




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Any:







				'''simple docstring'''


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

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

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

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

				__lowerCAmelCase					=  4

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

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

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

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

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

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

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

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

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

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

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

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

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

				return new_model_architecture.state_dict()


if __name__ == "__main__":
	__A						:		Any          =						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						:		Union[str, Any]          =						json.loads(f.read())

	__A						:		Union[str, Any]          =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

	__A						:		Tuple          =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__A						:		Union[str, Any]          =						ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase:		Tuple    						=  {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[str]    						=  ["ImageGPTFeatureExtractor"]
       __lowercase:		Tuple    						=  ["ImageGPTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		str    						=  [
           "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ImageGPTForCausalImageModeling",
           "ImageGPTForImageClassification",
           "ImageGPTModel",
           "ImageGPTPreTrainedModel",
           "load_tf_weights_in_imagegpt",
       ]


if TYPE_CHECKING:
       from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
              from .image_processing_imagegpt import ImageGPTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_imagegpt import (
                  IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ImageGPTForCausalImageModeling,
                  ImageGPTForImageClassification,
                  ImageGPTModel,
                  ImageGPTPreTrainedModel,
                  load_tf_weights_in_imagegpt,
              )

else:
       import sys

       __lowercase:		List[str]    						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)