


"""simple docstring"""




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


lowercase_			     =  logging.get_logger(__name__)

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     Union[int, Iterable[int]]    ,					lowerCAmelCase__ :     bool    ,					lowerCAmelCase__ :     int			) ->      Tuple[int, int]:
		def constraint_to_multiple_of(lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     List[str]=0    ,					lowerCAmelCase__ :     List[str]=None			):
				__a           =			round(val / multiple			) * multiple

				if max_val is not None and x > max_val:
						__a           =			math.floor(val / multiple			) * multiple

				if x < min_val:
						__a           =			math.ceil(val / multiple			) * multiple

				return x

		__a           =			(output_size, output_size) if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) else output_size

		__a      ,     __a           =			get_image_size(lowerCAmelCase__			)
		__a      ,     __a           =			output_size

		# determine new height and width
		__a           =			output_height / input_height
		__a           =			output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width			) < abs(1 - scale_height			):
						# fit width
						__a           =			scale_width
				else:
						# fit height
						__a           =			scale_height

		__a           =			constraint_to_multiple_of(scale_height * input_height    ,					multiple=lowerCAmelCase__			)
		__a           =			constraint_to_multiple_of(scale_width * input_width    ,					multiple=lowerCAmelCase__			)

		return (new_height, new_width)




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Dict										=				['pixel_values']

		def __init__(  self							, _a = True							, _a = None							, _a = PILImageResampling.BILINEAR							, _a = False							, _a = 1							, _a = True							, _a = 1 / 255							, _a = True							, _a = None							, _a = None							, **_a							, ):
				super().__init__(**_a      )
				__a           =			size if size is not None else {'''height''': 384, '''width''': 384}
				__a           =			get_size_dict(_a      )
				__a           =			do_resize
				__a           =			size
				__a           =			keep_aspect_ratio
				__a           =			ensure_multiple_of
				__a           =			resample
				__a           =			do_rescale
				__a           =			rescale_factor
				__a           =			do_normalize
				__a           =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__a           =			image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = False							, _a = 1							, _a = PILImageResampling.BICUBIC							, _a = None							, **_a							, ):
				__a           =			get_size_dict(_a      )
				if "height" not in size or "width" not in size:
						raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''      )
				__a           =			get_resize_output_image_size(
				    _a							, output_size=(size['''height'''], size['''width'''])							, keep_aspect_ratio=_a							, multiple=_a							, )
				return resize(_a							, size=_a							, resample=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a							, ):
				return rescale(_a							, scale=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = None							, **_a							, ):
				return normalize(_a							, mean=_a							, std=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = None							, _a = ChannelDimension.FIRST							, **_a							, ):
				__a           =			do_resize if do_resize is not None else self.do_resize
				__a           =			size if size is not None else self.size
				__a           =			get_size_dict(_a      )
				__a           =			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
				__a           =			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
				__a           =			resample if resample is not None else self.resample
				__a           =			do_rescale if do_rescale is not None else self.do_rescale
				__a           =			rescale_factor if rescale_factor is not None else self.rescale_factor
				__a           =			do_normalize if do_normalize is not None else self.do_normalize
				__a           =			image_mean if image_mean is not None else self.image_mean
				__a           =			image_std if image_std is not None else self.image_std

				__a           =			make_list_of_images(_a      )

				if not valid_images(_a      ):
						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_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.
				__a           =			[to_numpy_array(_a      ) for image in images]

				if do_resize:
						__a           =			[self.resize(image=_a							, size=_a							, resample=_a      ) for image in images]

				if do_rescale:
						__a           =			[self.rescale(image=_a							, scale=_a      ) for image in images]

				if do_normalize:
						__a           =			[self.normalize(image=_a							, mean=_a							, std=_a      ) for image in images]

				__a           =			[to_channel_dimension_format(_a							, _a      ) for image in images]

				__a           =			{'''pixel_values''': images}
				return BatchFeature(data=_a							, tensor_type=_a      )





		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
						if len(_a      ) != len(_a      ):
								raise ValueError(
								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''      )

						if is_torch_tensor(_a      ):
								__a           =			target_sizes.numpy()

						__a           =			[]

						for idx in range(len(_a      )      ):
								__a           =			torch.nn.functional.interpolate(
								    logits[idx].unsqueeze(dim=0      )							, size=target_sizes[idx]							, mode='''bilinear'''							, align_corners=_a      )
								__a           =			resized_logits[0].argmax(dim=0      )
								semantic_segmentation.append(_a      )
				else:
						__a           =			logits.argmax(dim=1      )
						__a           =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

				return semantic_segmentation











'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





