

from __future__ import annotations

import bisect



def       __lowerCAmelCase      (						A       ,     A       ,     A = 0       ,     A = -1    ):

	if hi < 0:
		UpperCAmelCase_       				=			len(A    )

	while lo < hi:
		UpperCAmelCase_       				=			lo + (hi - lo) // 2
		if sorted_collection[mid] < item:
			UpperCAmelCase_       				=			mid + 1
		else:
			UpperCAmelCase_       				=			mid

	return lo



def       __lowerCAmelCase      (						A       ,     A       ,     A = 0       ,     A = -1    ):

	if hi < 0:
		UpperCAmelCase_       				=			len(A    )

	while lo < hi:
		UpperCAmelCase_       				=			lo + (hi - lo) // 2
		if sorted_collection[mid] <= item:
			UpperCAmelCase_       				=			mid + 1
		else:
			UpperCAmelCase_       				=			mid

	return lo



def       __lowerCAmelCase      (						A       ,     A       ,     A = 0       ,     A = -1    ):
	sorted_collection.insert(bisect_left(A       ,     A       ,     A       ,     A    )       ,     A    )



def       __lowerCAmelCase      (						A       ,     A       ,     A = 0       ,     A = -1    ):
	sorted_collection.insert(bisect_right(A       ,     A       ,     A       ,     A    )       ,     A    )



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			0
	UpperCAmelCase_       				=			len(A    ) - 1

	while left <= right:
		UpperCAmelCase_       				=			left + (right - left) // 2
		UpperCAmelCase_       				=			sorted_collection[midpoint]
		if current_item == item:
			return midpoint
		elif item < current_item:
			UpperCAmelCase_       				=			midpoint - 1
		else:
			UpperCAmelCase_       				=			midpoint + 1
	return None



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			bisect.bisect_left(A       ,     A    )
	if index != len(A    ) and sorted_collection[index] == item:
		return index
	return None



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):

	if right < left:
		return None

	UpperCAmelCase_       				=			left + (right - left) // 2

	if sorted_collection[midpoint] == item:
		return midpoint
	elif sorted_collection[midpoint] > item:
		return binary_search_by_recursion(A       ,     A       ,     A       ,     midpoint - 1    )
	else:
		return binary_search_by_recursion(A       ,     A       ,     midpoint + 1       ,     A    )


if __name__ == "__main__":
				_a:    Tuple 							=			input("""Enter numbers separated by comma:\n""").strip()
				_a:    List[Any] 							=			sorted(int(item) for item in user_input.split(""","""))
				_a:    Tuple 							=			int(input("""Enter a single number to be found in the list:\n"""))
				_a:    Dict 							=			binary_search(collection, target)
				if result is None:
								print(F'{target} was not found in {collection}.')
				else:
								print(F'{target} was found at position {result} in {collection}.')









import importlib

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



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



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



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			model.encode(A    )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"    )
	UpperCAmelCase_       				=			model.decode(A    )
	return xrec



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



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



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