





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def _A   (    __magic_name__  ,					__magic_name__       ):
   lowercase__    =  UNetaDModel(**__magic_name__       )
   lowercase__    =  checkpoint["all_modules.0.W"].data
   lowercase__    =  checkpoint["all_modules.0.W"].data
   lowercase__    =  checkpoint["all_modules.1.weight"].data
   lowercase__    =  checkpoint["all_modules.1.bias"].data

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

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

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

   lowercase__    =  4

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

      lowercase__    =  old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

      lowercase__    =  old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

      lowercase__    =  old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

   def set_resnet_weights(__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

      lowercase__    =  old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
      lowercase__    =  old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

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

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

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

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

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

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

   lowercase__    =  checkpoint[f'''all_modules.{module_index}.weight'''].data
   lowercase__    =  checkpoint[f'''all_modules.{module_index}.bias'''].data
   module_index += 1
   lowercase__    =  checkpoint[f'''all_modules.{module_index}.weight'''].data
   lowercase__    =  checkpoint[f'''all_modules.{module_index}.bias'''].data

   return new_model_architecture.state_dict()


if __name__ == "__main__":
  _snake_case				= 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.""",
  )

  _snake_case				= parser.parse_args()

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

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

  _snake_case				= convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

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

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

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













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case				= {"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RegNetForImageClassification""",
      """RegNetModel""",
      """RegNetPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRegNetForImageClassification""",
      """TFRegNetModel""",
      """TFRegNetPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """FlaxRegNetForImageClassification""",
      """FlaxRegNetModel""",
      """FlaxRegNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






