

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						UNetaDModel(**__A	)
    SCREAMING_SNAKE_CASE__					     =						checkpoint["all_modules.0.W"].data
    SCREAMING_SNAKE_CASE__					     =						checkpoint["all_modules.0.W"].data
    SCREAMING_SNAKE_CASE__					     =						checkpoint["all_modules.1.weight"].data
    SCREAMING_SNAKE_CASE__					     =						checkpoint["all_modules.1.bias"].data

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

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

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

    SCREAMING_SNAKE_CASE__					     =						4

    def set_attention_weights(__A ,       __A ,       __A	):
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

    def set_resnet_weights(__A ,       __A ,       __A	):
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

        SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
        SCREAMING_SNAKE_CASE__					     =						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__					     =						old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
            SCREAMING_SNAKE_CASE__					     =						old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

    for i, block in enumerate(new_model_architecture.downsample_blocks	):
        SCREAMING_SNAKE_CASE__					     =						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
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
            SCREAMING_SNAKE_CASE__					     =						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	):
        SCREAMING_SNAKE_CASE__					     =						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:
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.weight"].data
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.bias"].data
            module_index += 1
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.weight"].data
            SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.bias"].data
            module_index += 1
            set_resnet_weights(block.resnet_up ,       __A ,       __A	)
            module_index += 1

    SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.weight"].data
    SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.bias"].data
    module_index += 1
    SCREAMING_SNAKE_CASE__					     =						checkpoint[f"all_modules.{module_index}.weight"].data
    SCREAMING_SNAKE_CASE__					     =						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)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__        =			logging.get_logger(__name__)

A__        =			{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      """convnext"""



  def __init__(	self    :				Optional[Any]     ,  A_    :				Any=3     ,  A_    :				Tuple=4     ,  A_    :				Optional[int]=4     ,  A_    :				Dict=None     ,  A_    :				List[Any]=None     ,  A_    :				Union[str, Any]="gelu"     ,  A_    :				Optional[Any]=0.02     ,  A_    :				Union[str, Any]=1E-1_2     ,  A_    :				Dict=1E-6     ,  A_    :				List[str]=0.0     ,  A_    :				int=224     ,  A_    :				Optional[Any]=None     ,  A_    :				List[str]=None     ,  **A_    :				Union[str, Any]     ,  )					-> Tuple:
      super().__init__(**A_	)

      SCREAMING_SNAKE_CASE__					     =						num_channels
      SCREAMING_SNAKE_CASE__					     =						patch_size
      SCREAMING_SNAKE_CASE__					     =						num_stages
      SCREAMING_SNAKE_CASE__					     =						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE__					     =						[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE__					     =						hidden_act
      SCREAMING_SNAKE_CASE__					     =						initializer_range
      SCREAMING_SNAKE_CASE__					     =						layer_norm_eps
      SCREAMING_SNAKE_CASE__					     =						layer_scale_init_value
      SCREAMING_SNAKE_CASE__					     =						drop_path_rate
      SCREAMING_SNAKE_CASE__					     =						image_size
      SCREAMING_SNAKE_CASE__					     =						["stem"] + [f"stage{idx}" for idx in range(1     ,  len(self.depths	) + 1	)]
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						get_aligned_output_features_output_indices(
          out_features=A_     ,  out_indices=A_     ,  stage_names=self.stage_names	)







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      version.parse("""1.11"""			)



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]	)







  @property
  def      _UpperCamelCase    (	self    :				Tuple	)					-> float:
      return 1E-5


