




'''simple docstring'''



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


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





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Tuple												=      ['safety_checker', 'feature_extractor']







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = True      ,					)    ->					Optional[int]:
         super().__init__()
         if hasattr(scheduler.config      ,					'steps_offset'     ) and scheduler.config.steps_offset != 1:
              __lowerCamelCase       :     int        =		(
                  f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
                  f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
                  'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
                  ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
                  ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
                  ' file'
              )
              deprecate('steps_offset!=1'      ,					'1.0.0'      ,					SCREAMING_SNAKE_CASE_      ,					standard_warn=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     Dict        =		dict(scheduler.config     )
              __lowerCamelCase       :     Optional[int]        =		1
              __lowerCamelCase       :     Tuple        =		FrozenDict(SCREAMING_SNAKE_CASE_     )

         self.register_modules(
             vae=SCREAMING_SNAKE_CASE_      ,					text_encoder=SCREAMING_SNAKE_CASE_      ,					tokenizer=SCREAMING_SNAKE_CASE_      ,					unet=SCREAMING_SNAKE_CASE_      ,					scheduler=SCREAMING_SNAKE_CASE_      ,					safety_checker=SCREAMING_SNAKE_CASE_      ,					feature_extractor=SCREAMING_SNAKE_CASE_      ,					)
         self.register_to_config(requires_safety_checker=SCREAMING_SNAKE_CASE_     )







    @property
    def 				lowercase_    (							self     )    ->					Dict[str, Any]:
         return {k: getattr(self      ,					SCREAMING_SNAKE_CASE_     ) for k in self.config.keys() if not k.startswith('_'     )}







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







    def 				lowercase_    (							self     )    ->					List[str]:
         self.enable_attention_slicing(SCREAMING_SNAKE_CASE_     )







    @torch.no_grad()
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = 0.8      ,					SCREAMING_SNAKE_CASE_ = 50      ,					SCREAMING_SNAKE_CASE_ = 7.5      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 1      ,					SCREAMING_SNAKE_CASE_ = 0.0      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "pil"      ,					SCREAMING_SNAKE_CASE_ = True      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 1      ,					)    ->					List[Any]:
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionInpaintPipelineLegacy(**self.components     )(
             prompt=SCREAMING_SNAKE_CASE_      ,					image=SCREAMING_SNAKE_CASE_      ,					mask_image=SCREAMING_SNAKE_CASE_      ,					strength=SCREAMING_SNAKE_CASE_      ,					num_inference_steps=SCREAMING_SNAKE_CASE_      ,					guidance_scale=SCREAMING_SNAKE_CASE_      ,					negative_prompt=SCREAMING_SNAKE_CASE_      ,					num_images_per_prompt=SCREAMING_SNAKE_CASE_      ,					eta=SCREAMING_SNAKE_CASE_      ,					generator=SCREAMING_SNAKE_CASE_      ,					output_type=SCREAMING_SNAKE_CASE_      ,					return_dict=SCREAMING_SNAKE_CASE_      ,					callback=SCREAMING_SNAKE_CASE_      ,					)







    @torch.no_grad()
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = 0.8      ,					SCREAMING_SNAKE_CASE_ = 50      ,					SCREAMING_SNAKE_CASE_ = 7.5      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 1      ,					SCREAMING_SNAKE_CASE_ = 0.0      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "pil"      ,					SCREAMING_SNAKE_CASE_ = True      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 1      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Union[str, Any]:
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionImgaImgPipeline(**self.components     )(
             prompt=SCREAMING_SNAKE_CASE_      ,					image=SCREAMING_SNAKE_CASE_      ,					strength=SCREAMING_SNAKE_CASE_      ,					num_inference_steps=SCREAMING_SNAKE_CASE_      ,					guidance_scale=SCREAMING_SNAKE_CASE_      ,					negative_prompt=SCREAMING_SNAKE_CASE_      ,					num_images_per_prompt=SCREAMING_SNAKE_CASE_      ,					eta=SCREAMING_SNAKE_CASE_      ,					generator=SCREAMING_SNAKE_CASE_      ,					output_type=SCREAMING_SNAKE_CASE_      ,					return_dict=SCREAMING_SNAKE_CASE_      ,					callback=SCREAMING_SNAKE_CASE_      ,					callback_steps=SCREAMING_SNAKE_CASE_      ,					)







    @torch.no_grad()
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = 5_12      ,					SCREAMING_SNAKE_CASE_ = 5_12      ,					SCREAMING_SNAKE_CASE_ = 50      ,					SCREAMING_SNAKE_CASE_ = 7.5      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 1      ,					SCREAMING_SNAKE_CASE_ = 0.0      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "pil"      ,					SCREAMING_SNAKE_CASE_ = True      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = 1      ,					)    ->					List[str]:
         # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
         return StableDiffusionPipeline(**self.components     )(
             prompt=SCREAMING_SNAKE_CASE_      ,					height=SCREAMING_SNAKE_CASE_      ,					width=SCREAMING_SNAKE_CASE_      ,					num_inference_steps=SCREAMING_SNAKE_CASE_      ,					guidance_scale=SCREAMING_SNAKE_CASE_      ,					negative_prompt=SCREAMING_SNAKE_CASE_      ,					num_images_per_prompt=SCREAMING_SNAKE_CASE_      ,					eta=SCREAMING_SNAKE_CASE_      ,					generator=SCREAMING_SNAKE_CASE_      ,					latents=SCREAMING_SNAKE_CASE_      ,					output_type=SCREAMING_SNAKE_CASE_      ,					return_dict=SCREAMING_SNAKE_CASE_      ,					callback=SCREAMING_SNAKE_CASE_      ,					callback_steps=SCREAMING_SNAKE_CASE_      ,					)










'''simple docstring'''



from typing import Literal

A__		: Tuple												=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    Literal["encrypt", "decrypt"]							)					->		str:
     __lowerCamelCase       :     Dict        =		LETTERS if mode == 'decrypt' else key
     __lowerCamelCase       :     Tuple        =		key if mode == 'decrypt' else LETTERS
     __lowerCamelCase       :     Optional[int]        =		''
     # loop through each symbol in the message
     for symbol in message:
          if symbol.upper() in chars_a:
               # encrypt/decrypt the symbol
               __lowerCamelCase       :     Optional[int]        =		chars_a.find(symbol.upper()							)
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               # symbol is not in LETTERS, just add it
               translated += symbol
     return translated



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    str							)					->		str:
     return translate_message(UpperCAmelCase_				,   UpperCAmelCase_				,   'encrypt'							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    str							)					->		str:
     return translate_message(UpperCAmelCase_				,   UpperCAmelCase_				,   'decrypt'							)



def       UpperCAmelCase__ (     )					->		None:
     __lowerCamelCase       :     Dict        =		'Hello World'
     __lowerCamelCase       :     str        =		'QWERTYUIOPASDFGHJKLZXCVBNM'
     __lowerCamelCase       :     Any        =		'decrypt'  # set to 'encrypt' or 'decrypt'

     if mode == "encrypt":
          __lowerCamelCase       :     Optional[int]        =		encrypt_message(UpperCAmelCase_				,   UpperCAmelCase_							)
     elif mode == "decrypt":
          __lowerCamelCase       :     List[str]        =		decrypt_message(UpperCAmelCase_				,   UpperCAmelCase_							)
     print(F'Using the key {key}, the {mode}ed message is: {translated}'							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()






