
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     UpperCAmelCase						(		A_					):



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


          '''simple docstring'''


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

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Union[str, List[str]]					)						->      List[int]:


          '''simple docstring'''


          if not isinstance(snake_case__       ,		snake_case__					):
                 snake_case			:  Tuple     							= 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						:   Optional[Any]       ,		snake_case__						:   List[int]       ,		snake_case__						:   float = 4.0       ,		snake_case__						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		snake_case__						:   int = 50       ,		snake_case__						:   Optional[str] = "pil"       ,		snake_case__						:   bool = True       ,		)						->      Union[ImagePipelineOutput, Tuple]:


          '''simple docstring'''


          snake_case			:  Tuple     							= len(snake_case__					)
          snake_case			:  str     							= self.transformer.config.sample_size
          snake_case			:  List[str]     							= self.transformer.config.in_channels

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

          snake_case			:  List[str]     							= torch.tensor(snake_case__       ,		device=self.device					).reshape(-1					)
          snake_case			:  Any     							= torch.tensor([10_00] * batch_size       ,		device=self.device					)
          snake_case			:  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:
                        snake_case			:  Tuple     							= latent_model_input[: len(snake_case__					) // 2]
                        snake_case			:  str     							= torch.cat([half, half]       ,		dim=0					)
                 snake_case			:  Optional[int]     							= self.scheduler.scale_model_input(snake_case__       ,		snake_case__					)

                 snake_case			:  Dict     							= 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+)
                        snake_case			:  str     							= latent_model_input.device.type == "mps"
                        if isinstance(snake_case__       ,		snake_case__					):
                               snake_case			:  Dict     							= torch.floataa if is_mps else torch.floataa
                        else:
                               snake_case			:  str     							= torch.intaa if is_mps else torch.intaa
                        snake_case			:  Union[str, Any]     							= torch.tensor([timesteps]       ,		dtype=snake_case__       ,		device=latent_model_input.device					)
                 elif len(timesteps.shape					) == 0:
                        snake_case			:  Any     							= timesteps[None].to(latent_model_input.device					)
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 snake_case			:  Union[str, Any]     							= timesteps.expand(latent_model_input.shape[0]					)
                 # predict noise model_output
                 snake_case			:  Any     							= self.transformer(
                     snake_case__       ,		timestep=snake_case__       ,		class_labels=snake_case__					).sample

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

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

                        snake_case			:  Dict     							= torch.cat([eps, rest]       ,		dim=1					)

                 # learned sigma
                 if self.transformer.config.out_channels // 2 == latent_channels:
                        snake_case							,   snake_case			:  int     							= torch.split(snake_case__       ,		snake_case__       ,		dim=1					)
                 else:
                        snake_case			:  str     							= noise_pred

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

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

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

          snake_case			:  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
          snake_case			:  Any     							= samples.cpu().permute(0       ,		2       ,		3       ,		1					).float().numpy()

          if output_type == "pil":
                 snake_case			:  Dict     							= self.numpy_to_pil(snake_case__					)

          if not return_dict:
                 return (samples,)

          return ImagePipelineOutput(images=snake_case__					)







__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





