

'''simple docstring'''

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Union[str, Any]		   =					UNetaDModel(**UpperCAmelCase_      )
 UpperCAmelCase			:      Union[str, Any]		   =					checkpoint['all_modules.0.W'].data
 UpperCAmelCase			:      int		   =					checkpoint['all_modules.0.W'].data
 UpperCAmelCase			:      List[Any]		   =					checkpoint['all_modules.1.weight'].data
 UpperCAmelCase			:      Tuple		   =					checkpoint['all_modules.1.bias'].data

 UpperCAmelCase			:      List[Any]		   =					checkpoint['all_modules.2.weight'].data
 UpperCAmelCase			:      Dict		   =					checkpoint['all_modules.2.bias'].data

 UpperCAmelCase			:      Union[str, Any]		   =					checkpoint['all_modules.3.weight'].data
 UpperCAmelCase			:      str		   =					checkpoint['all_modules.3.bias'].data

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

 UpperCAmelCase			:      Any		   =					4

 def set_attention_weights(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
  UpperCAmelCase			:      List[str]		   =					old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
  UpperCAmelCase			:      List[str]		   =					old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
  UpperCAmelCase			:      Optional[Any]		   =					old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

  UpperCAmelCase			:      Tuple		   =					old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
  UpperCAmelCase			:      Any		   =					old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
  UpperCAmelCase			:      Dict		   =					old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

  UpperCAmelCase			:      str		   =					old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
  UpperCAmelCase			:      List[Any]		   =					old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

  UpperCAmelCase			:      Any		   =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  UpperCAmelCase			:      Dict		   =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

 def set_resnet_weights(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
  UpperCAmelCase			:      Optional[int]		   =					old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
  UpperCAmelCase			:      Dict		   =					old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
  UpperCAmelCase			:      Optional[Any]		   =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  UpperCAmelCase			:      Optional[Any]		   =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

  UpperCAmelCase			:      Optional[int]		   =					old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
  UpperCAmelCase			:      Optional[int]		   =					old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
  UpperCAmelCase			:      Dict		   =					old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
  UpperCAmelCase			:      Tuple		   =					old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

  UpperCAmelCase			:      Union[str, Any]		   =					old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
  UpperCAmelCase			:      Dict		   =					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			:      Any		   =					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			:      Any		   =					hasattr(UpperCAmelCase_    ,			'attentions'      )
  for j in range(len(block.resnets      )      ):
   set_resnet_weights(block.resnets[j]    ,			UpperCAmelCase_    ,			UpperCAmelCase_      )
   module_index += 1
   if has_attentions:
    set_attention_weights(block.attentions[j]    ,			UpperCAmelCase_    ,			UpperCAmelCase_      )
    module_index += 1

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

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

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

  if hasattr(UpperCAmelCase_    ,			'resnet_up'      ) and block.resnet_up is not None:
   UpperCAmelCase			:      Dict		   =					checkpoint[F"""all_modules.{module_index}.weight"""].data
   UpperCAmelCase			:      Dict		   =					checkpoint[F"""all_modules.{module_index}.bias"""].data
   module_index += 1
   UpperCAmelCase			:      Optional[int]		   =					checkpoint[F"""all_modules.{module_index}.weight"""].data
   UpperCAmelCase			:      Optional[Any]		   =					checkpoint[F"""all_modules.{module_index}.bias"""].data
   module_index += 1
   set_resnet_weights(block.resnet_up    ,			UpperCAmelCase_    ,			UpperCAmelCase_      )
   module_index += 1

 UpperCAmelCase			:      Dict		   =					checkpoint[F"""all_modules.{module_index}.weight"""].data
 UpperCAmelCase			:      str		   =					checkpoint[F"""all_modules.{module_index}.bias"""].data
 module_index += 1
 UpperCAmelCase			:      Dict		   =					checkpoint[F"""all_modules.{module_index}.weight"""].data
 UpperCAmelCase			:      Tuple		   =					checkpoint[F"""all_modules.{module_index}.bias"""].data

 return new_model_architecture.state_dict()


if __name__ == "__main__":
 lowercase__            =     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.",
 )

 lowercase__            =     parser.parse_args()

 lowercase__            =     torch.load(args.checkpoint_path, map_location="cpu")

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

 lowercase__            =     convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

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

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

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

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







'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = 1_00    ,			):
 UpperCAmelCase			:      Any		   =					x_start
 UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					0.0

 for _ in range(UpperCAmelCase_      ):
  # Approximates small segments of curve as linear and solve
  # for trapezoidal area
  UpperCAmelCase			:      List[Any]		   =					(x_end - x_start) / steps + xa
  UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
  area += abs(fxa + fxa      ) * (xa - xa) / 2

  # Increment step
  UpperCAmelCase			:      List[str]		   =					xa
  UpperCAmelCase			:      List[Any]		   =					fxa
 return area


if __name__ == "__main__":




 def  UpperCamelCase(	UpperCAmelCase_      ):
  return x**3

 print("f(x) = x^3")
 print("The area between the curve, x = -10, x = 10 and the x axis is:")
 lowercase__            =     10
 while i <= 100000:
  lowercase__            =     trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




