





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     OmegaConf.load(lowercase				)
							lowercase_	:   int  				=     torch.load(lowercase				,       map_location="""cpu"""				)["""model"""]
							lowercase_	:   int  				=     list(state_dict.keys()				)

							# extract state_dict for VQVAE
							lowercase_	:   List[Any]  				=     {}
							lowercase_	:   Union[str, Any]  				=     """first_stage_model."""
							for key in keys:
														if key.startswith(lowercase				):
																					lowercase_	:   str  				=     state_dict[key]

    # extract state_dict for UNetLDM
							lowercase_	:   str  				=     {}
							lowercase_	:   List[Any]  				=     """model.diffusion_model."""
							for key in keys:
														if key.startswith(lowercase				):
																					lowercase_	:   List[str]  				=     state_dict[key]

							lowercase_	:   str  				=     config.model.params.first_stage_config.params
							lowercase_	:   List[str]  				=     config.model.params.unet_config.params

							lowercase_	:   str  				=     VQModel(**lowercase				).eval()
							vqvae.load_state_dict(lowercase				)

							lowercase_	:   Optional[int]  				=     UNetLDMModel(**lowercase				).eval()
							unet.load_state_dict(lowercase				)

							lowercase_	:   str  				=     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=lowercase				,       )

							lowercase_	:   Optional[int]  				=     LDMPipeline(lowercase				,       lowercase				,       lowercase				)
							pipeline.save_pretrained(lowercase				)


if __name__ == "__main__":
							UpperCAmelCase_							=				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_							=				parser.parse_args()

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






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline





@pytest.mark.integration
def  __magic_name__  (				)				-> Union[str, Any]:

							"""simple docstring"""



							with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT				):
														with pytest.raises(lowercase				):
																					requests.request("""GET"""				,       """https://huggingface.co"""				)
														with pytest.raises(requests.exceptions.ConnectTimeout				):
																					requests.request("""GET"""				,       """https://huggingface.co"""				,       timeout=1.0				)





@pytest.mark.integration
def  __magic_name__  (				)				-> Union[str, Any]:

							"""simple docstring"""



							with offline(OfflineSimulationMode.CONNECTION_FAILS				):
														with pytest.raises(requests.exceptions.ConnectionError				):
																					requests.request("""GET"""				,       """https://huggingface.co"""				)





def  __magic_name__  (				)				-> Dict:

							"""simple docstring"""



							with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1				):
														with pytest.raises(lowercase				):
																					http_head("""https://huggingface.co"""				)