

'''simple docstring'''

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 				A_					(	snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:str   =   UNetaDModel(**snake_case	)
  SCREAMING_SNAKE_CASE:List[str]   =   checkpoint["all_modules.0.W"].data
  SCREAMING_SNAKE_CASE:Any   =   checkpoint["all_modules.0.W"].data
  SCREAMING_SNAKE_CASE:Optional[int]   =   checkpoint["all_modules.1.weight"].data
  SCREAMING_SNAKE_CASE:Optional[Any]   =   checkpoint["all_modules.1.bias"].data

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

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

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

  SCREAMING_SNAKE_CASE:Optional[Any]   =   4

  def set_attention_weights(snake_case ,    snake_case ,    snake_case	):
    SCREAMING_SNAKE_CASE:Tuple   =   old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
    SCREAMING_SNAKE_CASE:int   =   old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
    SCREAMING_SNAKE_CASE:Optional[int]   =   old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

    SCREAMING_SNAKE_CASE:Any   =   old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
    SCREAMING_SNAKE_CASE:List[Any]   =   old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
    SCREAMING_SNAKE_CASE:Optional[int]   =   old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

    SCREAMING_SNAKE_CASE:List[Any]   =   old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
    SCREAMING_SNAKE_CASE:Any   =   old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

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

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

    SCREAMING_SNAKE_CASE:Optional[Any]   =   old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
    SCREAMING_SNAKE_CASE:List[Any]   =   old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
    SCREAMING_SNAKE_CASE:Optional[Any]   =   old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
    SCREAMING_SNAKE_CASE:str   =   old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

    SCREAMING_SNAKE_CASE:List[Any]   =   old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
    SCREAMING_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:
      SCREAMING_SNAKE_CASE:Dict   =   old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
      SCREAMING_SNAKE_CASE:Any   =   old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

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

  SCREAMING_SNAKE_CASE:Optional[Any]   =   checkpoint[F'''all_modules.{module_index}.weight'''].data
  SCREAMING_SNAKE_CASE:List[Any]   =   checkpoint[F'''all_modules.{module_index}.bias'''].data
  module_index += 1
  SCREAMING_SNAKE_CASE:List[Any]   =   checkpoint[F'''all_modules.{module_index}.weight'''].data
  SCREAMING_SNAKE_CASE:Dict   =   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)









'''simple docstring'''

from collections import Counter





def 				A_					(	snake_case	):
  return sum(socks_by_color // 2 for socks_by_color in Counter(snake_case	).values()	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A_      =							[int(x) for x in input("Enter socks by color :").rstrip().split()]
    print(f'''sock_merchant({colors}) = {sock_merchant(colors)}''')






