






"""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						(				lowercase_		):


       def __init__(			self      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						List[str]			,    snake_case      :						int			,    snake_case      :						Dict = None			,    ):
             '''simple docstring'''





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

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


       def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Tuple							):
             '''simple docstring'''





             if not isinstance(snake_case			,    snake_case							):
                   A__						:  int		  =  list(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      :						List[Any]			,    snake_case      :						int			,    snake_case      :						Optional[int] = 4.0			,    snake_case      :						List[str] = None			,    snake_case      :						Tuple = 50			,    snake_case      :						List[str] = "pil"			,    snake_case      :						List[Any] = True			,    ):
             '''simple docstring'''





             A__						:  Union[str, Any]		  =  len(snake_case							)
             A__						:  Optional[int]		  =  self.transformer.config.sample_size
             A__						:  Tuple		  =  self.transformer.config.in_channels

             A__						:  Optional[int]		  =  randn_tensor(
                 shape=(batch_size, latent_channels, latent_size, latent_size)			,    generator=snake_case			,    device=self.device			,    dtype=self.transformer.dtype			,    )
             A__						:  Tuple		  =  torch.cat([latents] * 2							) if guidance_scale > 1 else latents

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

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

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

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

                   # perform guidance
                   if guidance_scale > 1:
                         A__  ,  A__						:  str		  =  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                         A__  ,  A__						:  Optional[Any]		  =  torch.split(snake_case			,    len(snake_case							) // 2			,    dim=0							)

                         A__						:  Dict		  =  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                         A__						:  Optional[int]		  =  torch.cat([half_eps, half_eps]			,    dim=0							)

                         A__						:  List[Any]		  =  torch.cat([eps, rest]			,    dim=1							)

                   # learned sigma
                   if self.transformer.config.out_channels // 2 == latent_channels:
                         A__  ,  A__						:  Union[str, Any]		  =  torch.split(snake_case			,    snake_case			,    dim=1							)
                   else:
                         A__						:  str		  =  noise_pred

                   # compute previous image: x_t -> x_t-1
                   A__						:  int		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case							).prev_sample

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

             A__						:  Optional[int]		  =  1 / self.vae.config.scaling_factor * latents
             A__						:  List[str]		  =  self.vae.decode(snake_case							).sample

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

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

             if output_type == "pil":
                   A__						:  List[Any]		  =  self.numpy_to_pil(snake_case							)

             if not return_dict:
                   return (samples,)

             return ImagePipelineOutput(images=snake_case							)














"""simple docstring"""



from __future__ import annotations

A_    =							10




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[int]      )		->list[int]:
						A__						:  Any		  =  1
						A__						:  Optional[int]		  =  max(UpperCAmelCase__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												A__						:  list[list]		  =  [[] for _ in range(UpperCAmelCase__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		A__						:  List[Any]		  =  int((i / placement) % RADIX      )
																		buckets[tmp].append(UpperCAmelCase__      )
												# put each buckets' contents into list_of_ints
												A__						:  Union[str, Any]		  =  0
												for b in range(UpperCAmelCase__      ):
																		for i in buckets[b]:
																								A__						:  str		  =  i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()






