



"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    super().__init__()
    self.register_modules(transformer=_SCREAMING_SNAKE_CASE      ,     vae=_SCREAMING_SNAKE_CASE      ,     scheduler=_SCREAMING_SNAKE_CASE						)

    # create a imagenet -> id dictionary for easier use
    UpperCAmelCase	:					Tuple	=	{}
    if idalabel is not None:
     for key, value in idalabel.items():
      for label in value.split(""","""						):
       UpperCAmelCase	:					Tuple	=	int(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	dict(sorted(self.labels.items()						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[int]:





    '''simple docstring'''




    if not isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					List[Any]	=	list(_SCREAMING_SNAKE_CASE						)

    for l in label:
     if l not in self.labels:
      raise ValueError(
          F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."						)

    return [self.labels[l] for l in label]




   @torch.no_grad()
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = 4.0      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 50      ,     _SCREAMING_SNAKE_CASE = "pil"      ,     _SCREAMING_SNAKE_CASE = True      ,     )  ->      Union[ImagePipelineOutput, Tuple]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	len(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	self.transformer.config.sample_size
    UpperCAmelCase	:					Optional[Any]	=	self.transformer.config.in_channels

    UpperCAmelCase	:					List[Any]	=	randn_tensor(
        shape=(batch_size, latent_channels, latent_size, latent_size)      ,     generator=_SCREAMING_SNAKE_CASE      ,     device=self.device      ,     dtype=self.transformer.dtype      ,     )
    UpperCAmelCase	:					Tuple	=	torch.cat([latents] * 2						) if guidance_scale > 1 else latents

    UpperCAmelCase	:					str	=	torch.tensor(_SCREAMING_SNAKE_CASE      ,     device=self.device						).reshape(-1						)
    UpperCAmelCase	:					int	=	torch.tensor([1000] * batch_size      ,     device=self.device						)
    UpperCAmelCase	:					Optional[int]	=	torch.cat([class_labels, class_null]      ,     0						) if guidance_scale > 1 else class_labels

    # set step values
    self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						)

    for t in self.progress_bar(self.scheduler.timesteps						):
     if guidance_scale > 1:
      UpperCAmelCase	:					str	=	latent_model_input[: len(_SCREAMING_SNAKE_CASE						) // 2]
      UpperCAmelCase	:					Optional[Any]	=	torch.cat([half, half]      ,     dim=0						)
     UpperCAmelCase	:					Optional[int]	=	self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Any	=	t
     if not torch.is_tensor(_SCREAMING_SNAKE_CASE						):
      # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
      # This would be a good case for the `match` statement (Python 3.10+)
      UpperCAmelCase	:					Union[str, Any]	=	latent_model_input.device.type == """mps"""
      if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
       UpperCAmelCase	:					Dict	=	torch.floataa if is_mps else torch.floataa
      else:
       UpperCAmelCase	:					Dict	=	torch.intaa if is_mps else torch.intaa
      UpperCAmelCase	:					Optional[Any]	=	torch.tensor([timesteps]      ,     dtype=_SCREAMING_SNAKE_CASE      ,     device=latent_model_input.device						)
     elif len(timesteps.shape						) == 0:
      UpperCAmelCase	:					Optional[Any]	=	timesteps[None].to(latent_model_input.device						)
     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
     UpperCAmelCase	:					int	=	timesteps.expand(latent_model_input.shape[0]						)
     # predict noise model_output
     UpperCAmelCase	:					int	=	self.transformer(
         _SCREAMING_SNAKE_CASE      ,     timestep=_SCREAMING_SNAKE_CASE      ,     class_labels=_SCREAMING_SNAKE_CASE						).sample

     # perform guidance
     if guidance_scale > 1:
      UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
      UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	torch.split(_SCREAMING_SNAKE_CASE      ,     len(_SCREAMING_SNAKE_CASE						) // 2      ,     dim=0						)

      UpperCAmelCase	:					List[str]	=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
      UpperCAmelCase	:					Optional[Any]	=	torch.cat([half_eps, half_eps]      ,     dim=0						)

      UpperCAmelCase	:					Optional[Any]	=	torch.cat([eps, rest]      ,     dim=1						)

     # learned sigma
     if self.transformer.config.out_channels // 2 == latent_channels:
      UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	torch.split(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     dim=1						)
     else:
      UpperCAmelCase	:					str	=	noise_pred

     # compute previous image: x_t -> x_t-1
     UpperCAmelCase	:					Any	=	self.scheduler.step(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).prev_sample

    if guidance_scale > 1:
     UpperCAmelCase	,		UpperCAmelCase	:					str	=	latent_model_input.chunk(2      ,     dim=0						)
    else:
     UpperCAmelCase	:					Optional[int]	=	latent_model_input

    UpperCAmelCase	:					Optional[int]	=	1 / self.vae.config.scaling_factor * latents
    UpperCAmelCase	:					Tuple	=	self.vae.decode(_SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					Any	=	(samples / 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	=	samples.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 (samples,)

    return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE						)





"""simple docstring"""
from math import isqrt, loga

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					Any	=	[True] * max_number
 for i in range(2			,		isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2			,		UpperCamelCase			,		UpperCamelCase						):
    UpperCAmelCase	:					str	=	False

 return [i for i in range(2			,		UpperCamelCase						) if is_prime[i]]

def       _snake_case						(						UpperCamelCase						:		int = 800800			,		UpperCamelCase						:		int = 800800						):
 UpperCAmelCase	:					Union[str, Any]	=	degree * loga(UpperCamelCase						)
 UpperCAmelCase	:					int	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Union[str, Any]	=	calculate_prime_numbers(UpperCamelCase						)

 UpperCAmelCase	:					Dict	=	0
 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					Dict	=	len(UpperCamelCase						) - 1
 while left < right:
  while (
      prime_numbers[right] * loga(prime_numbers[left]						)
      + prime_numbers[left] * loga(prime_numbers[right]						)
      > upper_bound
  ):
   right -= 1
  hybrid_integers_count += right - left
  left += 1

 return hybrid_integers_count


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