

'''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        A__   (   _snake_case      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					)    ->   Any:





           '''simple docstring'''
           super().__init__()
           self.register_modules(transformer=UpperCamelCase__		,					vae=UpperCamelCase__		,					scheduler=UpperCamelCase__							)

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



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   List[int]:





           '''simple docstring'''

           if not isinstance(UpperCamelCase__		,					UpperCamelCase__							):
                  A_						       =   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		,					UpperCamelCase__		,					UpperCamelCase__ = 4.0		,					UpperCamelCase__ = None		,					UpperCamelCase__ = 50		,					UpperCamelCase__ = "pil"		,					UpperCamelCase__ = True		,					)    ->   Union[ImagePipelineOutput, Tuple]:





           '''simple docstring'''
           A_						       =   len(UpperCamelCase__							)
           A_						       =   self.transformer.config.sample_size
           A_						       =   self.transformer.config.in_channels

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

           A_						       =   torch.tensor(UpperCamelCase__		,					device=self.device							).reshape(-1							)
           A_						       =   torch.tensor([1000] * batch_size		,					device=self.device							)
           A_						       =   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:
                         A_						       =   latent_model_input[: len(UpperCamelCase__							) // 2]
                         A_						       =   torch.cat([half, half]		,					dim=0							)
                  A_						       =   self.scheduler.scale_model_input(UpperCamelCase__		,					UpperCamelCase__							)

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

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

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

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

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

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

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

           A_						       =   1 / self.vae.config.scaling_factor * latents
           A_						       =   self.vae.decode(UpperCamelCase__							).sample

           A_						       =   (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_						       =   samples.cpu().permute(0		,					2		,					3		,					1							).float().numpy()

           if output_type == "pil":
                  A_						       =   self.numpy_to_pil(UpperCamelCase__							)

           if not return_dict:
                  return (samples,)

           return ImagePipelineOutput(images=UpperCamelCase__							)



'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
