
'''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_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  ):

												"""simple docstring"""



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

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




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												if not isinstance(_lowercase       ,  _lowercase ):
																		_lowerCAmelCase    		=					list(_lowercase )

												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       ,  _lowercase       ,  _lowercase = 4.0       ,  _lowercase = None       ,  _lowercase = 50       ,  _lowercase = "pil"       ,  _lowercase = True       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					len(_lowercase )
												_lowerCAmelCase    		=					self.transformer.config.sample_size
												_lowerCAmelCase    		=					self.transformer.config.in_channels

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

												_lowerCAmelCase    		=					torch.tensor(_lowercase       ,  device=self.device ).reshape(-1 )
												_lowerCAmelCase    		=					torch.tensor([1_000] * batch_size       ,  device=self.device )
												_lowerCAmelCase    		=					torch.cat([class_labels, class_null]       ,  0 ) if guidance_scale > 1 else class_labels

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

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

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

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

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

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

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

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

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

												_lowerCAmelCase    		=					1 / self.vae.config.scaling_factor * latents
												_lowerCAmelCase    		=					self.vae.decode(_lowercase ).sample

												_lowerCAmelCase    		=					(samples / 2 + 0.5).clamp(0       ,  1 )

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

												if output_type == "pil":
																		_lowerCAmelCase    		=					self.numpy_to_pil(_lowercase )

												if not return_dict:
																		return (samples,)

												return ImagePipelineOutput(images=_lowercase )


'''simple docstring'''



import warnings

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


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												warnings.warn(
												    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use DonutImageProcessor instead."""       ,  _lowercase       ,  )
												super().__init__(*_lowercase       ,  **_lowercase )
