






'''simple docstring'''





import argparse
import json

import torch

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

			UpperCAmelCase_     :   int       =   checkpoint['''all_modules.2.weight'''].data
			UpperCAmelCase_     :   Union[str, Any]       =   checkpoint['''all_modules.2.bias'''].data

			UpperCAmelCase_     :   Dict       =   checkpoint['''all_modules.3.weight'''].data
			UpperCAmelCase_     :   Tuple       =   checkpoint['''all_modules.3.bias'''].data

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

			UpperCAmelCase_     :   Optional[int]       =   4

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

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

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

						UpperCAmelCase_     :   List[str]       =   old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
						UpperCAmelCase_     :   Dict       =   old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

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

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

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

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

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

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

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

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

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

			return new_model_architecture.state_dict()


if __name__ == "__main__":
						_UpperCamelCase		:		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.',
						)

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

						_UpperCamelCase		:		str								=			torch.load(args.checkpoint_path, map_location='cpu')

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

						_UpperCamelCase		:		Dict								=			convert_ncsnpp_checkpoint(
						    checkpoint,
						    config,
						)

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

						_UpperCamelCase		:		Optional[int]								=			UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

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

												_UpperCamelCase		:		Optional[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_sentencepiece_available, is_tokenizers_available


_UpperCamelCase		:		str								=			{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[Any]								=			['MBart50Tokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[str]								=			['MBart50TokenizerFast']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa import MBartaaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
						import sys

						_UpperCamelCase		:		Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


