



"""simple docstring"""







import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case    ,			snake_case   :			Tuple						    = image.size
						snake_case    ,			snake_case   :			Any						    = (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						snake_case   :			Union[str, Any]						    = image.resize((w, h)     ,		resample=PIL_INTERPOLATION["lanczos"] )
						snake_case   :			List[str]						    = np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_5_5.0
						snake_case   :			List[Any]						    = image[None].transpose(0     ,		3     ,		1     ,		2 )
						snake_case   :			Union[str, Any]						    = torch.from_numpy(lowerCamelCase_ )
						return 2.0 * image - 1.0

class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				int   ,	_lowercase							:				VQModel   ,	_lowercase							:				UNetaDModel   ,	_lowercase							:				Union[
			        DDIMScheduler,
			        PNDMScheduler,
			        LMSDiscreteScheduler,
			        EulerDiscreteScheduler,
			        EulerAncestralDiscreteScheduler,
			        DPMSolverMultistepScheduler,
			    ]   ,	)							->					Dict:
									super().__init__()
									self.register_modules(vqvae=_lowercase   ,	unet=_lowercase   ,	scheduler=_lowercase		)



			@torch.no_grad()
			def __call__(			self							:				Dict   ,	_lowercase							:				Union[torch.Tensor, PIL.Image.Image] = None   ,	_lowercase							:				Optional[int] = 1   ,	_lowercase							:				Optional[int] = 100   ,	_lowercase							:				Optional[float] = 0.0   ,	_lowercase							:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,	_lowercase							:				Optional[str] = "pil"   ,	_lowercase							:				bool = True   ,	)							->					Union[Tuple, ImagePipelineOutput]:
									if isinstance(_lowercase   ,	PIL.Image.Image		):
															snake_case   :			Tuple						    = 1
									elif isinstance(_lowercase   ,	torch.Tensor		):
															snake_case   :			Any						    = image.shape[0]
									else:
															raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowercase		)}'''		)

									if isinstance(_lowercase   ,	PIL.Image.Image		):
															snake_case   :			List[str]						    = preprocess(_lowercase		)

									snake_case    ,			snake_case   :			str						    = image.shape[-2:]

									# in_channels should be 6: 3 for latents, 3 for low resolution image
									snake_case   :			str						    = (batch_size, self.unet.config.in_channels // 2, height, width)
									snake_case   :			Dict						    = next(self.unet.parameters()		).dtype

									snake_case   :			List[str]						    = randn_tensor(_lowercase   ,	generator=_lowercase   ,	device=self.device   ,	dtype=_lowercase		)

									snake_case   :			int						    = image.to(device=self.device   ,	dtype=_lowercase		)

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(_lowercase   ,	device=self.device		)
									snake_case   :			Tuple						    = self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									snake_case   :			Optional[Any]						    = latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									snake_case   :			Any						    = "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
									snake_case   :			Dict						    = {}
									if accepts_eta:
															snake_case   :			Optional[Any]						    = eta

									for t in self.progress_bar(_lowercase		):
															# concat latents and low resolution image in the channel dimension.
															snake_case   :			Optional[int]						    = torch.cat([latents, image]   ,	dim=1		)
															snake_case   :			Union[str, Any]						    = self.scheduler.scale_model_input(_lowercase   ,	_lowercase		)
															# predict the noise residual
															snake_case   :			Tuple						    = self.unet(_lowercase   ,	_lowercase		).sample
															# compute the previous noisy sample x_t -> x_t-1
															snake_case   :			Optional[int]						    = self.scheduler.step(_lowercase   ,	_lowercase   ,	_lowercase   ,	**_lowercase		).prev_sample

									# decode the image latents with the VQVAE
									snake_case   :			List[Any]						    = self.vqvae.decode(_lowercase		).sample
									snake_case   :			Dict						    = torch.clamp(_lowercase   ,	-1.0   ,	1.0		)
									snake_case   :			Union[str, Any]						    = image / 2 + 0.5
									snake_case   :			List[Any]						    = image.cpu().permute(0   ,	2   ,	3   ,	1		).numpy()

									if output_type == "pil":
															snake_case   :			Optional[int]						    = self.numpy_to_pil(_lowercase		)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_lowercase		)








"""simple docstring"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str		)							->					None:
									snake_case   :			Optional[int]						    = self._trie
									for char in text:
															if char not in trie:
																					snake_case   :			str						    = {}
															snake_case   :			Optional[Any]						    = trie[char]
									snake_case   :			str						    = True



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



