
"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     OmegaConf.load(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     torch.load(_snake_case     ,map_location="""cpu"""		)["""model"""]
							SCREAMING_SNAKE_CASE__		: Dict     =     list(state_dict.keys()		)

							# extract state_dict for VQVAE
							SCREAMING_SNAKE_CASE__		: int     =     {}
							SCREAMING_SNAKE_CASE__		: Dict     =     """first_stage_model."""
							for key in keys:
														if key.startswith(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     state_dict[key]

    # extract state_dict for UNetLDM
							SCREAMING_SNAKE_CASE__		: str     =     {}
							SCREAMING_SNAKE_CASE__		: str     =     """model.diffusion_model."""
							for key in keys:
														if key.startswith(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: Tuple     =     state_dict[key]

							SCREAMING_SNAKE_CASE__		: List[str]     =     config.model.params.first_stage_config.params
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     config.model.params.unet_config.params

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     VQModel(**_snake_case		).eval()
							vqvae.load_state_dict(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     UNetLDMModel(**_snake_case		).eval()
							unet.load_state_dict(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     DDIMScheduler(
							    timesteps=config.model.params.timesteps     ,beta_schedule="""scaled_linear"""     ,beta_start=config.model.params.linear_start     ,beta_end=config.model.params.linear_end     ,clip_sample=_snake_case     ,)

							SCREAMING_SNAKE_CASE__		: Any     =     LDMPipeline(_snake_case     ,_snake_case     ,_snake_case		)
							pipeline.save_pretrained(_snake_case		)


if __name__ == "__main__":
					UpperCAmelCase__   :					List[Any]												=				argparse.ArgumentParser()
					parser.add_argument('--checkpoint_path', type=str, required=True)
					parser.add_argument('--config_path', type=str, required=True)
					parser.add_argument('--output_path', type=str, required=True)
					UpperCAmelCase__   :					Union[str, Any]												=				parser.parse_args()

					convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__   :					Union[str, Any]												=				_LazyModule(__name__, globals()['__file__'], _import_structure)




