


import importlib

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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		OmegaConf.load(lowercase__      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase__      )      )      )
						return config


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		model.encode(lowercase__      )
						print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		model.decode(lowercase__      )
						return xrec


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		string.rsplit('''.'''    ,       1      )
						if reload:
												__SCREAMING_SNAKE_CASE						:     int					=		importlib.import_module(lowercase__      )
												importlib.reload(lowercase__      )
						return getattr(importlib.import_module(lowercase__    ,       package=lowercase__      )    ,       cls      )


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


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


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


class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()