






"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


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


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= ["pixel_values"]




						def __init__(       self	,  snake_case__ = True	,  snake_case__ = None	,  snake_case__ = 0.9	,  snake_case__ = PILImageResampling.BICUBIC	,  snake_case__ = True	,  snake_case__ = None	,  snake_case__ = 1 / 255	,  snake_case__ = True	,  snake_case__ = True	,  snake_case__ = None	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												super().__init__(**snake_case__      )
												lowercase__    :				Optional[Any]=	size if size is not None else {"shortest_edge": 224}
												lowercase__    :				Union[str, Any]=	get_size_dict(snake_case__	,  default_to_square=snake_case__      )
												lowercase__    :				Optional[Any]=	crop_size if crop_size is not None else {"height": 224, "width": 224}
												lowercase__    :				int=	get_size_dict(snake_case__	,  param_name="crop_size"      )

												lowercase__    :				Union[str, Any]=	do_resize
												lowercase__    :				str=	size
												lowercase__    :				List[Any]=	crop_pct
												lowercase__    :				List[Any]=	resample
												lowercase__    :				Optional[int]=	do_center_crop
												lowercase__    :				Dict=	crop_size
												lowercase__    :				List[Any]=	do_rescale
												lowercase__    :				List[str]=	rescale_factor
												lowercase__    :				int=	do_normalize
												lowercase__    :				Union[str, Any]=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												lowercase__    :				Dict=	image_std if image_std is not None else IMAGENET_DEFAULT_STD




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  snake_case__ = PILImageResampling.BICUBIC	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Any=	get_size_dict(snake_case__	,  default_to_square=snake_case__      )
												if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																		raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''      )
												if crop_pct is not None:
																		if "shortest_edge" in size:
																								lowercase__    :				str=	int(size["shortest_edge"] / crop_pct      )
																		elif "height" in size and "width" in size:
																								if size["height"] == size["width"]:
																														lowercase__    :				List[str]=	int(size["height"] / crop_pct      )
																								else:
																														lowercase__    :				int=	(int(size["height"] / crop_pct      ), int(size["width"] / crop_pct      ))
																		else:
																								raise ValueError("Invalid size for resize: {}".format(snake_case__      )      )

																		lowercase__    :				List[Any]=	get_resize_output_image_size(snake_case__	,  size=snake_case__	,  default_to_square=snake_case__      )
												else:
																		if "shortest_edge" in size:
																								lowercase__    :				List[str]=	get_resize_output_image_size(snake_case__	,  size=size["shortest_edge"]	,  default_to_square=snake_case__      )
																		elif "height" in size and "width" in size:
																								lowercase__    :				Optional[Any]=	(size["height"], size["width"])
																		else:
																								raise ValueError("Invalid size for resize: {}".format(snake_case__      )      )

												return resize(snake_case__	,  size=snake_case__	,  resample=snake_case__	,  data_format=snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Dict=	get_size_dict(snake_case__      )
												if "height" not in size or "width" not in size:
																		raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''      )
												return center_crop(snake_case__	,  size=(size["height"], size["width"])	,  data_format=snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												return rescale(snake_case__	,  scale=snake_case__	,  data_format=snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												return normalize(snake_case__	,  mean=snake_case__	,  std=snake_case__	,  data_format=snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = ChannelDimension.FIRST	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	do_resize if do_resize is not None else self.do_resize
												lowercase__    :				Optional[int]=	crop_pct if crop_pct is not None else self.crop_pct
												lowercase__    :				Union[str, Any]=	resample if resample is not None else self.resample
												lowercase__    :				str=	do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase__    :				List[Any]=	do_rescale if do_rescale is not None else self.do_rescale
												lowercase__    :				Optional[int]=	rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase__    :				int=	do_normalize if do_normalize is not None else self.do_normalize
												lowercase__    :				Dict=	image_mean if image_mean is not None else self.image_mean
												lowercase__    :				Optional[Any]=	image_std if image_std is not None else self.image_std

												lowercase__    :				List[Any]=	size if size is not None else self.size
												lowercase__    :				int=	get_size_dict(snake_case__	,  default_to_square=snake_case__      )
												lowercase__    :				List[str]=	crop_size if crop_size is not None else self.crop_size
												lowercase__    :				Optional[int]=	get_size_dict(snake_case__	,  param_name="crop_size"      )

												lowercase__    :				Union[str, Any]=	make_list_of_images(snake_case__      )

												if not valid_images(snake_case__      ):
																		raise ValueError(
																		    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																		    "torch.Tensor, tf.Tensor or jax.ndarray."      )

												if do_resize and size is None or resample is None:
																		raise ValueError("Size and resample must be specified if do_resize is True."      )

												if do_center_crop and crop_pct is None:
																		raise ValueError("Crop_pct must be specified if do_center_crop is True."      )

												if do_rescale and rescale_factor is None:
																		raise ValueError("Rescale factor must be specified if do_rescale is True."      )

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError("Image mean and std must be specified if do_normalize is True."      )

												# All transformations expect numpy arrays.
												lowercase__    :				Union[str, Any]=	[to_numpy_array(snake_case__      ) for image in images]

												if do_resize:
																		lowercase__    :				Any=	[self.resize(image=snake_case__	,  size=snake_case__	,  crop_pct=snake_case__	,  resample=snake_case__      ) for image in images]

												if do_center_crop:
																		lowercase__    :				List[str]=	[self.center_crop(image=snake_case__	,  size=snake_case__      ) for image in images]

												if do_rescale:
																		lowercase__    :				Tuple=	[self.rescale(image=snake_case__	,  scale=snake_case__      ) for image in images]

												if do_normalize:
																		lowercase__    :				List[str]=	[self.normalize(image=snake_case__	,  mean=snake_case__	,  std=snake_case__      ) for image in images]

												lowercase__    :				Optional[Any]=	[to_channel_dimension_format(snake_case__	,  snake_case__      ) for image in images]

												lowercase__    :				Optional[Any]=	{"pixel_values": images}
												return BatchFeature(data=snake_case__	,  tensor_type=snake_case__      )














"""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	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  ):
												'''simple docstring'''
												super().__init__()
												self.register_modules(transformer=snake_case__	,  vae=snake_case__	,  scheduler=snake_case__      )

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




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''

												if not isinstance(snake_case__	,  snake_case__      ):
																		lowercase__    :				List[Any]=	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	,  snake_case__	,  snake_case__ = 4.0	,  snake_case__ = None	,  snake_case__ = 50	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	len(snake_case__      )
												lowercase__    :				Optional[int]=	self.transformer.config.sample_size
												lowercase__    :				List[str]=	self.transformer.config.in_channels

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

												lowercase__    :				Tuple=	torch.tensor(snake_case__	,  device=self.device      ).reshape(-1      )
												lowercase__    :				Any=	torch.tensor([1000] * batch_size	,  device=self.device      )
												lowercase__    :				Tuple=	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:
																								lowercase__    :				List[str]=	latent_model_input[: len(snake_case__      ) // 2]
																								lowercase__    :				int=	torch.cat([half, half]	,  dim=0      )
																		lowercase__    :				Union[str, Any]=	self.scheduler.scale_model_input(snake_case__	,  snake_case__      )

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

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

																								lowercase__    :				str=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																								lowercase__    :				Dict=	torch.cat([half_eps, half_eps]	,  dim=0      )

																								lowercase__    :				Optional[int]=	torch.cat([eps, rest]	,  dim=1      )

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

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

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

												lowercase__    :				Dict=	1 / self.vae.config.scaling_factor * latents
												lowercase__    :				Any=	self.vae.decode(snake_case__      ).sample

												lowercase__    :				Tuple=	(samples / 2 + 0.5).clamp(0	,  1      )

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

												if output_type == "pil":
																		lowercase__    :				Optional[Any]=	self.numpy_to_pil(snake_case__      )

												if not return_dict:
																		return (samples,)

												return ImagePipelineOutput(images=snake_case__      )






