

"""simple docstring"""






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


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







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,):
      '''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=_A    ,speech_processor=_A    ,vae=_A    ,text_encoder=_A    ,tokenizer=_A    ,unet=_A    ,scheduler=_A    ,feature_extractor=_A    ,)

  def __lowerCamelCase		(		self    ,_A = "auto"      ):
      '''simple docstring'''

      if slice_size == "auto":
          _lowerCAmelCase       :   int  =    self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      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    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.speech_processor.feature_extractor(
          _A    ,return_tensors='pt'    ,sampling_rate=_A      ).input_features.to(self.device      )
      _lowerCAmelCase       :   List[str]  =    self.speech_model.generate(_A    ,max_length=48_0000      )

      _lowerCAmelCase       :   Optional[Any]  =    self.speech_processor.tokenizer.batch_decode(_A    ,skip_special_tokens=_A    ,normalize=_A      )[
          0
      ]

      if isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   str  =    1
      elif isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Tuple  =    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
      _lowerCAmelCase       :   Optional[int]  =    self.tokenizer(
          _A    ,padding='max_length'    ,max_length=self.tokenizer.model_max_length    ,return_tensors='pt'    ,)
      _lowerCAmelCase       :   Optional[Any]  =    text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          _lowerCAmelCase       :   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}"""      )
          _lowerCAmelCase       :   Any  =    text_input_ids[:, : self.tokenizer.model_max_length]
      _lowerCAmelCase       :   Optional[Any]  =    self.text_encoder(text_input_ids.to(self.device      )      )[0]

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

          _lowerCAmelCase       :   str  =    text_input_ids.shape[-1]
          _lowerCAmelCase       :   str  =    self.tokenizer(
              _A    ,padding='max_length'    ,max_length=_A    ,truncation=_A    ,return_tensors='pt'    ,)
          _lowerCAmelCase       :   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
          _lowerCAmelCase       :   Any  =    uncond_embeddings.shape[1]
          _lowerCAmelCase       :   Any  =    uncond_embeddings.repeat(1    ,_A    ,1      )
          _lowerCAmelCase       :   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
          _lowerCAmelCase       :   Optional[int]  =    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`.
      _lowerCAmelCase       :   Union[str, Any]  =    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      _lowerCAmelCase       :   int  =    text_embeddings.dtype
      if latents is None:
          if self.device.type == "mps":
              # randn does not exist on mps
              _lowerCAmelCase       :   int  =    torch.randn(_A    ,generator=_A    ,device='cpu'    ,dtype=_A      ).to(
                  self.device      )
          else:
              _lowerCAmelCase       :   Tuple  =    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}"""      )
          _lowerCAmelCase       :   int  =    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
      _lowerCAmelCase       :   int  =    self.scheduler.timesteps.to(self.device      )

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

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

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

          # perform guidance
          if do_classifier_free_guidance:
              _lowerCAmelCase,					_lowerCAmelCase       :   Any  =    noise_pred.chunk(2      )
              _lowerCAmelCase       :   Union[str, Any]  =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          _lowerCAmelCase       :   Optional[int]  =    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      )

      _lowerCAmelCase       :   Any  =    1 / 0.1_8_2_1_5 * latents
      _lowerCAmelCase       :   Union[str, Any]  =    self.vae.decode(_A      ).sample

      _lowerCAmelCase       :   List[str]  =    (image / 2 + 0.5).clamp(0    ,1      )

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

      if output_type == "pil":
          _lowerCAmelCase       :   Any  =    self.numpy_to_pil(_A      )

      if not return_dict:
          return image

      return StableDiffusionPipelineOutput(images=_A    ,nsfw_content_detected=_A      )








"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

_lowerCAmelCase						=		list[list[float | int]]

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    len(_lowerCamelCase   )
    _lowerCAmelCase       :   Matrix  =    [[0 for _ in range(size + 1   )] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   float

    for row in range(_lowerCamelCase   ):
        for col in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   Union[str, Any]  =    matrix[row][col]

        _lowerCAmelCase       :   Tuple  =    vector[row][0]

    _lowerCAmelCase       :   Dict  =    0
    _lowerCAmelCase       :   Any  =    0
    while row < size and col < size:
        # pivoting
        _lowerCAmelCase       :   Optional[int]  =    max((abs(augmented[rowa][col]   ), rowa) for rowa in range(_lowerCamelCase ,   _lowerCamelCase   )   )[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    augmented[pivot_row], augmented[row]

        for rowa in range(row + 1 ,   _lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    augmented[rowa][col] / augmented[row][col]
            _lowerCAmelCase       :   Optional[Any]  =    0
            for cola in range(col + 1 ,   size + 1   ):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1 ,   _lowerCamelCase   ):
        for row in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   int  =    augmented[row][col] / augmented[col][col]
            for cola in range(_lowerCamelCase ,   size + 1   ):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row] ,   10   )] for row in range(_lowerCamelCase   )
    ]

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    len(_lowerCamelCase   )
    _lowerCAmelCase       :   Matrix  =    [[0 for _ in range(_lowerCamelCase   )] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   Matrix  =    [[0] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   Matrix
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int

    for x_val, y_val in enumerate(_lowerCamelCase   ):
        for col in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   Union[str, Any]  =    (x_val + 1) ** (size - col - 1)
        _lowerCAmelCase       :   Optional[int]  =    y_val

    _lowerCAmelCase       :   List[Any]  =    solve(_lowerCamelCase ,   _lowerCamelCase   )

    def interpolated_func(_lowerCamelCase   ) -> int:
        return sum(
            round(coeffs[x_val][0]   ) * (var ** (size - x_val - 1))
            for x_val in range(_lowerCamelCase   )   )

    return interpolated_func

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )

def 		lowerCamelCase__		(					_lowerCamelCase = question_function ,   _lowerCamelCase = 10   ):

    '''simple docstring'''



    _lowerCAmelCase       :   list[int]  =    [func(_lowerCamelCase   ) for x_val in range(1 ,   order + 1   )]

    _lowerCAmelCase       :   list[Callable[[int], int]]  =    [
        interpolate(data_points[:max_coeff]   ) for max_coeff in range(1 ,   order + 1   )
    ]

    _lowerCAmelCase       :   int  =    0
    _lowerCAmelCase       :   Callable[[int], int]
    _lowerCAmelCase       :   int

    for poly in polynomials:
        _lowerCAmelCase       :   Any  =    1
        while func(_lowerCamelCase   ) == poly(_lowerCamelCase   ):
            x_val += 1

        ret += poly(_lowerCamelCase   )

    return ret


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





