



"""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


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



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   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      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      int:





    '''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      ,     _SCREAMING_SNAKE_CASE = "auto"						)  ->      Optional[Any]:





    '''simple docstring'''




    if slice_size == "auto":
     UpperCAmelCase	:					Optional[int]	=	self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    self.enable_attention_slicing(_SCREAMING_SNAKE_CASE						)




   @torch.no_grad()
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=16000      ,     _SCREAMING_SNAKE_CASE = 512      ,     _SCREAMING_SNAKE_CASE = 512      ,     _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      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.speech_processor.feature_extractor(
        _SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""      ,     sampling_rate=_SCREAMING_SNAKE_CASE						).input_features.to(self.device						)
    UpperCAmelCase	:					Union[str, Any]	=	self.speech_model.generate(_SCREAMING_SNAKE_CASE      ,     max_length=480000						)

    UpperCAmelCase	:					Union[str, Any]	=	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	:					Tuple	=	1
    elif isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					int	=	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	:					Union[str, Any]	=	self.tokenizer(
        _SCREAMING_SNAKE_CASE      ,     padding="""max_length"""      ,     max_length=self.tokenizer.model_max_length      ,     return_tensors="""pt"""      ,     )
    UpperCAmelCase	:					Optional[Any]	=	text_inputs.input_ids

    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
     UpperCAmelCase	:					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}"						)
     UpperCAmelCase	:					Optional[int]	=	text_input_ids[:, : self.tokenizer.model_max_length]
    UpperCAmelCase	:					List[Any]	=	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	:					int	=	text_embeddings.shape
    UpperCAmelCase	:					Optional[Any]	=	text_embeddings.repeat(1      ,     _SCREAMING_SNAKE_CASE      ,     1						)
    UpperCAmelCase	:					Optional[Any]	=	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	:					List[Any]	=	guidance_scale > 1.0
    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance:
     UpperCAmelCase	:					List[str]
     if negative_prompt is None:
      UpperCAmelCase	:					str	=	[""""""] * 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	:					Union[str, Any]	=	[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	:					Any	=	negative_prompt

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

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

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

     # predict the noise residual
     UpperCAmelCase	:					Tuple	=	self.unet(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     encoder_hidden_states=_SCREAMING_SNAKE_CASE						).sample

     # perform guidance
     if do_classifier_free_guidance:
      UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	noise_pred.chunk(2						)
      UpperCAmelCase	:					str	=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase	:					Tuple	=	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	:					Optional[int]	=	1 / 0.1_8215 * latents
    UpperCAmelCase	:					Optional[Any]	=	self.vae.decode(_SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					List[Any]	=	(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	:					Tuple	=	image.cpu().permute(0      ,     2      ,     3      ,     1						).float().numpy()

    if output_type == "pil":
     UpperCAmelCase	:					List[Any]	=	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						)





"""simple docstring"""
A:   int								=		range(2, 2_0 + 1)
A:   Any								=		[1_0**k for k in range(ks[-1] + 1)]
A:   dict[int, dict[int, list[list[int]]]]								=		{}

def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					List[str]	=	sum(a_i[j] for j in range(UpperCamelCase			,		len(UpperCamelCase						)						)						)
 UpperCAmelCase	:					str	=	sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase						)			,		UpperCamelCase						)						)						)

 UpperCAmelCase	,		UpperCAmelCase	:					str	=	0, 0
 UpperCAmelCase	:					Optional[Any]	=	n - i

 UpperCAmelCase	:					Optional[int]	=	memo.get(UpperCamelCase						)

 if sub_memo is not None:
  UpperCAmelCase	:					str	=	sub_memo.get(UpperCamelCase						)

  if jumps is not None and len(UpperCamelCase						) > 0:
   # find and make the largest jump without going over
   UpperCAmelCase	:					Tuple	=	-1
   for _k in range(len(UpperCamelCase						) - 1			,		-1			,		-1						):
    if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
     UpperCAmelCase	:					int	=	_k
     break

   if max_jump >= 0:
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	jumps[max_jump]
    # since the difference between jumps is cached, add c
    UpperCAmelCase	:					List[str]	=	diff + c
    for j in range(min(UpperCamelCase			,		len(UpperCamelCase						)						)						):
     UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	divmod(UpperCamelCase			,		10						)
    if new_c > 0:
     add(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

  else:
   UpperCAmelCase	:					int	=	[]
 else:
  UpperCAmelCase	:					List[str]	=	{c: []}
  UpperCAmelCase	:					str	=	sub_memo

 if dn >= max_dn or c + diff >= base[k]:
  return diff, dn

 if k > ks[0]:
  while True:
   # keep doing smaller jumps
   UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	next_term(UpperCamelCase			,		k - 1			,		i + dn			,		UpperCamelCase						)
   diff += _diff
   dn += terms_jumped

   if dn >= max_dn or c + diff >= base[k]:
    break
 else:
  # would be too small a jump, just compute sequential terms instead
  UpperCAmelCase	,		UpperCAmelCase	:					int	=	compute(UpperCamelCase			,		UpperCamelCase			,		i + dn			,		UpperCamelCase						)
  diff += _diff
  dn += terms_jumped

 UpperCAmelCase	:					Dict	=	sub_memo[c]

 # keep jumps sorted by # of terms skipped
 UpperCAmelCase	:					str	=	0
 while j < len(UpperCamelCase						):
  if jumps[j][1] > dn:
   break
  j += 1

 # cache the jump for this value digitsum(b) and c
 sub_memo[c].insert(UpperCamelCase			,		(diff, dn, k)						)
 return (diff, dn)

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Any						):
 if i >= n:
  return 0, i
 if k > len(UpperCamelCase						):
  a_i.extend([0 for _ in range(k - len(UpperCamelCase						)						)]						)

 # note: a_i -> b * 10^k + c
 # ds_b -> digitsum(b)
 # ds_c -> digitsum(c)
 UpperCAmelCase	:					List[str]	=	i
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	0, 0, 0
 for j in range(len(UpperCamelCase						)						):
  if j >= k:
   ds_b += a_i[j]
  else:
   ds_c += a_i[j]

 while i < n:
  i += 1
  UpperCAmelCase	:					Optional[int]	=	ds_c + ds_b
  diff += addend
  UpperCAmelCase	:					str	=	0
  for j in range(UpperCamelCase						):
   UpperCAmelCase	:					Any	=	a_i[j] + addend
   UpperCAmelCase	,		UpperCAmelCase	:					Any	=	divmod(UpperCamelCase			,		10						)

   ds_c += a_i[j]

  if addend > 0:
   break

 if addend > 0:
  add(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 return diff, i - start_i

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[int]						):
 for j in range(UpperCamelCase			,		len(UpperCamelCase						)						):
  UpperCAmelCase	:					Optional[int]	=	digits[j] + addend
  if s >= 10:
   UpperCAmelCase	,		UpperCAmelCase	:					int	=	divmod(UpperCamelCase			,		10						)
   UpperCAmelCase	:					str	=	addend // 10 + quotient
  else:
   UpperCAmelCase	:					Any	=	s
   UpperCAmelCase	:					Union[str, Any]	=	addend // 10

  if addend == 0:
   break

 while addend > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Any	=	divmod(UpperCamelCase			,		10						)
  digits.append(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		int = 10**15						):
 UpperCAmelCase	:					Dict	=	[1]
 UpperCAmelCase	:					int	=	1
 UpperCAmelCase	:					Tuple	=	0
 while True:
  UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	next_term(UpperCamelCase			,		20			,		i + dn			,		UpperCamelCase						)
  dn += terms_jumped
  if dn == n - i:
   break

 UpperCAmelCase	:					Any	=	0
 for j in range(len(UpperCamelCase						)						):
  a_n += digits[j] * 10**j
 return a_n


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