"""simple docstring"""






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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowerCAmelCase__						:						List[str]		  =			logging.get_logger(__name__)  # pylint: disable=invalid-name
class        snake_case (				__lowercase       ):




					"""simple docstring"""

					def __init__(       self     :			List[Any]					,lowerCamelCase__     :			AutoencoderKL					,lowerCamelCase__     :			CLIPTextModel					,lowerCamelCase__     :			CLIPTokenizer					,lowerCamelCase__     :			UNetaDConditionModel					,lowerCamelCase__     :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,lowerCamelCase__     :			StableDiffusionSafetyChecker					,lowerCamelCase__     :			CLIPImageProcessor					,):
									super().__init__()
									self.register_modules(
									    vae=__a					,text_encoder=__a					,tokenizer=__a					,unet=__a					,scheduler=__a					,safety_checker=__a					,feature_extractor=__a					,)







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Optional[Union[str, int]] = "auto"    ):
									if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													UpperCAmelCase__    =							self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(__a    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									self.enable_attention_slicing(__a    )







					@torch.no_grad()
					def __call__(       self     :			List[str]					,lowerCamelCase__     :			Union[str, List[str]]					,lowerCamelCase__     :			int = 512					,lowerCamelCase__     :			int = 512					,lowerCamelCase__     :			int = 50					,lowerCamelCase__     :			float = 7.5					,lowerCamelCase__     :			Optional[Union[str, List[str]]] = None					,lowerCamelCase__     :			Optional[int] = 1					,lowerCamelCase__     :			float = 0.0					,lowerCamelCase__     :			Optional[torch.Generator] = None					,lowerCamelCase__     :			Optional[torch.FloatTensor] = None					,lowerCamelCase__     :			Optional[str] = "pil"					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,lowerCamelCase__     :			int = 1					,lowerCamelCase__     :			Optional[torch.FloatTensor] = None					,**lowerCamelCase__     :			Dict					,):

									if isinstance(__a					,__a    ):
													UpperCAmelCase__    =							1
									elif isinstance(__a					,__a    ):
													UpperCAmelCase__    =							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
									UpperCAmelCase__    =							self.tokenizer(
									    __a					,padding='max_length'					,max_length=self.tokenizer.model_max_length					,return_tensors='pt'					,)
									UpperCAmelCase__    =							text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													UpperCAmelCase__    =							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}'''    )
													UpperCAmelCase__    =							text_input_ids[:, : self.tokenizer.model_max_length]

									if text_embeddings is None:
													UpperCAmelCase__    =							self.text_encoder(text_input_ids.to(self.device    )    )[0]

									# duplicate text embeddings for each generation per prompt, using mps friendly method
									UpperCAmelCase__    =							text_embeddings.shape
									UpperCAmelCase__    =							text_embeddings.repeat(1					,__a					,1    )
									UpperCAmelCase__    =							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.
									UpperCAmelCase__    =							guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
													UpperCAmelCase__    =							42
													if negative_prompt is None:
																	UpperCAmelCase__    =							[""""""]
													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    ):
																	UpperCAmelCase__    =							[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:
																	UpperCAmelCase__    =							negative_prompt

													UpperCAmelCase__    =							text_input_ids.shape[-1]
													UpperCAmelCase__    =							self.tokenizer(
													    __a					,padding='max_length'					,max_length=__a					,truncation=__a					,return_tensors='pt'					,)
													UpperCAmelCase__    =							self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													UpperCAmelCase__    =							uncond_embeddings.shape[1]
													UpperCAmelCase__    =							uncond_embeddings.repeat(__a					,__a					,1    )
													UpperCAmelCase__    =							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
													UpperCAmelCase__    =							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`.
									UpperCAmelCase__    =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									UpperCAmelCase__    =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
									UpperCAmelCase__    =							text_embeddings.dtype
									if latents is None:
													if self.device.type == "mps":
																	# randn does not exist on mps
																	UpperCAmelCase__    =							torch.randn(
																	    __a					,generator=__a					,device='cpu'					,dtype=__a    ).to(self.device    )
																	UpperCAmelCase__    =							torch.randn(__a					,generator=__a					,device='cpu'					,dtype=__a    ).to(
																	    self.device    )
													else:
																	UpperCAmelCase__    =							torch.randn(
																	    __a					,generator=__a					,device=self.device					,dtype=__a    )
																	UpperCAmelCase__    =							torch.randn(__a					,generator=__a					,device=self.device					,dtype=__a    )
									else:
													if latents_reference.shape != latents_shape:
																	raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
													UpperCAmelCase__    =							latents_reference.to(self.device    )
													UpperCAmelCase__    =							latents.to(self.device    )

									# This is the key part of the pipeline where we
									# try to ensure that the generated images w/ the same seed
									# but different sizes actually result in similar images
									UpperCAmelCase__    =							(latents_shape[3] - latents_shape_reference[3]) // 2
									UpperCAmelCase__    =							(latents_shape[2] - latents_shape_reference[2]) // 2
									UpperCAmelCase__    =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
									UpperCAmelCase__    =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
									UpperCAmelCase__    =							0 if dx < 0 else dx
									UpperCAmelCase__    =							0 if dy < 0 else dy
									UpperCAmelCase__    =							max(-dx					,0    )
									UpperCAmelCase__    =							max(-dy					,0    )
									# import pdb
									# pdb.set_trace()
									UpperCAmelCase__    =							latents_reference[:, :, dy : dy + h, dx : dx + w]

									# 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
									UpperCAmelCase__    =							self.scheduler.timesteps.to(self.device    )

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

									for i, t in enumerate(self.progress_bar(__a    )    ):
													# expand the latents if we are doing classifier free guidance
													UpperCAmelCase__    =							torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
													UpperCAmelCase__    =							self.scheduler.scale_model_input(__a					,__a    )

													# predict the noise residual
													UpperCAmelCase__    =							self.unet(__a					,__a					,encoder_hidden_states=__a    ).sample

													# perform guidance
													if do_classifier_free_guidance:
																	UpperCAmelCase__    =							noise_pred.chunk(2    )
																	UpperCAmelCase__    =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													UpperCAmelCase__    =							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    )

									UpperCAmelCase__    =							1 / 0.1_8_2_1_5 * latents
									UpperCAmelCase__    =							self.vae.decode(__a    ).sample

									UpperCAmelCase__    =							(image / 2 + 0.5).clamp(0					,1    )

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

									if self.safety_checker is not None:
													UpperCAmelCase__    =							self.feature_extractor(self.numpy_to_pil(__a    )					,return_tensors='pt'    ).to(
													    self.device    )
													UpperCAmelCase__    =							self.safety_checker(
													    images=__a					,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
									else:
													UpperCAmelCase__    =							None

									if output_type == "pil":
													UpperCAmelCase__    =							self.numpy_to_pil(__a    )

									if not return_dict:
													return (image, has_nsfw_concept)

									return StableDiffusionPipelineOutput(images=__a					,nsfw_content_detected=__a    )




"""simple docstring"""






from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 						a_		(      lowerCamelCase ):
				return "".join(sorted(lowerCamelCase ) )



def 						a_		(      lowerCamelCase ):
				return word_by_signature[signature(lowerCamelCase )]


lowerCAmelCase__						:						str		  =			Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
lowerCAmelCase__						:						str		  =			sorted({word.strip().lower() for word in data.splitlines()})

lowerCAmelCase__						:						Optional[Any]		  =			collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



