






'''simple docstring'''







import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def _UpperCamelCase	(					__A				,      __A   )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__  =    UNetaDModel(**__A   )
      UpperCamelCase__  =    checkpoint["all_modules.0.W"].data
      UpperCamelCase__  =    checkpoint["all_modules.0.W"].data
      UpperCamelCase__  =    checkpoint["all_modules.1.weight"].data
      UpperCamelCase__  =    checkpoint["all_modules.1.bias"].data

      UpperCamelCase__  =    checkpoint["all_modules.2.weight"].data
      UpperCamelCase__  =    checkpoint["all_modules.2.bias"].data

      UpperCamelCase__  =    checkpoint["all_modules.3.weight"].data
      UpperCamelCase__  =    checkpoint["all_modules.3.bias"].data

      UpperCamelCase__  =    checkpoint[list(checkpoint.keys()   )[-4]].data
      UpperCamelCase__  =    checkpoint[list(checkpoint.keys()   )[-3]].data
      UpperCamelCase__  =    checkpoint[list(checkpoint.keys()   )[-2]].data
      UpperCamelCase__  =    checkpoint[list(checkpoint.keys()   )[-1]].data

      UpperCamelCase__  =    4

      def set_attention_weights(__A				,      __A				,      __A   ):
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

      def set_resnet_weights(__A				,      __A				,      __A   ):
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
            UpperCamelCase__  =    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__  =    old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
                  UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

            if hasattr(__A				,      "resnet_up"   ) and block.resnet_up is not None:
                  UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.weight'''].data
                  UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.weight'''].data
                  UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up				,      __A				,      __A   )
                  module_index += 1

      UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.weight'''].data
      UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.weight'''].data
      UpperCamelCase__  =    checkpoint[F'''all_modules.{module_index}.bias'''].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
  a__  : Tuple				=	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__  : str				=	parser.parse_args()

  a__  : List[str]				=	torch.load(args.checkpoint_path, map_location='cpu')

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

  a__  : int				=	convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

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

  try:
    a__  : List[str]				=	ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

    a__  : List[Any]				=	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)









'''simple docstring'''







import math







def _UpperCamelCase	(					__A   )							->		bool:


      '''simple docstring'''



      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5				,      int(math.sqrt(__A   ) + 1   )				,      6   ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True







def _UpperCamelCase	(					__A = 1000				,      __A = 1000   )							->		int:


      '''simple docstring'''



      UpperCamelCase__  =    [0, 0, 0]  # length, a, b
      for a in range((a_limit * -1) + 1				,      __A   ):
            for b in range(2				,      __A   ):
                  if is_prime(__A   ):
                        UpperCamelCase__  =    0
                        UpperCamelCase__  =    0
                        while is_prime((n**2) + (a * n) + b   ):
                              count += 1
                              n += 1
                        if count > longest[0]:
                              UpperCamelCase__  =    [count, a, b]
      UpperCamelCase__  =    longest[1] * longest[2]
      return ans


if __name__ == "__main__":
  print(solution(1_0_0_0, 1_0_0_0))





