






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def   lowercase__   (  __snake_case					:							str  , __snake_case					:							List[str]						):


					'''simple docstring'''


					UpperCAmelCase_					:   List[Any]   =		UNetaDModel(**__snake_case						)
					UpperCAmelCase_					:   Optional[Any]   =		checkpoint['all_modules.0.W'].data
					UpperCAmelCase_					:   Union[str, Any]   =		checkpoint['all_modules.0.W'].data
					UpperCAmelCase_					:   Optional[Any]   =		checkpoint['all_modules.1.weight'].data
					UpperCAmelCase_					:   Tuple   =		checkpoint['all_modules.1.bias'].data

					UpperCAmelCase_					:   Optional[Any]   =		checkpoint['all_modules.2.weight'].data
					UpperCAmelCase_					:   List[Any]   =		checkpoint['all_modules.2.bias'].data

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

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

					UpperCAmelCase_					:   List[str]   =		4

					def set_attention_weights(__snake_case					:							Any  , __snake_case					:							Dict  , __snake_case					:							Optional[int]						):
										UpperCAmelCase_					:   List[str]   =		old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
										UpperCAmelCase_					:   List[str]   =		old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
										UpperCAmelCase_					:   Tuple   =		old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

										UpperCAmelCase_					:   str   =		old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
										UpperCAmelCase_					:   Optional[int]   =		old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
										UpperCAmelCase_					:   Dict   =		old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

										UpperCAmelCase_					:   Optional[int]   =		old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
										UpperCAmelCase_					:   Union[str, Any]   =		old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

										UpperCAmelCase_					:   Optional[Any]   =		old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
										UpperCAmelCase_					:   int   =		old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

					def set_resnet_weights(__snake_case					:							str  , __snake_case					:							Optional[int]  , __snake_case					:							Union[str, Any]						):
										UpperCAmelCase_					:   Tuple   =		old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
										UpperCAmelCase_					:   Union[str, Any]   =		old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
										UpperCAmelCase_					:   Any   =		old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
										UpperCAmelCase_					:   List[str]   =		old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

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

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

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

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

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

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

										if hasattr(__snake_case  , 'resnet_up'						) and block.resnet_up is not None:
															UpperCAmelCase_					:   Optional[int]   =		checkpoint[F"all_modules.{module_index}.weight"].data
															UpperCAmelCase_					:   str   =		checkpoint[F"all_modules.{module_index}.bias"].data
															module_index += 1
															UpperCAmelCase_					:   List[str]   =		checkpoint[F"all_modules.{module_index}.weight"].data
															UpperCAmelCase_					:   Tuple   =		checkpoint[F"all_modules.{module_index}.bias"].data
															module_index += 1
															set_resnet_weights(block.resnet_up  , __snake_case  , __snake_case						)
															module_index += 1

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














import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
		import transformer_engine.pytorch as te




def   lowercase__   (  __snake_case					:							List[str]  , __snake_case					:							List[Any]=True  , __snake_case					:							str=True  , __snake_case					:							Tuple=True						):


					'''simple docstring'''


					if not is_fpa_available():
										raise ImportError('Using `convert_model` requires transformer_engine to be installed.'						)
					for name, module in model.named_children():
										if isinstance(__snake_case  , nn.Linear						) and to_transformer_engine and _convert_linear:
															# Return early if the linear layer weights are not multiples of 16
															if any(p % 16 != 0 for p in module.weight.shape						):
																				return
															UpperCAmelCase_					:   Optional[int]   =		module.bias is not None
															UpperCAmelCase_					:   Optional[int]   =		te.Linear(
															    module.in_features  , module.out_features  , bias=__snake_case  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   List[str]   =		module.weight.data.clone()
															if has_bias:
																				UpperCAmelCase_					:   List[Any]   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , nn.LayerNorm						) and to_transformer_engine and _convert_ln:
															UpperCAmelCase_					:   int   =		te.LayerNorm(module.normalized_shape[0]  , eps=module.eps  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   Any   =		module.weight.data.clone()
															UpperCAmelCase_					:   Any   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , te.Linear						) and not to_transformer_engine and _convert_linear:
															UpperCAmelCase_					:   int   =		module.bias is not None
															UpperCAmelCase_					:   Tuple   =		nn.Linear(
															    module.in_features  , module.out_features  , bias=__snake_case  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   str   =		module.weight.data.clone()
															if has_bias:
																				UpperCAmelCase_					:   Dict   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , te.LayerNorm						) and not to_transformer_engine and _convert_ln:
															UpperCAmelCase_					:   List[Any]   =		nn.LayerNorm(module.normalized_shape[0]  , eps=module.eps  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   List[str]   =		module.weight.data.clone()
															UpperCAmelCase_					:   Tuple   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										else:
															convert_model(
															    __snake_case  , to_transformer_engine=__snake_case  , _convert_linear=_convert_linear  , _convert_ln=_convert_ln  , )






def   lowercase__   (  __snake_case					:							List[Any]						):


					'''simple docstring'''


					if not is_fpa_available():
										raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'						)
					for m in model.modules():
										if isinstance(__snake_case  , (te.LayerNorm, te.Linear)						):
															return True
					return False






