code: 
  
  
  
  '''simple docstring'''
  
  
  
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  a__						:			int										=			getLogger(__name__)
  
  
  a__						:			Union[str, Any]										=			4
  a__						:			str										=			7
  
  a__						:			Optional[Any]										=			{
      'float16': torch.floataa,
      'float32': torch.floataa,
  }
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   if isinstance(__A							,torch.Tensor       ):
    return image
   elif isinstance(__A							,PIL.Image.Image       ):
    __UpperCamelCase				      =  [image]
  
   if isinstance(image[0]							,PIL.Image.Image       ):
    __UpperCamelCase      ,		__UpperCamelCase				      =  image[0].size
    __UpperCamelCase      ,		__UpperCamelCase				      =  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
    __UpperCamelCase				      =  [np.array(i.resize((w, h)       )       )[None, :] for i in image]
    __UpperCamelCase				      =  np.concatenate(__A							,axis=0       )
    __UpperCamelCase				      =  np.array(__A       ).astype(np.floataa       ) / 255.0
    __UpperCamelCase				      =  image.transpose(0							,3							,1							,2       )
    __UpperCamelCase				      =  2.0 * image - 1.0
    __UpperCamelCase				      =  torch.from_numpy(__A       )
   elif isinstance(image[0]							,torch.Tensor       ):
    __UpperCamelCase				      =  torch.cat(__A							,dim=0       )
  
   return image
  
  
  
  
  
  
  class        UpperCAmelCase__  (    UpperCAmelCase_):
  
  
  
   def __init__(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase = 3_5_0		,	)  ->							Any:
    super().__init__(
        vae=lowercase		,	text_encoder=lowercase		,	tokenizer=lowercase		,	unet=lowercase		,	low_res_scheduler=lowercase		,	scheduler=lowercase		,	safety_checker=lowercase		,	feature_extractor=lowercase		,	watermarker=lowercase		,	max_noise_level=lowercase		,	)
  
  
  
   def __call__(  self		,	lowercase		,	lowercase		,	lowercase = 7_5		,	lowercase = 9.0		,	lowercase = 2_0		,	lowercase = None		,	lowercase = 1		,	lowercase = 0.0		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = "pil"		,	lowercase = True		,	lowercase = None		,	lowercase = 1		,	)  ->							Dict:
    self.check_inputs(lowercase		,	lowercase		,	lowercase		,	lowercase			)
  
    # 2. Define call parameters
    if prompt is not None and isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  1
    elif prompt is not None and isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  len(lowercase			)
    else:
     __UpperCamelCase				      =  prompt_embeds.shape[0]
  
    __UpperCamelCase				      =  self._execution_device
    # 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
  
    # 3. Encode input prompt
    __UpperCamelCase				      =  self._encode_prompt(
        lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	prompt_embeds=lowercase		,	negative_prompt_embeds=lowercase		,	)
  
    __UpperCamelCase				      =  ORT_TO_PT_TYPE[str(text_embeddings.dtype			)]
  
    # 4. Preprocess image
    __UpperCamelCase				      =  preprocess(lowercase			)
    __UpperCamelCase				      =  image.cpu()
  
    # 5. set timesteps
    self.scheduler.set_timesteps(lowercase		,	device=lowercase			)
    __UpperCamelCase				      =  self.scheduler.timesteps
  
    # 5. Add noise to image
    __UpperCamelCase				      =  torch.tensor([noise_level]		,	dtype=torch.long		,	device=lowercase			)
    __UpperCamelCase				      =  torch.randn(image.shape		,	generator=lowercase		,	device=lowercase		,	dtype=lowercase			)
    __UpperCamelCase				      =  self.low_res_scheduler.add_noise(lowercase		,	lowercase		,	lowercase			)
  
    __UpperCamelCase				      =  2 if do_classifier_free_guidance else 1
    __UpperCamelCase				      =  np.concatenate([image] * batch_multiplier * num_images_per_prompt			)
    __UpperCamelCase				      =  np.concatenate([noise_level] * image.shape[0]			)
  
    # 6. Prepare latent variables
    __UpperCamelCase      ,		__UpperCamelCase				      =  image.shape[2:]
    __UpperCamelCase				      =  self.prepare_latents(
        batch_size * num_images_per_prompt		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)
  
    # 7. Check that sizes of image and latents match
    __UpperCamelCase				      =  image.shape[1]
    if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
     raise ValueError(
         """Incorrect configuration settings! The config of `pipeline.unet` expects"""
         f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
         f" `num_channels_image`: {num_channels_image} "
         f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
         """ `pipeline.unet` or your `image` input."""			)
  
    # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
    __UpperCamelCase				      =  self.prepare_extra_step_kwargs(lowercase		,	lowercase			)
  
    __UpperCamelCase				      =  next(
        (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")		,	"""tensor(float)"""			)
    __UpperCamelCase				      =  ORT_TO_NP_TYPE[timestep_dtype]
  
    # 9. Denoising loop
    __UpperCamelCase				      =  len(lowercase			) - num_inference_steps * self.scheduler.order
    with self.progress_bar(total=lowercase			) as progress_bar:
     for i, t in enumerate(lowercase			):
      # expand the latents if we are doing classifier free guidance
      __UpperCamelCase				      =  np.concatenate([latents] * 2			) if do_classifier_free_guidance else latents
  
      # concat latents, mask, masked_image_latents in the channel dimension
      __UpperCamelCase				      =  self.scheduler.scale_model_input(lowercase		,	lowercase			)
      __UpperCamelCase				      =  np.concatenate([latent_model_input, image]		,	axis=1			)
  
      # timestep to tensor
      __UpperCamelCase				      =  np.array([t]		,	dtype=lowercase			)
  
      # predict the noise residual
      __UpperCamelCase				      =  self.unet(
          sample=lowercase		,	timestep=lowercase		,	encoder_hidden_states=lowercase		,	class_labels=noise_level.astype(np.intaa			)		,	)[0]
  
      # perform guidance
      if do_classifier_free_guidance:
       __UpperCamelCase      ,		__UpperCamelCase				      =  np.split(lowercase		,	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(
          torch.from_numpy(lowercase			)		,	lowercase		,	lowercase		,	**lowercase			).prev_sample
  
      # call the callback, if provided
      if i == len(lowercase			) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
       progress_bar.update()
       if callback is not None and i % callback_steps == 0:
        callback(lowercase		,	lowercase		,	lowercase			)
  
          # 10. Post-processing
    __UpperCamelCase				      =  self.decode_latents(latents.float()			)
  
    # 11. Convert to PIL
    if output_type == "pil":
     __UpperCamelCase				      =  self.numpy_to_pil(lowercase			)
  
    if not return_dict:
     return (image,)
  
    return ImagePipelineOutput(images=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Any:
    __UpperCamelCase				      =  1 / 0.08_333 * latents
    __UpperCamelCase				      =  self.vae(latent_sample=lowercase			)[0]
    __UpperCamelCase				      =  np.clip(image / 2 + 0.5		,	0		,	1			)
    __UpperCamelCase				      =  image.transpose((0, 2, 3, 1)			)
    return image
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase = None		,	lowercase = None		,	)  ->							Dict:
    if prompt is not None and isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  1
    elif prompt is not None and isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  len(lowercase			)
    else:
     __UpperCamelCase				      =  prompt_embeds.shape[0]
  
    if prompt_embeds is None:
     __UpperCamelCase				      =  self.tokenizer(
         lowercase		,	padding="""max_length"""		,	max_length=self.tokenizer.model_max_length		,	truncation=lowercase		,	return_tensors="""pt"""		,	)
     __UpperCamelCase				      =  text_inputs.input_ids
     __UpperCamelCase				      =  self.tokenizer(lowercase		,	padding="""longest"""		,	return_tensors="""pt"""			).input_ids
  
     if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
         lowercase		,	lowercase			):
      __UpperCamelCase				      =  self.tokenizer.batch_decode(
          untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]			)
      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}"			)
  
     # no positional arguments to text_encoder
     __UpperCamelCase				      =  self.text_encoder(
         input_ids=text_input_ids.int().to(lowercase			)		,	)
     __UpperCamelCase				      =  prompt_embeds[0]
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    __UpperCamelCase				      =  prompt_embeds.repeat(1		,	lowercase			)
    __UpperCamelCase				      =  prompt_embeds.reshape(bs_embed * num_images_per_prompt		,	lowercase		,	-1			)
  
    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance and negative_prompt_embeds is None:
     __UpperCamelCase				      =  42
     if negative_prompt is None:
      __UpperCamelCase				      =  [""""""] * batch_size
     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			):
      __UpperCamelCase				      =  [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:
      __UpperCamelCase				      =  negative_prompt
  
     __UpperCamelCase				      =  text_input_ids.shape[-1]
     __UpperCamelCase				      =  self.tokenizer(
         lowercase		,	padding="""max_length"""		,	max_length=lowercase		,	truncation=lowercase		,	return_tensors="""pt"""		,	)
  
     # if hasattr(uncond_input, "attention_mask"):
     #     attention_mask = uncond_input.attention_mask.to(device)
     # else:
     #     attention_mask = None
  
     __UpperCamelCase				      =  self.text_encoder(
         input_ids=uncond_input.input_ids.int().to(lowercase			)		,	)
     __UpperCamelCase				      =  uncond_embeddings[0]
  
    if do_classifier_free_guidance:
     __UpperCamelCase				      =  uncond_embeddings.shape[1]
     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     __UpperCamelCase				      =  uncond_embeddings.repeat(1		,	lowercase			)
     __UpperCamelCase				      =  uncond_embeddings.reshape(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
     __UpperCamelCase				      =  np.concatenate([uncond_embeddings, prompt_embeds]			)
  
    return prompt_embeds
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  a__						:			str										=			[chr(i) for i in range(3_2, 1_2_6)]
  a__						:			List[Any]										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(range(len(alphabets)))
  a__						:			Dict										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(reversed(range(len(alphabets))))
  a__						:			List[str]										=			[]
  a__						:			int										=			0
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   global gear_one_pos
   global gear_two_pos
   global gear_three_pos
   __UpperCamelCase				      =  gear_one[0]
   gear_one.append(__A       )
   del gear_one[0]
   gear_one_pos += 1
   if gear_one_pos % int(len(__A       )       ) == 0:
    __UpperCamelCase				      =  gear_two[0]
    gear_two.append(__A       )
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__A       )       ) == 0:
     __UpperCamelCase				      =  gear_three[0]
     gear_three.append(__A       )
     del gear_three[0]
     gear_three_pos += 1
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  alphabets.index(__A       )
   __UpperCamelCase				      =  gear_one[target]
   __UpperCamelCase				      =  gear_two[target]
   __UpperCamelCase				      =  gear_three[target]
   __UpperCamelCase				      =  reflector[target]
   __UpperCamelCase				      =  gear_three.index(__A       )
   __UpperCamelCase				      =  gear_two.index(__A       )
   __UpperCamelCase				      =  gear_one.index(__A       )
   code.append(alphabets[target]       )
   rotator()
  
  
  if __name__ == "__main__":
   a__						:			Any										=			list(input('Type your message:\n'))
   while True:
    try:
     a__						:			List[Any]										=			int(input('Please set token:(must be only digits)\n'))
     break
    except Exception as error:
     print(error)
   for _ in range(token):
    rotator()
   for j in decode:
    engine(j)
   print('\n' + ''.join(code))
   print(
       f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
       'this message again you should input same digits as token!'
   )
  
  
  
  
  
  