


"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      Dict	)							->	int:

	'''simple docstring'''


	snake_case__  :						Any		=      UNetaDModel(**_UpperCAmelCase	)
	snake_case__  :						List[str]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						Union[str, Any]		=      checkpoint["all_modules.0.W"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.1.bias"].data

	snake_case__  :						str		=      checkpoint["all_modules.2.weight"].data
	snake_case__  :						int		=      checkpoint["all_modules.2.bias"].data

	snake_case__  :						Tuple		=      checkpoint["all_modules.3.weight"].data
	snake_case__  :						Any		=      checkpoint["all_modules.3.bias"].data

	snake_case__  :						Any		=      checkpoint[list(checkpoint.keys()	)[-4]].data
	snake_case__  :						Dict		=      checkpoint[list(checkpoint.keys()	)[-3]].data
	snake_case__  :						List[str]		=      checkpoint[list(checkpoint.keys()	)[-2]].data
	snake_case__  :						Union[str, Any]		=      checkpoint[list(checkpoint.keys()	)[-1]].data

	snake_case__  :						Tuple		=      4

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

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

		snake_case__  :						Dict		=      old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		snake_case__  :						Tuple		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

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

		snake_case__  :						Optional[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
		snake_case__  :						List[Any]		=      old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
		snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
		snake_case__  :						Optional[int]		=      old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

		snake_case__  :						Any		=      old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
		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:
			snake_case__  :						List[str]		=      old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
			snake_case__  :						int		=      old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

	for i, block in enumerate(new_model_architecture.downsample_blocks	):
		snake_case__  :						Optional[int]		=      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
			snake_case__  :						Optional[Any]		=      checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
			snake_case__  :						Union[str, Any]		=      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	):
		snake_case__  :						str		=      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:
			snake_case__  :						Optional[int]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
			snake_case__  :						List[Any]		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			snake_case__  :						str		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
			snake_case__  :						Tuple		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			set_resnet_weights(block.resnet_up				,    _UpperCAmelCase				,    _UpperCAmelCase	)
			module_index += 1

	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Any		=      checkpoint[F"""all_modules.{module_index}.bias"""].data
	module_index += 1
	snake_case__  :						List[str]		=      checkpoint[F"""all_modules.{module_index}.weight"""].data
	snake_case__  :						Union[str, Any]		=      checkpoint[F"""all_modules.{module_index}.bias"""].data

	return new_model_architecture.state_dict()


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

					lowercase  		=			parser.parse_args()

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

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

					lowercase  		=			convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

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

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

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

										lowercase  		=			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


_A					:     List[Any]      ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Optional[Any]      =[
		    '''UMT5EncoderModel''',
		    '''UMT5ForConditionalGeneration''',
		    '''UMT5ForQuestionAnswering''',
		    '''UMT5Model''',
		    '''UMT5PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

		_A					:     Optional[int]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
