
'''simple docstring'''







import importlib

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=False			) ->			Any:
	_snake_case     		=	OmegaConf.load(__A			)
	if display:
		print(yaml.dump(OmegaConf.to_container(__A			)			)			)
	return config







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=None    ,       __A=None			) ->			str:
	if conf_path is None:
		_snake_case     		=	'./model_checkpoints/vqgan_only.yaml'
	_snake_case     		=	load_config(__A    ,       display=__A			)
	_snake_case     		=	VQModel(**config.model.params			)
	if ckpt_path is None:
		_snake_case     		=	'./model_checkpoints/vqgan_only.pt'
	_snake_case     		=	torch.load(__A    ,       map_location=__A			)
	if ".ckpt" in ckpt_path:
		_snake_case     		=	sd['state_dict']
	model.load_state_dict(__A    ,       strict=__A			)
	model.to(__A			)
	del sd
	return model







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Union[str, Any]:
	_snake_case		,				_snake_case		,				_snake_case     		=	model.encode(__A			)
	print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
	_snake_case     		=	model.decode(__A			)
	return xrec







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=False			) ->			List[str]:
	_snake_case		,				_snake_case     		=	string.rsplit('.'    ,       1			)
	if reload:
		_snake_case     		=	importlib.import_module(__A			)
		importlib.reload(__A			)
	return getattr(importlib.import_module(__A    ,       package=__A			)    ,       cls			)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[int]:
	if "target" not in config:
		raise KeyError('Expected key `target` to instantiate.'			)
	return get_obj_from_str(config['target']			)(**config.get('params'    ,       {}			)			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=True    ,       __A=True			) ->			List[Any]:
	_snake_case     		=	instantiate_from_config(__A			)
	if sd is not None:
		model.load_state_dict(__A			)
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A			) ->			Any:
	# load the specified checkpoint
	if ckpt:
		_snake_case     		=	torch.load(__A    ,       map_location='cpu'			)
		_snake_case     		=	pl_sd['global_step']
		print(F'loaded model from global step {global_step}.'			)
	else:
		_snake_case     		=	{'state_dict': None}
		_snake_case     		=	None
	_snake_case     		=	load_model_from_config(config.model    ,       pl_sd['state_dict']    ,       gpu=__A    ,       eval_mode=__A			)['model']
	return model, global_step



'''simple docstring'''







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = "x"    ,       __A = 10**-10    ,       __A = 1    ,       ) ->			complex:
	_snake_case     		=	symbols(__A			)
	_snake_case     		=	lambdify(__A    ,       __A			)
	_snake_case     		=	lambdify(__A    ,       diff(__A    ,       __A			)			)

	_snake_case     		=	starting_point

	while True:
		if diff_function(__A			) != 0:
			_snake_case     		=	prev_guess - multiplicity * func(__A			) / diff_function(
			    __A			)
		else:
			raise ZeroDivisionError('Could not find root'			) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess			) < precision:
			return next_guess

		_snake_case     		=	next_guess


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

					# Find root of polynomial
					# Find fourth Root of 5
					print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
					)

					# Find root of cos(x)
					print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

