

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Optional[int]=False       )					->				Optional[Any]:
	__A :			Tuple		    =			OmegaConf.load(a__       )
	if display:
		print(yaml.dump(OmegaConf.to_container(a__       )       )       )
	return config
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Dict=None			,a__    :   List[str]=None       )					->				Optional[int]:
	if conf_path is None:
		__A :			str		    =			"""./model_checkpoints/vqgan_only.yaml"""
	__A :			List[Any]		    =			load_config(a__			,display=a__       )
	__A :			Any		    =			VQModel(**config.model.params       )
	if ckpt_path is None:
		__A :			str		    =			"""./model_checkpoints/vqgan_only.pt"""
	__A :			List[str]		    =			torch.load(a__			,map_location=a__       )
	if ".ckpt" in ckpt_path:
		__A :			List[str]		    =			sd["""state_dict"""]
	model.load_state_dict(a__			,strict=a__       )
	model.to(a__       )
	del sd
	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict			,a__    :   Optional[int]       )					->				Any:
	__A    ,				__A    ,				__A :			Union[str, Any]		    =			model.encode(a__       )
	print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""       )
	__A :			Optional[int]		    =			model.decode(a__       )
	return xrec
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   List[Any]=False       )					->				Optional[int]:
	__A    ,				__A :			Optional[int]		    =			string.rsplit("""."""			,1       )
	if reload:
		__A :			Tuple		    =			importlib.import_module(a__       )
		importlib.reload(a__       )
	return getattr(importlib.import_module(a__			,package=a__       )			,cls       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]       )					->				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__    :   Optional[int]			,a__    :   Any			,a__    :   Dict=True			,a__    :   int=True       )					->				List[str]:
	__A :			Any		    =			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__    :   str			,a__    :   Tuple			,a__    :   int			,a__    :   int       )					->				Optional[Any]:
	# load the specified checkpoint
	if ckpt:
		__A :			List[str]		    =			torch.load(a__			,map_location="""cpu"""       )
		__A :			str		    =			pl_sd["""global_step"""]
		print(f"""loaded model from global step {global_step}."""       )
	else:
		__A :			str		    =			{"""state_dict""": None}
		__A :			str		    =			None
	__A :			Dict		    =			load_model_from_config(config.model			,pl_sd["""state_dict"""]			,gpu=a__			,eval_mode=a__       )["""model"""]
	return model, global_step








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			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

		__A :			List[Any]		    =			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)}""")





