
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[Any]:
			lowerCAmelCase__ : Dict   =						UNetaDModel(**SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Union[str, Any]   =						checkpoint['all_modules.0.W'].data
			lowerCAmelCase__ : List[str]   =						checkpoint['all_modules.0.W'].data
			lowerCAmelCase__ : Any   =						checkpoint['all_modules.1.weight'].data
			lowerCAmelCase__ : Optional[Any]   =						checkpoint['all_modules.1.bias'].data

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

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

			lowerCAmelCase__ : int   =						checkpoint[list(checkpoint.keys()	)[-4]].data
			lowerCAmelCase__ : Any   =						checkpoint[list(checkpoint.keys()	)[-3]].data
			lowerCAmelCase__ : Tuple   =						checkpoint[list(checkpoint.keys()	)[-2]].data
			lowerCAmelCase__ : Optional[Any]   =						checkpoint[list(checkpoint.keys()	)[-1]].data

			lowerCAmelCase__ : str   =						4

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

						lowerCAmelCase__ : Tuple   =						old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
						lowerCAmelCase__ : List[Any]   =						old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
						lowerCAmelCase__ : str   =						old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

						lowerCAmelCase__ : Optional[int]   =						old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
						lowerCAmelCase__ : Tuple   =						old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

						lowerCAmelCase__ : List[str]   =						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
						lowerCAmelCase__ : int   =						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

			def set_resnet_weights(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	):
						lowerCAmelCase__ : Any   =						old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
						lowerCAmelCase__ : int   =						old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
						lowerCAmelCase__ : int   =						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
						lowerCAmelCase__ : List[Any]   =						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

						lowerCAmelCase__ : Optional[int]   =						old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
						lowerCAmelCase__ : Union[str, Any]   =						old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
						lowerCAmelCase__ : int   =						old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
						lowerCAmelCase__ : int   =						old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

						lowerCAmelCase__ : Union[str, Any]   =						old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
						lowerCAmelCase__ : 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:
									lowerCAmelCase__ : Any   =						old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
									lowerCAmelCase__ : int   =						old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

						if hasattr(SCREAMING_SNAKE_CASE_			,				'downsamplers'	) and block.downsamplers is not None:
									set_resnet_weights(block.resnet_down			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
									module_index += 1
									lowerCAmelCase__ : Optional[int]   =						checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
									lowerCAmelCase__ : 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]			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
			module_index += 1
			set_attention_weights(new_model_architecture.mid_block.attentions[0]			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
			module_index += 1
			set_resnet_weights(new_model_architecture.mid_block.resnets[1]			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
			module_index += 1

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

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

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

			return new_model_architecture.state_dict()


if __name__ == "__main__":
					lowerCamelCase__        =						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__        =						parser.parse_args()

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

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

					lowerCamelCase__        =						convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

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

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

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

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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__        =						{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RegNetForImageClassification""",
					    """RegNetModel""",
					    """RegNetPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRegNetForImageClassification""",
					    """TFRegNetModel""",
					    """TFRegNetPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """FlaxRegNetForImageClassification""",
					    """FlaxRegNetModel""",
					    """FlaxRegNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_regnet import (
										    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RegNetForImageClassification,
										    RegNetModel,
										    RegNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_regnet import (
										    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRegNetForImageClassification,
										    TFRegNetModel,
										    TFRegNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_regnet import (
										    FlaxRegNetForImageClassification,
										    FlaxRegNetModel,
										    FlaxRegNetPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase__        =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)