




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


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







class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  )		->							Tuple:
           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       a__			(						self	,  _a = "auto"       )		->							List[Any]:
           if slice_size == "auto":
                 _A							:  Any    =		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(_a       )

     def       a__			(						self       )		->							Tuple:
           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	,  )		->							int:
           _A							:  Dict    =		self.speech_processor.feature_extractor(
               _a	,  return_tensors="""pt"""	,  sampling_rate=_a       ).input_features.to(self.device       )
           _A							:  Optional[int]    =		self.speech_model.generate(_a	,  max_length=48_0000       )

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

           if isinstance(_a	,  _a       ):
                 _A							:  Any    =		1
           elif isinstance(_a	,  _a       ):
                 _A							:  Optional[int]    =		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
           _A							:  List[Any]    =		self.tokenizer(
               _a	,  padding="""max_length"""	,  max_length=self.tokenizer.model_max_length	,  return_tensors="""pt"""	,  )
           _A							:  Union[str, Any]    =		text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 _A							:  List[str]    =		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}'''       )
                 _A							:  str    =		text_input_ids[:, : self.tokenizer.model_max_length]
           _A							:  Optional[int]    =		self.text_encoder(text_input_ids.to(self.device       )       )[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           _A    ,     _A    ,     _A							:  List[Any]    =		text_embeddings.shape
           _A							:  Tuple    =		text_embeddings.repeat(1	,  _a	,  1       )
           _A							:  Union[str, Any]    =		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.
           _A							:  Any    =		guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                 _A							:  List[str]
                 if negative_prompt is None:
                       _A							:  List[Any]    =		[""""""] * 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       ):
                       _A							:  str    =		[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:
                       _A							:  Optional[int]    =		negative_prompt

                 _A							:  Tuple    =		text_input_ids.shape[-1]
                 _A							:  Dict    =		self.tokenizer(
                     _a	,  padding="""max_length"""	,  max_length=_a	,  truncation=_a	,  return_tensors="""pt"""	,  )
                 _A							:  int    =		self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _A							:  Optional[Any]    =		uncond_embeddings.shape[1]
                 _A							:  Dict    =		uncond_embeddings.repeat(1	,  _a	,  1       )
                 _A							:  str    =		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
                 _A							:  Union[str, 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`.
           _A							:  Union[str, Any]    =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           _A							:  Optional[Any]    =		text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not exist on mps
                       _A							:  List[str]    =		torch.randn(_a	,  generator=_a	,  device="""cpu"""	,  dtype=_a       ).to(
                           self.device       )
                 else:
                       _A							:  Dict    =		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}'''       )
                 _A							:  Any    =		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
           _A							:  int    =		self.scheduler.timesteps.to(self.device       )

           # scale the initial noise by the standard deviation required by the scheduler
           _A							:  Union[str, 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]
           _A							:  List[str]    =		"""eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
           _A							:  Optional[int]    =		{}
           if accepts_eta:
                 _A							:  Dict    =		eta

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

                 # predict the noise residual
                 _A							:  Optional[Any]    =		self.unet(_a	,  _a	,  encoder_hidden_states=_a       ).sample

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

                 # compute the previous noisy sample x_t -> x_t-1
                 _A							:  str    =		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       )

           _A							:  List[str]    =		1 / 0.18215 * latents
           _A							:  List[str]    =		self.vae.decode(_a       ).sample

           _A							:  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
           _A							:  int    =		image.cpu().permute(0	,  2	,  3	,  1       ).float().numpy()

           if output_type == "pil":
                 _A							:  Dict    =		self.numpy_to_pil(_a       )

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=_a	,  nsfw_content_detected=_a       )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Union[str, Any]    =		""""""
      for i in table:
            res += inp[i - 1]
      return res







def        lowerCAmelCase_			(  snake_case_  ):
      return data[1:] + data[0]







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Dict    =		""""""
      for i in range(len(snake_case_  )  ):
            if a[i] == b[i]:
                  res += "0"
            else:
                  res += "1"
      return res







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  int    =		int("""0b""" + data[0] + data[-1],2  )
      _A							:  Any    =		int("""0b""" + data[1:3],2  )
      return bin(s[row][col]  )[2:]







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_  ):
      _A							:  List[str]    =		message[:4]
      _A							:  List[Any]    =		message[4:]
      _A							:  Union[str, Any]    =		apply_table(snake_case_,snake_case_  )
      _A							:  List[Any]    =		xor(snake_case_,snake_case_  )
      _A							:  Optional[Any]    =		apply_sbox(snake_case_,temp[:4]  )  # noqa: E741
      _A							:  List[Any]    =		apply_sbox(snake_case_,temp[4:]  )
      _A							:  int    =		"""0""" * (2 - len(snake_case_  )) + l  # noqa: E741
      _A							:  Union[str, Any]    =		"""0""" * (2 - len(snake_case_  )) + r
      _A							:  List[Any]    =		apply_table(l + r,snake_case_  )
      _A							:  Any    =		xor(snake_case_,snake_case_  )
      return temp + right


if __name__ == "__main__":
  _snake_case            = input("Enter 10 bit key: ")
  _snake_case            = input("Enter 8 bit message: ")

  _snake_case            = [6, 3, 7, 4, 8, 5, 10, 9]
  _snake_case            = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
  _snake_case            = [2, 4, 3, 1]
  _snake_case            = [2, 6, 3, 1, 4, 8, 5, 7]
  _snake_case            = [4, 1, 3, 5, 7, 2, 8, 6]
  _snake_case            = [4, 1, 2, 3, 2, 3, 4, 1]
  _snake_case            = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
  _snake_case            = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

  # key generation
  _snake_case            = apply_table(key, paa_table)
  _snake_case            = temp[:5]
  _snake_case            = temp[5:]
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)

  # encryption
  _snake_case            = apply_table(message, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Cipher text is:", CT)

  # decryption
  _snake_case            = apply_table(CT, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Plain text after decypting is:", PT)

