'''simple docstring'''




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   Tuple					)     -> int:
							lowercase_   :     int									=			UNetaDModel(**UpperCAmelCase__					)
							lowercase_   :     str									=			checkpoint["""all_modules.0.W"""].data
							lowercase_   :     Optional[Any]									=			checkpoint["""all_modules.0.W"""].data
							lowercase_   :     int									=			checkpoint["""all_modules.1.weight"""].data
							lowercase_   :     Dict									=			checkpoint["""all_modules.1.bias"""].data

							lowercase_   :     Optional[int]									=			checkpoint["""all_modules.2.weight"""].data
							lowercase_   :     Union[str, Any]									=			checkpoint["""all_modules.2.bias"""].data

							lowercase_   :     str									=			checkpoint["""all_modules.3.weight"""].data
							lowercase_   :     Dict									=			checkpoint["""all_modules.3.bias"""].data

							lowercase_   :     Dict									=			checkpoint[list(checkpoint.keys()					)[-4]].data
							lowercase_   :     int									=			checkpoint[list(checkpoint.keys()					)[-3]].data
							lowercase_   :     Optional[Any]									=			checkpoint[list(checkpoint.keys()					)[-2]].data
							lowercase_   :     List[Any]									=			checkpoint[list(checkpoint.keys()					)[-1]].data

							lowercase_   :     List[Any]									=			4

							def set_attention_weights(UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   int					):
														lowercase_   :     Union[str, Any]									=			old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
														lowercase_   :     List[Any]									=			old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
														lowercase_   :     Optional[Any]									=			old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

														lowercase_   :     Optional[int]									=			old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
														lowercase_   :     Optional[Any]									=			old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
														lowercase_   :     Any									=			old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

														lowercase_   :     Optional[Any]									=			old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
														lowercase_   :     Union[str, Any]									=			old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

														lowercase_   :     Optional[int]									=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
														lowercase_   :     Tuple									=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

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

														lowercase_   :     List[str]									=			old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
														lowercase_   :     List[Any]									=			old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
														lowercase_   :     Union[str, Any]									=			old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
														lowercase_   :     Tuple									=			old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

														lowercase_   :     Union[str, Any]									=			old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
														lowercase_   :     Dict									=			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:
																					lowercase_   :     Optional[Any]									=			old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
																					lowercase_   :     List[Any]									=			old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

							lowercase_   :     List[Any]									=			checkpoint[F'''all_modules.{module_index}.weight'''].data
							lowercase_   :     Any									=			checkpoint[F'''all_modules.{module_index}.bias'''].data
							module_index += 1
							lowercase_   :     Tuple									=			checkpoint[F'''all_modules.{module_index}.weight'''].data
							lowercase_   :     Optional[int]									=			checkpoint[F'''all_modules.{module_index}.bias'''].data

							return new_model_architecture.state_dict()


if __name__ == "__main__":
						_lowercase		:					Optional[int]													=		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		:					List[Any]													=		parser.parse_args()

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

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

						_lowercase		:					Any													=		convert_ncsnpp_checkpoint(
						    checkpoint,
						    config,
						)

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

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

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

												_lowercase		:					Dict													=		ScoreSdeVePipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)
						except:  # noqa: E722
												model.save_pretrained(args.dump_path)






'''simple docstring'''




import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:					int													=		[(1,)]




def 				lowerCamelCase      ( )     -> Dict:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow:
														assert True
							else:
														assert False, "should have been skipped"




def 				lowerCamelCase      ( )     -> List[str]:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow and torch_device == "cuda":
														assert True
							else:
														assert False, "should have been skipped"





@require_torch
class        __magic_name__		(       unittest.TestCase):







			@slow
			@require_torch_gpu
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										check_slow_torch_cuda()







			@require_torch_gpu
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										check_slow_torch_cuda()







			@parameterized.expand(lowercase_       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    Dict=None       ):
										check_slow()




@slow
@require_torch_gpu
def 				lowerCamelCase      ( )     -> Union[str, Any]:
							check_slow_torch_cuda()




@require_torch_gpu
@slow
def 				lowerCamelCase      ( )     -> Any:
							check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param""" , [1]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> List[Any]:
							check_slow()




@pytest.mark.parametrize("""param""" , [1]					)
@slow
def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any]					)     -> Any:
							check_slow()





