

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__  :      Union[str, Any]			=    logging.get_logger(__name__)



class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		*_A						,		**_A):
      warnings.warn(
          'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DeiTImageProcessor instead.'						,		_A						,		)
      super().__init__(*_A						,		**_A)





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       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

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

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						list(_A)

      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						,		_A						,		_A = 4.0						,		_A = None						,		_A = 50						,		_A = "pil"						,		_A = True						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.sample_size
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.in_channels

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

      SCREAMING_SNAKE_CASE_									=						torch.tensor(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * batch_size						,		device=self.device)
      SCREAMING_SNAKE_CASE_									=						torch.cat([class_labels, class_null]						,		0) if guidance_scale > 1 else class_labels

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

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

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

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

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

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

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

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

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

      SCREAMING_SNAKE_CASE_									=						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

      SCREAMING_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
      SCREAMING_SNAKE_CASE_									=						samples.cpu().permute(0						,		2						,		3						,		1).float().numpy()

      if output_type == "pil":
          SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)


