


'''simple docstring'''


import importlib

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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase						:				Optional[int]	    =					OmegaConf.load(_lowerCamelCase      )
		if display:
				print(yaml.dump(OmegaConf.to_container(_lowerCamelCase      )      )      )
		return config






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






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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase						:				Any	    =					string.rsplit("."					,       1      )
		if reload:
				_UpperCAmelCase						:				List[Any]	    =					importlib.import_module(_lowerCamelCase      )
				importlib.reload(_lowerCamelCase      )
		return getattr(importlib.import_module(_lowerCamelCase					,       package=_lowerCamelCase      )					,       cls      )






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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=True					,       __lowerCAmelCase=True      ):
		_UpperCAmelCase						:				Optional[Any]	    =					instantiate_from_config(_lowerCamelCase      )
		if sd is not None:
				model.load_state_dict(_lowerCamelCase      )
		if gpu:
				model.cuda()
		if eval_mode:
				model.eval()
		return {"model": model}






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		# load the specified checkpoint
		if ckpt:
				_UpperCAmelCase						:				int	    =					torch.load(_lowerCamelCase					,       map_location="cpu"      )
				_UpperCAmelCase						:				Tuple	    =					pl_sd["""global_step"""]
				print(F"""loaded model from global step {global_step}."""      )
		else:
				_UpperCAmelCase						:				str	    =					{"""state_dict""": None}
				_UpperCAmelCase						:				List[Any]	    =					None
		_UpperCAmelCase						:				Union[str, Any]	    =					load_model_from_config(config.model					,       pl_sd["state_dict"]					,       gpu=_lowerCamelCase					,       eval_mode=_lowerCamelCase      )["""model"""]
		return model, global_step








'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')