






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 		_a					(   a				:Tuple			,							a				:Union[str, Any]				)							->    Union[str, Any]:
 a							=   UNetaDModel(**a				)
 a							=   checkpoint["all_modules.0.W"].data
 a							=   checkpoint["all_modules.0.W"].data
 a							=   checkpoint["all_modules.1.weight"].data
 a							=   checkpoint["all_modules.1.bias"].data

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

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

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

 a							=   4

 def set_attention_weights(a				:List[str]			,							a				:Dict			,							a				:Dict				):
  a							=   old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
  a							=   old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
  a							=   old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

  a							=   old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

  a							=   old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
  a							=   old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

  a							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

 def set_resnet_weights(a				:Optional[Any]			,							a				:Optional[Any]			,							a				:int				):
  a							=   old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

  a							=   old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
  a							=   old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

  a							=   old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
  a							=   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:
   a							=   old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
   a							=   old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

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

 a							=   checkpoint[F"""all_modules.{module_index}.weight"""].data
 a							=   checkpoint[F"""all_modules.{module_index}.bias"""].data
 module_index += 1
 a							=   checkpoint[F"""all_modules.{module_index}.weight"""].data
 a							=   checkpoint[F"""all_modules.{module_index}.bias"""].data

 return new_model_architecture.state_dict()


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

     UpperCAmelCase__            =	parser.parse_args()

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

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

     UpperCAmelCase__            =	convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

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

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

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

          UpperCAmelCase__            =	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_torch_available


UpperCAmelCase__            =	{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "VisualBertForMultipleChoice",
         "VisualBertForPreTraining",
         "VisualBertForQuestionAnswering",
         "VisualBertForRegionToPhraseAlignment",
         "VisualBertForVisualReasoning",
         "VisualBertLayer",
         "VisualBertModel",
         "VisualBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_visual_bert import (
              VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              VisualBertForMultipleChoice,
              VisualBertForPreTraining,
              VisualBertForQuestionAnswering,
              VisualBertForRegionToPhraseAlignment,
              VisualBertForVisualReasoning,
              VisualBertLayer,
              VisualBertModel,
              VisualBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


