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


__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			logging.get_logger(__name__)  # pylint: disable=invalid-name



class        lowercase_   (					__snake_case		):



							def __init__(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
													super().__init__()
													self.register_modules(
													    vae=lowercase_				,    text_encoder=lowercase_				,    tokenizer=lowercase_				,    unet=lowercase_				,    scheduler=lowercase_				,    safety_checker=lowercase_				,    feature_extractor=lowercase_				,    )



							def 	UpperCamelCase	(						self				,    lowercase_ = "auto"    ):
													if slice_size == "auto":
																			# half the attention head size is usually a good trade-off between
																			# speed and memory
																			_snake_case						:					str    =   self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self.enable_attention_slicing(lowercase_    )



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

													if isinstance(lowercase_				,    lowercase_    ):
																			_snake_case						:					str    =   1
													elif isinstance(lowercase_				,    lowercase_    ):
																			_snake_case						:					str    =   len(lowercase_    )
													else:
																			raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_    )}"""    )

													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(lowercase_				,    lowercase_    ) or callback_steps <= 0)
													):
																			raise ValueError(
																			    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																			    f""" {type(lowercase_    )}."""    )

													# get prompt text embeddings
													_snake_case						:					Tuple    =   self.tokenizer(
													    lowercase_				,    padding="max_length"				,    max_length=self.tokenizer.model_max_length				,    return_tensors="pt"				,    )
													_snake_case						:					Union[str, Any]    =   text_inputs.input_ids

													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			_snake_case						:					Optional[Any]    =   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}"""    )
																			_snake_case						:					List[str]    =   text_input_ids[:, : self.tokenizer.model_max_length]

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

													# duplicate text embeddings for each generation per prompt, using mps friendly method
													_snake_case    ,_snake_case    ,_snake_case						:					List[str]    =   text_embeddings.shape
													_snake_case						:					List[Any]    =   text_embeddings.repeat(1				,    lowercase_				,    1    )
													_snake_case						:					Tuple    =   text_embeddings.view(bs_embed * num_images_per_prompt				,    lowercase_				,    -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.
													_snake_case						:					str    =   guidance_scale > 1.0
													# get unconditional embeddings for classifier free guidance
													if do_classifier_free_guidance:
																			_snake_case						:					List[str]
																			if negative_prompt is None:
																									_snake_case						:					Tuple    =   [""]
																			elif type(lowercase_    ) is not type(lowercase_    ):
																									raise TypeError(
																									    f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_    )} !="""
																									    f""" {type(lowercase_    )}."""    )
																			elif isinstance(lowercase_				,    lowercase_    ):
																									_snake_case						:					Any    =   [negative_prompt]
																			elif batch_size != len(lowercase_    ):
																									raise ValueError(
																									    f"""`negative_prompt`: {negative_prompt} has batch size {len(lowercase_    )}, but `prompt`:"""
																									    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																									    " the batch size of `prompt`."    )
																			else:
																									_snake_case						:					Tuple    =   negative_prompt

																			_snake_case						:					Any    =   text_input_ids.shape[-1]
																			_snake_case						:					Tuple    =   self.tokenizer(
																			    lowercase_				,    padding="max_length"				,    max_length=lowercase_				,    truncation=lowercase_				,    return_tensors="pt"				,    )
																			_snake_case						:					Optional[Any]    =   self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																			_snake_case						:					Optional[int]    =   uncond_embeddings.shape[1]
																			_snake_case						:					Optional[Any]    =   uncond_embeddings.repeat(lowercase_				,    lowercase_				,    1    )
																			_snake_case						:					str    =   uncond_embeddings.view(batch_size * num_images_per_prompt				,    lowercase_				,    -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
																			_snake_case						:					Any    =   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`.
													_snake_case						:					Tuple    =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
													_snake_case						:					str    =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
													_snake_case						:					Optional[int]    =   text_embeddings.dtype
													if latents is None:
																			if self.device.type == "mps":
																									# randn does not exist on mps
																									_snake_case						:					Optional[Any]    =   torch.randn(
																									    lowercase_				,    generator=lowercase_				,    device="cpu"				,    dtype=lowercase_    ).to(self.device    )
																									_snake_case						:					Union[str, Any]    =   torch.randn(lowercase_				,    generator=lowercase_				,    device="cpu"				,    dtype=lowercase_    ).to(
																									    self.device    )
																			else:
																									_snake_case						:					Dict    =   torch.randn(
																									    lowercase_				,    generator=lowercase_				,    device=self.device				,    dtype=lowercase_    )
																									_snake_case						:					List[str]    =   torch.randn(lowercase_				,    generator=lowercase_				,    device=self.device				,    dtype=lowercase_    )
													else:
																			if latents_reference.shape != latents_shape:
																									raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
																			_snake_case						:					Optional[Any]    =   latents_reference.to(self.device    )
																			_snake_case						:					Tuple    =   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
													_snake_case						:					Union[str, Any]    =   (latents_shape[3] - latents_shape_reference[3]) // 2
													_snake_case						:					List[Any]    =   (latents_shape[2] - latents_shape_reference[2]) // 2
													_snake_case						:					Union[str, Any]    =   latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
													_snake_case						:					Dict    =   latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
													_snake_case						:					int    =   0 if dx < 0 else dx
													_snake_case						:					int    =   0 if dy < 0 else dy
													_snake_case						:					Dict    =   max(-dx				,    0    )
													_snake_case						:					List[str]    =   max(-dy				,    0    )
													# import pdb
													# pdb.set_trace()
													_snake_case						:					Optional[int]    =   latents_reference[:, :, dy : dy + h, dx : dx + w]

													# set timesteps
													self.scheduler.set_timesteps(lowercase_    )

													# Some schedulers like PNDM have timesteps as arrays
													# It's more optimized to move all timesteps to correct device beforehand
													_snake_case						:					List[str]    =   self.scheduler.timesteps.to(self.device    )

													# 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						:					str    =   "eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
													_snake_case						:					Optional[int]    =   {}
													if accepts_eta:
																			_snake_case						:					int    =   eta

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

																			# predict the noise residual
																			_snake_case						:					str    =   self.unet(lowercase_				,    lowercase_				,    encoder_hidden_states=lowercase_    ).sample

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

																			# compute the previous noisy sample x_t -> x_t-1
																			_snake_case						:					Any    =   self.scheduler.step(lowercase_				,    lowercase_				,    lowercase_				,    **lowercase_    ).prev_sample

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

													_snake_case						:					List[str]    =   1 / 0.18_215 * latents
													_snake_case						:					int    =   self.vae.decode(lowercase_    ).sample

													_snake_case						:					Optional[int]    =   (image / 2 + 0.5).clamp(0				,    1    )

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

													if self.safety_checker is not None:
																			_snake_case						:					int    =   self.feature_extractor(self.numpy_to_pil(lowercase_    )				,    return_tensors="pt"    ).to(
																			    self.device    )
																			_snake_case    ,_snake_case						:					Dict    =   self.safety_checker(
																			    images=lowercase_				,    clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
													else:
																			_snake_case						:					Any    =   None

													if output_type == "pil":
																			_snake_case						:					Optional[int]    =   self.numpy_to_pil(lowercase_    )

													if not return_dict:
																			return (image, has_nsfw_concept)

													return StableDiffusionPipelineOutput(images=lowercase_				,    nsfw_content_detected=lowercase_    )
from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__SCREAMING_SNAKE_CASE						:   str         =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE						:   list[int]         =			[ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE						:   set[int]         =			{ord(char) for char in VALID_CHARS}

__SCREAMING_SNAKE_CASE						:   list[str]         =			["the", "be", "to", "of", "and", "in", "that", "have"]
def  snake_case						(__lowercase ,      __lowercase						)	->	str | None:

						'''simple docstring'''



						_snake_case						:					str    =   ""
						_snake_case						:					int
						_snake_case						:					int
						_snake_case						:					int

						for keychar, cipherchar in zip(cycle(__lowercase						) ,      __lowercase						):
												_snake_case						:					str    =   cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__lowercase						)

						return decoded
def  snake_case						(__lowercase						)	->	list[str]:

						'''simple docstring'''



						_snake_case						:					list[str]    =   []
						for key in product(__lowercase ,      repeat=3						):
												_snake_case						:					Union[str, Any]    =   try_key(__lowercase ,      __lowercase						)
												if encoded is not None:
																		possibles.append(__lowercase						)
						return possibles
def  snake_case						(__lowercase ,      __lowercase						)	->	list[str]:

						'''simple docstring'''



						return [possible for possible in possibles if common_word in possible.lower()]
def  snake_case						(__lowercase = "p059_cipher.txt"						)	->	int:

						'''simple docstring'''



						_snake_case						:					list[int]
						_snake_case						:					list[str]
						_snake_case						:					str
						_snake_case						:					str
						_snake_case						:					str    =   Path(__lowercase						).parent.joinpath(__lowercase						).read_text(encoding="utf-8"						)

						_snake_case						:					Dict    =   [int(__lowercase						) for number in data.strip().split(","						)]

						_snake_case						:					Tuple    =   filter_valid_chars(__lowercase						)
						for common_word in COMMON_WORDS:
												_snake_case						:					Optional[int]    =   filter_common_word(__lowercase ,      __lowercase						)
												if len(__lowercase						) == 1:
																		break

						_snake_case						:					int    =   possibles[0]
						return sum(ord(__lowercase						) for char in decoded_text						)


if __name__ == "__main__":
				print(F'''{solution() = }''')