCase							:			Union[str, Any]     =						old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

    __UpperCAmelCase							:			Optional[Any]     =						old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
    __UpperCAmelCase							:			List[Any]     =						old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
    __UpperCAmelCase							:			str     =						old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
    __UpperCAmelCase							:			Optional[int]     =						old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

    __UpperCAmelCase							:			Optional[Any]     =						old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
    __UpperCAmelCase							:			Tuple     =						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:
      __UpperCAmelCase							:			List[str]     =						old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
      __UpperCAmelCase							:			Optional[int]     =						old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

  for i, block in enumerate(new_model_architecture.downsample_blocks	):
    __UpperCAmelCase							:			int     =						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
      __UpperCAmelCase							:			Dict     =						checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
      __UpperCAmelCase							:			int     =						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	):
    __UpperCAmelCase							:			Union[str, 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:
      __UpperCAmelCase							:			List[str]     =						checkpoint[f"""all_modules.{module_index}.weight"""].data
      __UpperCAmelCase							:			Any     =						checkpoint[f"""all_modules.{module_index}.bias"""].data
      module_index += 1
      __UpperCAmelCase							:			List[str]     =						checkpoint[f"""all_modules.{module_index}.weight"""].data
      __UpperCAmelCase							:			int     =						checkpoint[f"""all_modules.{module_index}.bias"""].data
      module_index += 1
      set_resnet_weights(block.resnet_up						,	lowerCamelCase__						,	lowerCamelCase__	)
      module_index += 1

  __UpperCAmelCase							:			Dict     =						checkpoint[f"""all_modules.{module_index}.weight"""].data
  __UpperCAmelCase							:			Tuple     =						checkpoint[f"""all_modules.{module_index}.bias"""].data
  module_index += 1
  __UpperCAmelCase							:			int     =						checkpoint[f"""all_modules.{module_index}.weight"""].data
  __UpperCAmelCase							:			Any     =						checkpoint[f"""all_modules.{module_index}.bias"""].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
       a	: Dict      =				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.",
       )

       a	: Optional[Any]      =				parser.parse_args()

       a	: List[Any]      =				torch.load(args.checkpoint_path, map_location="cpu")

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

       a	: List[str]      =				convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )

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

       a	: Dict      =				UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              a	: Any      =				ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

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










a	: List[str]      =				[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class 					a					:



      """simple docstring"""


      def __init__(   self      :    Union[str, Any]	,   __lowercase      :    List[str]      )							->   Optional[int]:
        __UpperCAmelCase							:			Dict     =						name
        __UpperCAmelCase							:			Optional[Any]     =						[]
        __UpperCAmelCase							:			List[Any]     =						[]
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    int      )							->   str:
        self.inbound.append(__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Optional[Any]      )							->   List[str]:
        self.outbound.append(__lowercase      )




      def __repr__(   self      :    Any      )							->   int:
        return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  str=3						,	__lowerCamelCase    :  int=0.8_5	):
  __UpperCAmelCase							:			Optional[int]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			str     =						1

  __UpperCAmelCase							:			Union[str, Any]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			Dict     =						len(node.outbound	)

  for i in range(__lowerCamelCase	):
    print(f"""======= Iteration {i + 1} ======="""	)
    for _, node in enumerate(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						(1 - d) + d * sum(
          ranks[ib] / outbounds[ib] for ib in node.inbound	)
    print(__lowerCamelCase	)






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						list(input("""Enter Names of the Nodes: """	).split()	)

  __UpperCAmelCase							:			Dict     =						[Node(__lowerCamelCase	) for name in names]

  for ri, row in enumerate(__lowerCamelCase	):
    for ci, col in enumerate(__lowerCamelCase	):
      if col == 1:
        nodes[ci].add_inbound(names[ri]	)
        nodes[ri].add_outbound(names[ci]	)

  print("""======= Nodes ======="""	)
  for node in nodes:
    print(__lowerCamelCase	)

  page_rank(__lowerCamelCase	)


if __name__ == "__main__":
       main()


