import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Optional[int]     ):
      snake_case_     :			int       =       UNetaDModel(**lowercase_     )
      snake_case_     :			Optional[int]       =       checkpoint['all_modules.0.W'].data
      snake_case_     :			List[Any]       =       checkpoint['all_modules.0.W'].data
      snake_case_     :			str       =       checkpoint['all_modules.1.weight'].data
      snake_case_     :			List[Any]       =       checkpoint['all_modules.1.bias'].data

      snake_case_     :			str       =       checkpoint['all_modules.2.weight'].data
      snake_case_     :			Optional[Any]       =       checkpoint['all_modules.2.bias'].data

      snake_case_     :			Any       =       checkpoint['all_modules.3.weight'].data
      snake_case_     :			Optional[Any]       =       checkpoint['all_modules.3.bias'].data

      snake_case_     :			Optional[int]       =       checkpoint[list(checkpoint.keys()     )[-4]].data
      snake_case_     :			Any       =       checkpoint[list(checkpoint.keys()     )[-3]].data
      snake_case_     :			Optional[Any]       =       checkpoint[list(checkpoint.keys()     )[-2]].data
      snake_case_     :			List[str]       =       checkpoint[list(checkpoint.keys()     )[-1]].data

      snake_case_     :			Any       =       4

      def set_attention_weights(lowerCAmelCase_: Dict ,	lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Optional[Any]     ):
            snake_case_     :			Optional[Any]       =       old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
            snake_case_     :			Tuple       =       old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
            snake_case_     :			Union[str, Any]       =       old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

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

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

            snake_case_     :			int       =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
            snake_case_     :			Tuple       =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

      def set_resnet_weights(lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: int ,	lowerCAmelCase_: Tuple     ):
            snake_case_     :			List[Any]       =       old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
            snake_case_     :			List[str]       =       old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
            snake_case_     :			Any       =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
            snake_case_     :			Optional[int]       =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

            snake_case_     :			Any       =       old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
            snake_case_     :			Optional[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:
                  snake_case_     :			int       =       old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                  snake_case_     :			Dict       =       old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

            if hasattr(lowercase_ ,	"resnet_up"     ) and block.resnet_up is not None:
                  snake_case_     :			int       =       checkpoint[f"all_modules.{module_index}.weight"].data
                  snake_case_     :			Dict       =       checkpoint[f"all_modules.{module_index}.bias"].data
                  module_index += 1
                  snake_case_     :			Tuple       =       checkpoint[f"all_modules.{module_index}.weight"].data
                  snake_case_     :			str       =       checkpoint[f"all_modules.{module_index}.bias"].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up ,	lowercase_ ,	lowercase_     )
                  module_index += 1

      snake_case_     :			int       =       checkpoint[f"all_modules.{module_index}.weight"].data
      snake_case_     :			int       =       checkpoint[f"all_modules.{module_index}.bias"].data
      module_index += 1
      snake_case_     :			Tuple       =       checkpoint[f"all_modules.{module_index}.weight"].data
      snake_case_     :			List[str]       =       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)




import math



class 					snake_case__       :



   def __init__(				self   :   int			,	A__   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			Tuple       =       len(A__   )
         snake_case_     :			Union[str, Any]       =       [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1			,	0			,	self.N - 1   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Tuple   )							->     Optional[int]:




         '''simple docstring'''




         return idx * 2



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   List[str]   )							->     Dict:




         '''simple docstring'''




         return idx * 2 + 1



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   Any			,	A__   :   Optional[int]			,	A__   :   Tuple   )							->     List[str]:  # noqa: E741




         '''simple docstring'''




         if l == r:
               snake_case_     :			str       =       A[l]
         else:
               snake_case_     :			List[str]       =       (l + r) // 2
               self.build(self.left(A__   )			,	A__			,	A__   )
               self.build(self.right(A__   )			,	mid + 1			,	A__   )
               snake_case_     :			Union[str, Any]       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Union[str, Any]			,	A__   :   str   )							->     int:




         '''simple docstring'''




         return self.update_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   List[Any]			,	A__   :   str   )							->     Any:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return True
         if l == r:
               snake_case_     :			List[str]       =       val
               return True
         snake_case_     :			Dict       =       (l + r) // 2
         self.update_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__			,	A__   )
         self.update_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Tuple       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )
         return True



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[Any]			,	A__   :   Optional[Any]   )							->     str:




         '''simple docstring'''




         return self.query_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1   )



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   Any			,	A__   :   int			,	A__   :   Tuple			,	A__   :   Tuple			,	A__   :   Dict   )							->     str:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return -math.inf
         if l >= a and r <= b:
               return self.st[idx]
         snake_case_     :			Dict       =       (l + r) // 2
         snake_case_     :			Optional[Any]       =       self.query_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Any       =       self.query_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__   )
         return max(A__			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			int       =       []
         for i in range(1			,	N + 1   ):
               show_list += [self.query(A__			,	A__   )]
         print(A__   )


if __name__ == "__main__":
     UpperCAmelCase							=       [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
     UpperCAmelCase							=       1_5
     UpperCAmelCase							=       SegmentTree(A)
     print(segt.query(4, 6))
     print(segt.query(7, 1_1))
     print(segt.query(7, 1_2))
     segt.update(1, 3, 1_1_1)
     print(segt.query(1, 1_5))
     segt.update(7, 8, 2_3_5)
     segt.show_data()



