"""simple docstring"""
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Any     				=      UNetaDModel(**_SCREAMING_SNAKE_CASE  )
  a__:	Union[str, Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	Optional[Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	int     				=      checkpoint['all_modules.1.weight'].data
  a__:	Dict     				=      checkpoint['all_modules.1.bias'].data

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

  a__:	Tuple     				=      checkpoint['all_modules.3.weight'].data
  a__:	Optional[int]     				=      checkpoint['all_modules.3.bias'].data

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

  a__:	Tuple     				=      4

  def set_attention_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_0.W'].data.T
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_1.W'].data.T
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.W'].data.T

    a__:	List[str]     				=      old_checkpoint[F'all_modules.{index}.NIN_0.b'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_1.b'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.b'].data

    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.W'].data.T
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.b'].data

    a__:	Optional[Any]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

  def set_resnet_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.Conv_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.Conv_0.bias'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

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

    a__:	str     				=      old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
    a__:	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:
      a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
      a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

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

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

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

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

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

  a__:	str     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	str     				=      checkpoint[F'all_modules.{module_index}.bias'].data
  module_index += 1
  a__:	Union[str, Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	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)





from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       np.ndarray:

 """simple docstring"""






 __lowerCAmelCase:  Any        = np.stack((horizontal_flow, vertical_flow)       ,   2						)

 # Create a grid of all pixel coordinates and subtract the flow to get the
 # target pixels coordinates
 __lowerCAmelCase:  List[str]        = np.stack(
     np.meshgrid(np.arange(0       ,   image.shape[1]						)       ,   np.arange(0       ,   image.shape[0]						)						)       ,   2						)
 __lowerCAmelCase:  int        = np.round(grid - flow						).astype(np.intaa						)

 # Find the locations outside of the original image
 __lowerCAmelCase:  Dict        = (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]						))
 __lowerCAmelCase:  str        = 0

 __lowerCAmelCase:  List[str]        = image[grid[:, :, 1], grid[:, :, 0]]

 # Set pixels at invalid locations to 0
 __lowerCAmelCase:  List[str]        = 0

 return warped







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     SupportsIndex       ,   SCREAMING_SNAKE_CASE						:     float | None = None       ,   )       ->       tuple[np.ndarray, np.ndarray]:

 """simple docstring"""






 if alpha is None:
  __lowerCAmelCase:  Tuple        = 0.1

 # Initialize flow
 __lowerCAmelCase:  Optional[int]        = np.zeros_like(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = np.zeros_like(SCREAMING_SNAKE_CASE						)

 # Prepare kernels for the calculation of the derivatives and the average velocity
 __lowerCAmelCase:  List[Any]        = np.array([[-1, 1], [-1, 1]]						) * 0.2_5
 __lowerCAmelCase:  int        = np.array([[-1, -1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Tuple        = np.array([[1, 1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Dict        = np.array(
     [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]						)

 # Iteratively refine the flow
 for _ in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Optional[int]        = warp(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[str]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[Any]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   -kernel_t						)

  __lowerCAmelCase:  Any        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Dict        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  # This updates the flow as proposed in the paper (Step 12)
  __lowerCAmelCase:  Tuple        = (
      derivative_x * avg_horizontal_velocity
      + derivative_y * avg_vertical_velocity
      + derivative_t
  )
  __lowerCAmelCase:  int        = update / (alpha**2 + derivative_x**2 + derivative_y**2)

  __lowerCAmelCase:  List[Any]        = avg_horizontal_velocity - derivative_x * update
  __lowerCAmelCase:  Union[str, Any]        = avg_vertical_velocity - derivative_y * update

 return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()





