


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 			__lowerCAmelCase   (       snake_case_    ):






  """simple docstring"""





  def __init__(   self				:				List[str]			, _lowerCAmelCase				:				TransformeraDModel			, _lowerCAmelCase				:				AutoencoderKL			, _lowerCAmelCase				:				KarrasDiffusionSchedulers			, _lowerCAmelCase				:				Optional[Dict[int, str]] = None			, )      ->       Optional[Any]:




     """simple docstring"""
     super().__init__()
     self.register_modules(transformer=UpperCAmelCase__			, vae=UpperCAmelCase__			, scheduler=UpperCAmelCase__     )

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



  def     lowerCAmelCase__     (   self				:				str			, _lowerCAmelCase				:				Union[str, List[str]]     )      ->       List[int]:




     """simple docstring"""

     if not isinstance(UpperCAmelCase__			, UpperCAmelCase__     ):
        snake_case_    			=    list(UpperCAmelCase__     )

     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				:				Tuple			, _lowerCAmelCase				:				List[int]			, _lowerCAmelCase				:				float = 4.0			, _lowerCAmelCase				:				Optional[Union[torch.Generator, List[torch.Generator]]] = None			, _lowerCAmelCase				:				int = 5_0			, _lowerCAmelCase				:				Optional[str] = "pil"			, _lowerCAmelCase				:				bool = True			, )      ->       Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     snake_case_    			=    len(UpperCAmelCase__     )
     snake_case_    			=    self.transformer.config.sample_size
     snake_case_    			=    self.transformer.config.in_channels

     snake_case_    			=    randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)			, generator=UpperCAmelCase__			, device=self.device			, dtype=self.transformer.dtype			, )
     snake_case_    			=    torch.cat([latents] * 2     ) if guidance_scale > 1 else latents

     snake_case_    			=    torch.tensor(UpperCAmelCase__			, device=self.device     ).reshape(-1     )
     snake_case_    			=    torch.tensor([1_0_0_0] * batch_size			, device=self.device     )
     snake_case_    			=    torch.cat([class_labels, class_null]			, 0     ) if guidance_scale > 1 else class_labels

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

     for t in self.progress_bar(self.scheduler.timesteps     ):
        if guidance_scale > 1:
           snake_case_    			=    latent_model_input[: len(UpperCAmelCase__     ) // 2]
           snake_case_    			=    torch.cat([half, half]			, dim=0     )
        snake_case_    			=    self.scheduler.scale_model_input(UpperCAmelCase__			, UpperCAmelCase__     )

        snake_case_    			=    t
        if not torch.is_tensor(UpperCAmelCase__     ):
           # 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+)
           snake_case_    			=    latent_model_input.device.type == """mps"""
           if isinstance(UpperCAmelCase__			, UpperCAmelCase__     ):
              snake_case_    			=    torch.floataa if is_mps else torch.floataa
           else:
              snake_case_    			=    torch.intaa if is_mps else torch.intaa
           snake_case_    			=    torch.tensor([timesteps]			, dtype=UpperCAmelCase__			, device=latent_model_input.device     )
        elif len(timesteps.shape     ) == 0:
           snake_case_    			=    timesteps[None].to(latent_model_input.device     )
        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        snake_case_    			=    timesteps.expand(latent_model_input.shape[0]     )
        # predict noise model_output
        snake_case_    			=    self.transformer(
            UpperCAmelCase__			, timestep=UpperCAmelCase__			, class_labels=UpperCAmelCase__     ).sample

        # perform guidance
        if guidance_scale > 1:
           snake_case_    			=    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
           snake_case_    			=    torch.split(UpperCAmelCase__			, len(UpperCAmelCase__     ) // 2			, dim=0     )

           snake_case_    			=    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
           snake_case_    			=    torch.cat([half_eps, half_eps]			, dim=0     )

           snake_case_    			=    torch.cat([eps, rest]			, dim=1     )

        # learned sigma
        if self.transformer.config.out_channels // 2 == latent_channels:
           snake_case_    			=    torch.split(UpperCAmelCase__			, UpperCAmelCase__			, dim=1     )
        else:
           snake_case_    			=    noise_pred

        # compute previous image: x_t -> x_t-1
        snake_case_    			=    self.scheduler.step(UpperCAmelCase__			, UpperCAmelCase__			, UpperCAmelCase__     ).prev_sample

     if guidance_scale > 1:
        snake_case_    			=    latent_model_input.chunk(2			, dim=0     )
     else:
        snake_case_    			=    latent_model_input

     snake_case_    			=    1 / self.vae.config.scaling_factor * latents
     snake_case_    			=    self.vae.decode(UpperCAmelCase__     ).sample

     snake_case_    			=    (samples / 2 + 0.5).clamp(0			, 1     )

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_    			=    samples.cpu().permute(0			, 2			, 3			, 1     ).float().numpy()

     if output_type == "pil":
        snake_case_    			=    self.numpy_to_pil(UpperCAmelCase__     )

     if not return_dict:
        return (samples,)

     return ImagePipelineOutput(images=UpperCAmelCase__     )












"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):



  '''simple docstring'''
  _a  :       Optional[Any]             =	[redshift, radiation_density, matter_density, dark_energy]
  if any(p < 0 for p in parameters				):
    raise ValueError("""All input parameters must be positive"""				)

  if any(p > 1 for p in parameters[1:4]				):
    raise ValueError("""Relative densities cannot be greater than one"""				)
  else:
    _a  :       Tuple             =	1 - (matter_density + radiation_density + dark_energy)

    _a  :       int             =	(
        radiation_density * (redshift + 1) ** 4
        + matter_density * (redshift + 1) ** 3
        + curvature * (redshift + 1) ** 2
        + dark_energy
    )

    _a  :       List[str]             =	hubble_constant * e_a ** (1 / 2)
    return hubble


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo LCDM approximation
 _snake_case          =   0.3

 print(
     hubble_parameter(
         hubble_constant=68.3,
         radiation_density=1e-4,
         matter_density=matter_density,
         dark_energy=1 - matter_density,
         redshift=0,
     )
 )

