
'''simple docstring'''






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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__lowerCamelCase			      =      logging.get_logger(__name__)  # pylint: disable=invalid-name




class     __snake_case		(   a__):
					def __init__(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									super().__init__()

									if safety_checker is None:
													logger.warning(
													    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
													    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
													    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
													    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
													    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
													    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'      )

									self.register_modules(
									    speech_model=A,    speech_processor=A,    vae=A,    text_encoder=A,    tokenizer=A,    unet=A,    scheduler=A,    feature_extractor=A,    )
					def     UpperCAmelCase_							(     self,    A = "auto"      ):

									"""simple docstring"""


									if slice_size == "auto":
													lowerCamelCase					:			Optional[int]									= self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.enable_attention_slicing(A      )




					@torch.no_grad()
					def __call__(     self,    A,    A=1_6000,    A = 512,    A = 512,    A = 50,    A = 7.5,    A = None,    A = 1,    A = 0.0,    A = None,    A = None,    A = "pil",    A = True,    A = None,    A = 1,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.speech_processor.feature_extractor(
									    A,    return_tensors='pt',    sampling_rate=A      ).input_features.to(self.device      )
									lowerCamelCase					:			str									= self.speech_model.generate(A,    max_length=48_0000      )

									lowerCamelCase					:			Union[str, Any]									= self.speech_processor.tokenizer.batch_decode(A,    skip_special_tokens=A,    normalize=A      )[
									    0
									]

									if isinstance(A,    A      ):
													lowerCamelCase					:			Any									= 1
									elif isinstance(A,    A      ):
													lowerCamelCase					:			Any									= len(A      )
									else:
													raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A      )}'''      )

									if height % 8 != 0 or width % 8 != 0:
													raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''      )

									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(A,    A      ) or callback_steps <= 0)
									):
													raise ValueError(
													    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
													    F''' {type(A      )}.'''      )

									# get prompt text embeddings
									lowerCamelCase					:			Optional[Any]									= self.tokenizer(
									    A,    padding='max_length',    max_length=self.tokenizer.model_max_length,    return_tensors='pt',    )
									lowerCamelCase					:			Dict									= text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													lowerCamelCase					:			Dict									= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
													logger.warning(
													    'The following part of your input was truncated because CLIP can only handle sequences up to'
													    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''      )
													lowerCamelCase					:			Optional[int]									= text_input_ids[:, : self.tokenizer.model_max_length]
									lowerCamelCase					:			Optional[int]									= self.text_encoder(text_input_ids.to(self.device      )      )[0]

									# duplicate text embeddings for each generation per prompt, using mps friendly method
									lowerCamelCase					:			Union[str, Any]									= text_embeddings.shape
									lowerCamelCase					:			str									= text_embeddings.repeat(1,    A,    1      )
									lowerCamelCase					:			str									= text_embeddings.view(bs_embed * num_images_per_prompt,    A,    -1      )

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									lowerCamelCase					:			Optional[int]									= guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
													lowerCamelCase					:			List[str]
													if negative_prompt is None:
																	lowerCamelCase					:			int									= [''] * batch_size
													elif type(A      ) is not type(A      ):
																	raise TypeError(
																	    F'''`negative_prompt` should be the same type to `prompt`, but got {type(A      )} !='''
																	    F''' {type(A      )}.'''      )
													elif isinstance(A,    A      ):
																	lowerCamelCase					:			Any									= [negative_prompt]
													elif batch_size != len(A      ):
																	raise ValueError(
																	    F'''`negative_prompt`: {negative_prompt} has batch size {len(A      )}, but `prompt`:'''
																	    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																	    ' the batch size of `prompt`.'      )
													else:
																	lowerCamelCase					:			Dict									= negative_prompt

													lowerCamelCase					:			Optional[Any]									= text_input_ids.shape[-1]
													lowerCamelCase					:			Optional[int]									= self.tokenizer(
													    A,    padding='max_length',    max_length=A,    truncation=A,    return_tensors='pt',    )
													lowerCamelCase					:			Any									= self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													lowerCamelCase					:			List[Any]									= uncond_embeddings.shape[1]
													lowerCamelCase					:			int									= uncond_embeddings.repeat(1,    A,    1      )
													lowerCamelCase					:			Dict									= uncond_embeddings.view(batch_size * num_images_per_prompt,    A,    -1      )

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													lowerCamelCase					:			Tuple									= torch.cat([uncond_embeddings, text_embeddings]      )

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									lowerCamelCase					:			int									= (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									lowerCamelCase					:			List[str]									= text_embeddings.dtype
									if latents is None:
													if self.device.type == "mps":
																	# randn does not exist on mps
																	lowerCamelCase					:			List[Any]									= torch.randn(A,    generator=A,    device='cpu',    dtype=A      ).to(
																	    self.device      )
													else:
																	lowerCamelCase					:			Optional[Any]									= torch.randn(A,    generator=A,    device=self.device,    dtype=A      )
									else:
													if latents.shape != latents_shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''      )
													lowerCamelCase					:			str									= latents.to(self.device      )

									# set timesteps
									self.scheduler.set_timesteps(A      )

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									lowerCamelCase					:			Tuple									= self.scheduler.timesteps.to(self.device      )

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCamelCase					:			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]
									lowerCamelCase					:			List[str]									= 'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
									lowerCamelCase					:			str									= {}
									if accepts_eta:
													lowerCamelCase					:			List[str]									= eta

									for i, t in enumerate(self.progress_bar(A      )      ):
													# expand the latents if we are doing classifier free guidance
													lowerCamelCase					:			Union[str, Any]									= torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
													lowerCamelCase					:			Optional[int]									= self.scheduler.scale_model_input(A,    A      )

													# predict the noise residual
													lowerCamelCase					:			List[Any]									= self.unet(A,    A,    encoder_hidden_states=A      ).sample

													# perform guidance
													if do_classifier_free_guidance:
																	lowerCamelCase					:			Optional[int]									= noise_pred.chunk(2      )
																	lowerCamelCase					:			Optional[int]									= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase					:			Optional[int]									= self.scheduler.step(A,    A,    A,    **A      ).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																	callback(A,    A,    A      )

									lowerCamelCase					:			Optional[int]									= 1 / 0.1_8215 * latents
									lowerCamelCase					:			List[Any]									= self.vae.decode(A      ).sample

									lowerCamelCase					:			List[str]									= (image / 2 + 0.5).clamp(0,    1      )

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									lowerCamelCase					:			List[Any]									= image.cpu().permute(0,    2,    3,    1      ).float().numpy()

									if output_type == "pil":
													lowerCamelCase					:			Dict									= self.numpy_to_pil(A      )

									if not return_dict:
													return image

									return StableDiffusionPipelineOutput(images=A,    nsfw_content_detected=A      )


'''simple docstring'''






from __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





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





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
