
"""simple docstring"""





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE            = OmegaConf.load(UpperCamelCase_				)
		if display:
				print(yaml.dump(OmegaConf.to_container(UpperCamelCase_				)				)				)
		return config



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=None  ,				UpperCamelCase_=None				):
		if conf_path is None:
				__SCREAMING_SNAKE_CASE            = """./model_checkpoints/vqgan_only.yaml"""
		__SCREAMING_SNAKE_CASE            = load_config(UpperCamelCase_  ,				display=UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = VQModel(**config.model.params				)
		if ckpt_path is None:
				__SCREAMING_SNAKE_CASE            = """./model_checkpoints/vqgan_only.pt"""
		__SCREAMING_SNAKE_CASE            = torch.load(UpperCamelCase_  ,				map_location=UpperCamelCase_				)
		if ".ckpt" in ckpt_path:
				__SCREAMING_SNAKE_CASE            = sd["""state_dict"""]
		model.load_state_dict(UpperCamelCase_  ,				strict=UpperCamelCase_				)
		model.to(UpperCamelCase_				)
		del sd
		return model



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = model.encode(UpperCamelCase_				)
		print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
		__SCREAMING_SNAKE_CASE            = model.decode(UpperCamelCase_				)
		return xrec



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = string.rsplit("""."""  ,				1				)
		if reload:
				__SCREAMING_SNAKE_CASE            = importlib.import_module(UpperCamelCase_				)
				importlib.reload(UpperCamelCase_				)
		return getattr(importlib.import_module(UpperCamelCase_  ,				package=UpperCamelCase_				)  ,				cls				)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		if "target" not in config:
				raise KeyError("""Expected key `target` to instantiate."""				)
		return get_obj_from_str(config["""target"""]				)(**config.get("""params"""  ,				{}				)				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=True  ,				UpperCamelCase_=True				):
		__SCREAMING_SNAKE_CASE            = instantiate_from_config(UpperCamelCase_				)
		if sd is not None:
				model.load_state_dict(UpperCamelCase_				)
		if gpu:
				model.cuda()
		if eval_mode:
				model.eval()
		return {"model": model}



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		# load the specified checkpoint
		if ckpt:
				__SCREAMING_SNAKE_CASE            = torch.load(UpperCamelCase_  ,				map_location="""cpu"""				)
				__SCREAMING_SNAKE_CASE            = pl_sd["""global_step"""]
				print(f"loaded model from global step {global_step}."				)
		else:
				__SCREAMING_SNAKE_CASE            = {"""state_dict""": None}
				__SCREAMING_SNAKE_CASE            = None
		__SCREAMING_SNAKE_CASE            = load_model_from_config(config.model  ,				pl_sd["""state_dict"""]  ,				gpu=UpperCamelCase_  ,				eval_mode=UpperCamelCase_				)["""model"""]
		return model, global_step






"""simple docstring"""





from __future__ import annotations

from fractions import Fraction



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = 11
		__SCREAMING_SNAKE_CASE            = int("""1""" + """0""" * digit_len				)
		for num in range(UpperCamelCase_  ,				UpperCamelCase_				):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(UpperCamelCase_  ,				UpperCamelCase_				):
										solutions.append(f"{num}/{den}"				)
						den += 1
				num += 1
				__SCREAMING_SNAKE_CASE            = 10
		return solutions



def 						_lowerCAmelCase				(				UpperCamelCase_ = 2				):
		__SCREAMING_SNAKE_CASE            = 1.0
		for fraction in fraction_list(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = Fraction(UpperCamelCase_				)
				result *= frac.denominator / frac.numerator
		return int(UpperCamelCase_				)


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




