






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


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





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    WhisperForConditionalGeneration   ,						_SCREAMING_SNAKE_CASE      :    WhisperProcessor   ,						_SCREAMING_SNAKE_CASE      :    AutoencoderKL   ,						_SCREAMING_SNAKE_CASE      :    CLIPTextModel   ,						_SCREAMING_SNAKE_CASE      :    CLIPTokenizer   ,						_SCREAMING_SNAKE_CASE      :    UNetaDConditionModel   ,						_SCREAMING_SNAKE_CASE      :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]   ,						_SCREAMING_SNAKE_CASE      :    StableDiffusionSafetyChecker   ,						_SCREAMING_SNAKE_CASE      :    CLIPImageProcessor   ,						):






           """simple docstring"""




           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=_SCREAMING_SNAKE_CASE   ,						speech_processor=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						feature_extractor=_SCREAMING_SNAKE_CASE   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, int]] = "auto" ):






           """simple docstring"""




           if slice_size == "auto":
                 UpperCamelCase					       =  self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           self.enable_attention_slicing(_SCREAMING_SNAKE_CASE )







     @torch.no_grad()
     def __call__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=1_6000   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 50   ,						_SCREAMING_SNAKE_CASE      :    float = 7.5   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, List[str]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = 1   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.Generator] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.FloatTensor] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = "pil"   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.speech_processor.feature_extractor(
               _SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						sampling_rate=_SCREAMING_SNAKE_CASE ).input_features.to(self.device )
           UpperCamelCase					       =  self.speech_model.generate(_SCREAMING_SNAKE_CASE   ,						max_length=48_0000 )

           UpperCamelCase					       =  self.speech_processor.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE   ,						normalize=_SCREAMING_SNAKE_CASE )[
               0
           ]

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

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

           # get prompt text embeddings
           UpperCamelCase					       =  self.tokenizer(
               _SCREAMING_SNAKE_CASE   ,						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]
           UpperCamelCase					       =  self.text_encoder(text_input_ids.to(self.device ) )[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  text_embeddings.shape
           UpperCamelCase					       =  text_embeddings.repeat(1   ,						_SCREAMING_SNAKE_CASE   ,						1 )
           UpperCamelCase					       =  text_embeddings.view(bs_embed * num_images_per_prompt   ,						_SCREAMING_SNAKE_CASE   ,						-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					       =  [''] * batch_size
                 elif type(_SCREAMING_SNAKE_CASE ) is not type(_SCREAMING_SNAKE_CASE ):
                       raise TypeError(
                           f'`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE )} !='
                           f' {type(_SCREAMING_SNAKE_CASE )}.' )
                 elif isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                       UpperCamelCase					       =  [negative_prompt]
                 elif batch_size != len(_SCREAMING_SNAKE_CASE ):
                       raise ValueError(
                           f'`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE )}, 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(
                     _SCREAMING_SNAKE_CASE   ,						padding='max_length'   ,						max_length=_SCREAMING_SNAKE_CASE   ,						truncation=_SCREAMING_SNAKE_CASE   ,						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(1   ,						_SCREAMING_SNAKE_CASE   ,						1 )
                 UpperCamelCase					       =  uncond_embeddings.view(batch_size * num_images_per_prompt   ,						_SCREAMING_SNAKE_CASE   ,						-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					       =  text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not exist on mps
                       UpperCamelCase					       =  torch.randn(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device='cpu'   ,						dtype=_SCREAMING_SNAKE_CASE ).to(
                           self.device )
                 else:
                       UpperCamelCase					       =  torch.randn(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						device=self.device   ,						dtype=_SCREAMING_SNAKE_CASE )
           else:
                 if latents.shape != latents_shape:
                       raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
                 UpperCamelCase					       =  latents.to(self.device )

           # set timesteps
           self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

           # 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(_SCREAMING_SNAKE_CASE ) ):
                 # 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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

                 # predict the noise residual
                 UpperCamelCase					       =  self.unet(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE ).sample

                 # perform guidance
                 if do_classifier_free_guidance:
                       UpperCamelCase       ,  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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE ).prev_sample

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

           UpperCamelCase					       =  1 / 0.1_8_2_1_5 * latents
           UpperCamelCase					       =  self.vae.decode(_SCREAMING_SNAKE_CASE ).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 output_type == "pil":
                 UpperCamelCase					       =  self.numpy_to_pil(_SCREAMING_SNAKE_CASE )

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE   ,						nsfw_content_detected=_SCREAMING_SNAKE_CASE )











def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  len(_UpperCamelCase) if (len(_UpperCamelCase) > 7) else 7

      # Print table header for output
      print(
          'Symbol'.center(8)       ,							'Stack'.center(_UpperCamelCase)       ,							'Postfix'.center(_UpperCamelCase)       ,							sep=' | '       ,							)
      print('-' * (print_width * 3 + 7))

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(_UpperCamelCase)  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(_UpperCamelCase)  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(_UpperCamelCase) == 0:
                        stack.append(_UpperCamelCase)  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(_UpperCamelCase) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop())  # pop stack & add to Postfix
                        stack.append(_UpperCamelCase)  # push x to stack

            print(
                x.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      while len(_UpperCamelCase) > 0:  # while stack is not empty
            post_fix.append(stack.pop())  # pop stack & add to Postfix
            print(
                ' '.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      return "".join(_UpperCamelCase)  # return Postfix as str
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

      return (infix_2_postfix(''.join(_UpperCamelCase)))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



