



"""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 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A       ,	A       ,	A = None       ,	)							->					Any:
         super().__init__()
         self.register_modules(transformer=A       ,	vae=A       ,	scheduler=A		)

         # create a imagenet -> id dictionary for easier use
         _UpperCAmelCase							:  List[Any]											=    {}
         if idalabel is not None:
             for key, value in idalabel.items():
                 for label in value.split(''','''		):
                     _UpperCAmelCase							:  Tuple											=    int(A		)
             _UpperCAmelCase							:  Union[str, Any]											=    dict(sorted(self.labels.items()		)		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					List[int]:

         if not isinstance(A       ,	A		):
             _UpperCAmelCase							:  str											=    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 = 5_0       ,	A = "pil"       ,	A = True       ,	)							->					Union[ImagePipelineOutput, Tuple]:
         _UpperCAmelCase							:  Optional[int]											=    len(A		)
         _UpperCAmelCase							:  Dict											=    self.transformer.config.sample_size
         _UpperCAmelCase							:  List[str]											=    self.transformer.config.in_channels

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

         _UpperCAmelCase							:  List[str]											=    torch.tensor(A       ,	device=self.device		).reshape(-1		)
         _UpperCAmelCase							:  Tuple											=    torch.tensor([1_0_0_0] * batch_size       ,	device=self.device		)
         _UpperCAmelCase							:  List[Any]											=    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:
                 _UpperCAmelCase							:  Tuple											=    latent_model_input[: len(A		) // 2]
                 _UpperCAmelCase							:  int											=    torch.cat([half, half]       ,	dim=0		)
             _UpperCAmelCase							:  List[Any]											=    self.scheduler.scale_model_input(A       ,	A		)

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

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

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

                 _UpperCAmelCase							:  Optional[Any]											=    torch.cat([eps, rest]       ,	dim=1		)

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                 _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[int]											=    torch.split(A       ,	A       ,	dim=1		)
             else:
                 _UpperCAmelCase							:  List[str]											=    noise_pred

             # compute previous image: x_t -> x_t-1
             _UpperCAmelCase							:  List[Any]											=    self.scheduler.step(A       ,	A       ,	A		).prev_sample

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

         _UpperCAmelCase							:  int											=    1 / self.vae.config.scaling_factor * latents
         _UpperCAmelCase							:  List[Any]											=    self.vae.decode(A		).sample

         _UpperCAmelCase							:  Optional[int]											=    (samples / 2 + 0.5).clamp(0       ,	1		)

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

         if output_type == "pil":
             _UpperCAmelCase							:  List[Any]											=    self.numpy_to_pil(A		)

         if not return_dict:
             return (samples,)

         return ImagePipelineOutput(images=A		)







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


