



'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Tuple			)		->						Any:
				snake_case__      :   List[Any]      =    UNetaDModel(**UpperCamelCase__			)
				snake_case__      :   int      =    checkpoint['''all_modules.0.W'''].data
				snake_case__      :   List[Any]      =    checkpoint['''all_modules.0.W'''].data
				snake_case__      :   Dict      =    checkpoint['''all_modules.1.weight'''].data
				snake_case__      :   Dict      =    checkpoint['''all_modules.1.bias'''].data

				snake_case__      :   Tuple      =    checkpoint['''all_modules.2.weight'''].data
				snake_case__      :   Optional[int]      =    checkpoint['''all_modules.2.bias'''].data

				snake_case__      :   Optional[Any]      =    checkpoint['''all_modules.3.weight'''].data
				snake_case__      :   List[str]      =    checkpoint['''all_modules.3.bias'''].data

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

				snake_case__      :   Any      =    4

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

								snake_case__      :   List[str]      =    old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
								snake_case__      :   Tuple      =    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__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

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

								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
								snake_case__      :   Union[str, Any]      =    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__      :   int      =    old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
								snake_case__      :   Optional[int]      =    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__      :   Any      =    old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
												snake_case__      :   Any      =    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__      :   Any      =    checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
												snake_case__      :   Optional[int]      =    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__      :   Optional[Any]      =    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__      :   Union[str, Any]      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
												snake_case__      :   List[str]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												snake_case__      :   str      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
												snake_case__      :   Union[str, Any]      =    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[Any]      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
				snake_case__      :   Dict      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
				module_index += 1
				snake_case__      :   Dict      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
				snake_case__      :   List[str]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
				_lowercase       :						Tuple						=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       :						Any						=parser.parse_args()

				_lowercase       :						List[Any]						=torch.load(args.checkpoint_path, map_location="cpu")

				with open(args.config_file) as f:
								_lowercase       :						List[Any]						=json.loads(f.read())

				_lowercase       :						List[str]						=convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

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

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

								_lowercase       :						List[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


_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__)
