
'''simple docstring'''
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			List[Any]   	=  UNetaDModel(**__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Tuple   	=  checkpoint["all_modules.0.W"].data
    snake_case_       :			Any   	=  checkpoint["all_modules.0.W"].data
    snake_case_       :			int   	=  checkpoint["all_modules.1.weight"].data
    snake_case_       :			Tuple   	=  checkpoint["all_modules.1.bias"].data

    snake_case_       :			Dict   	=  checkpoint["all_modules.2.weight"].data
    snake_case_       :			Union[str, Any]   	=  checkpoint["all_modules.2.bias"].data

    snake_case_       :			List[Any]   	=  checkpoint["all_modules.3.weight"].data
    snake_case_       :			Dict   	=  checkpoint["all_modules.3.bias"].data

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

    snake_case_       :			Optional[Any]   	=  4

    def set_attention_weights(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
        snake_case_       :			Any   	=  old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
        snake_case_       :			Any   	=  old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
        snake_case_       :			Any   	=  old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

        snake_case_       :			List[Any]   	=  old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
        snake_case_       :			int   	=  old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
        snake_case_       :			Dict   	=  old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

        snake_case_       :			Any   	=  old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
        snake_case_       :			Dict   	=  old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

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

    def set_resnet_weights(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
        snake_case_       :			Optional[int]   	=  old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
        snake_case_       :			Optional[int]   	=  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_       :			Any   	=  old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

        snake_case_       :			List[Any]   	=  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_       :			int   	=  old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
        snake_case_       :			Optional[int]   	=  old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

        snake_case_       :			str   	=  old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
        snake_case_       :			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:
            snake_case_       :			str   	=  old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
            snake_case_       :			Any   	=  old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

    for i, block in enumerate(new_model_architecture.downsample_blocks				):
        snake_case_       :			Optional[int]   	=  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
            snake_case_       :			List[str]   	=  checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
            snake_case_       :			Any   	=  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				):
        snake_case_       :			List[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:
            snake_case_       :			Optional[int]   	=  checkpoint[f'all_modules.{module_index}.weight'].data
            snake_case_       :			Optional[Any]   	=  checkpoint[f'all_modules.{module_index}.bias'].data
            module_index += 1
            snake_case_       :			Optional[Any]   	=  checkpoint[f'all_modules.{module_index}.weight'].data
            snake_case_       :			Optional[Any]   	=  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

    snake_case_       :			Optional[Any]   	=  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[Any]   	=  checkpoint[f'all_modules.{module_index}.bias'].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
  a_      =  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_      =  parser.parse_args()

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

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

  a_      =  convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

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

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

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








UpperCAmelCase			=    10**8






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  bool:




      """simple docstring"""

      if n % 10 == 0:
            return False
      snake_case_       =      str(SCREAMING_SNAKE_CASE					)
      return s == s[::-1]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      set()
      snake_case_       =      1
      snake_case_       =      5
      while sum_squares < LIMIT:
            snake_case_       =      first_square + 1
            while sum_squares < LIMIT:
                  if is_palindrome(SCREAMING_SNAKE_CASE					):
                        answer.add(SCREAMING_SNAKE_CASE					)
                  last_square += 1
                  sum_squares += last_square**2
            first_square += 1
            snake_case_       =      first_square**2 + (first_square + 1) ** 2

      return sum(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   print(solution())