



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  OmegaConf.load(snake_case__					)
				lowerCAmelCase__		  =  torch.load(snake_case__    ,			map_location='cpu'					)['model']
				lowerCAmelCase__		  =  list(state_dict.keys()					)

				# extract state_dict for VQVAE
				lowerCAmelCase__		  =  {}
				lowerCAmelCase__		  =  'first_stage_model.'
				for key in keys:
								if key.startswith(snake_case__					):
												lowerCAmelCase__		  =  state_dict[key]

    # extract state_dict for UNetLDM
				lowerCAmelCase__		  =  {}
				lowerCAmelCase__		  =  'model.diffusion_model.'
				for key in keys:
								if key.startswith(snake_case__					):
												lowerCAmelCase__		  =  state_dict[key]

				lowerCAmelCase__		  =  config.model.params.first_stage_config.params
				lowerCAmelCase__		  =  config.model.params.unet_config.params

				lowerCAmelCase__		  =  VQModel(**snake_case__					).eval()
				vqvae.load_state_dict(snake_case__					)

				lowerCAmelCase__		  =  UNetLDMModel(**snake_case__					).eval()
				unet.load_state_dict(snake_case__					)

				lowerCAmelCase__		  =  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__    ,			)

				lowerCAmelCase__		  =  LDMPipeline(snake_case__    ,			snake_case__    ,			snake_case__					)
				pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   Tuple    =   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)
	_lowerCAmelCase	:   List[Any]    =   parser.parse_args()

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





from typing import TYPE_CHECKING

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


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
