import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   UNetaDModel(**__lowerCamelCase						)
					__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(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
										__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(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
										__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(__lowerCamelCase      ,				'attentions'						)
										for j in range(len(block.resnets						)						):
															set_resnet_weights(block.resnets[j]      ,				__lowerCamelCase      ,				__lowerCamelCase						)
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j]      ,				__lowerCamelCase      ,				__lowerCamelCase						)
																				module_index += 1

										if hasattr(__lowerCamelCase      ,				'downsamplers'						) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down      ,				__lowerCamelCase      ,				__lowerCamelCase						)
															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]      ,				__lowerCamelCase      ,				__lowerCamelCase						)
					module_index += 1
					set_attention_weights(new_model_architecture.mid_block.attentions[0]      ,				__lowerCamelCase      ,				__lowerCamelCase						)
					module_index += 1
					set_resnet_weights(new_model_architecture.mid_block.resnets[1]      ,				__lowerCamelCase      ,				__lowerCamelCase						)
					module_index += 1

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

										if hasattr(__lowerCamelCase      ,				'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      ,				__lowerCamelCase      ,				__lowerCamelCase						)
															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__":
	lowerCamelCase_				:		str       						=       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.""",
	)

	lowerCamelCase_				:		Optional[Any]       						=       parser.parse_args()

	lowerCamelCase_				:		Dict       						=       torch.load(args.checkpoint_path, map_location="""cpu""")

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

	lowerCamelCase_				:		str       						=       convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

	lowerCamelCase_				:		Any       						=       UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

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

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





from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




