

'''simple docstring'''

import importlib

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

def  __UpperCamelCase							(		lowercase__  :     Dict,    lowercase__  :     Optional[Any]=False       ):






     '''simple docstring'''





     __lowercase							=OmegaConf.load(UpperCamelCase_       )
     if display:
          print(yaml.dump(OmegaConf.to_container(UpperCamelCase_       )       )       )
     return config

def  __UpperCamelCase							(		lowercase__  :     List[str],    lowercase__  :     Optional[int]=None,    lowercase__  :     Optional[int]=None       ):






     '''simple docstring'''





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

def  __UpperCamelCase							(		lowercase__  :     Union[str, Any],    lowercase__  :     Optional[int]       ):






     '''simple docstring'''





     __lowercase						,   __lowercase						,   __lowercase							=model.encode(UpperCamelCase_       )
     print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''       )
     __lowercase							=model.decode(UpperCamelCase_       )
     return xrec

def  __UpperCamelCase							(		lowercase__  :     Union[str, Any],    lowercase__  :     Union[str, Any]=False       ):






     '''simple docstring'''





     __lowercase						,   __lowercase							=string.rsplit('.',    1       )
     if reload:
          __lowercase							=importlib.import_module(UpperCamelCase_       )
          importlib.reload(UpperCamelCase_       )
     return getattr(importlib.import_module(UpperCamelCase_,    package=UpperCamelCase_       ),    cls       )

def  __UpperCamelCase							(		lowercase__  :     List[str]       ):






     '''simple docstring'''





     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__  :     List[Any],    lowercase__  :     str,    lowercase__  :     Tuple=True,    lowercase__  :     Any=True       ):






     '''simple docstring'''





     __lowercase							=instantiate_from_config(UpperCamelCase_       )
     if sd is not None:
          model.load_state_dict(UpperCamelCase_       )
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def  __UpperCamelCase							(		lowercase__  :     Union[str, Any],    lowercase__  :     List[str],    lowercase__  :     Union[str, Any],    lowercase__  :     Any       ):






     '''simple docstring'''





     if ckpt:
          __lowercase							=torch.load(UpperCamelCase_,    map_location='cpu'       )
          __lowercase							=pl_sd['global_step']
          print(F'''loaded model from global step {global_step}.'''       )
     else:
          __lowercase							={'state_dict': None}
          __lowercase							=None
     __lowercase							=load_model_from_config(config.model,    pl_sd['state_dict'],    gpu=UpperCamelCase_,    eval_mode=UpperCamelCase_       )['model']
     return model, global_step




"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


if __name__ == "__main__":
							from doctest import testmod
							from timeit import timeit

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)




