


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Any							,					UpperCamelCase__:     Dict						) ->   List[str]:





	"""simple docstring"""







	A								=				OmegaConf.load(UpperCamelCase__						)
	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)["""model"""]
	A								=				list(state_dict.keys()						)

	# extract state_dict for VQVAE
	A								=				{}
	A								=				"""first_stage_model."""
	for key in keys:
		if key.startswith(UpperCamelCase__						):
			A								=				state_dict[key]

    # extract state_dict for UNetLDM
	A								=				{}
	A								=				"""model.diffusion_model."""
	for key in keys:
		if key.startswith(UpperCamelCase__						):
			A								=				state_dict[key]

	A								=				config.model.params.first_stage_config.params
	A								=				config.model.params.unet_config.params

	A								=				VQModel(**UpperCamelCase__						).eval()
	vqvae.load_state_dict(UpperCamelCase__						)

	A								=				UNetLDMModel(**UpperCamelCase__						).eval()
	unet.load_state_dict(UpperCamelCase__						)

	A								=				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=UpperCamelCase__							,					)

	A								=				LDMPipeline(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
	pipeline.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
				_lowercase       :      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)
				_lowercase       :      Any	=	parser.parse_args()

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







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







	A								=				f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
	return requests.get(UpperCamelCase__						).json()





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







	A								=				"""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
	A								=				requests.get(UpperCamelCase__						).json()[:max_stories]
	return [get_hackernews_story(UpperCamelCase__						) for story_id in story_ids]





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







	A								=				hackernews_top_stories(UpperCamelCase__						)
	return "\n".join("""* [{title}]({url})""".format(**UpperCamelCase__						) for story in stories						)


if __name__ == "__main__":
				print(hackernews_top_stories_as_markdown())



