



'''simple docstring'''




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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		int		      = 1


	@register_to_config
	def __init__(self	,				lowercase = 1000	,				lowercase = None							):
				# set `betas`, `alphas`, `timesteps`
				self.set_timesteps(lowercase							)

				# standard deviation of the initial noise distribution
				A_     :						List[Any]     =					1.0

				# For now we only support F-PNDM, i.e. the runge-kutta method
				# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
				# mainly at formula (9), (12), (13) and the Algorithm 2.
				A_     :						Optional[int]     =					4

				# running values
				A_     :						int     =					[]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Dict     =					num_inference_steps
				A_     :						Optional[Any]     =					torch.linspace(1	,				0	,				num_inference_steps + 1							)[:-1]
				A_     :						Optional[Any]     =					torch.cat([steps, torch.tensor([0.0]							)]							)

				if self.config.trained_betas is not None:
							A_     :						Optional[int]     =					torch.tensor(self.config.trained_betas	,				dtype=torch.floataa							)
				else:
							A_     :						Dict     =					torch.sin(steps * math.pi / 2							) ** 2

				A_     :						int     =					(1.0 - self.betas**2) ** 0.5

				A_     :						Optional[Any]     =					(torch.atana(self.betas	,				self.alphas							) / math.pi * 2)[:-1]
				A_     :						Dict     =					timesteps.to(lowercase							)

				A_     :						Dict     =					[]


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase = True	,				):
				if self.num_inference_steps is None:
							raise ValueError(
							    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""							)

				A_     :						List[Any]     =					(self.timesteps == timestep).nonzero().item()
				A_     :						Any     =					timestep_index + 1

				A_     :						Dict     =					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
				self.ets.append(lowercase							)

				if len(self.ets							) == 1:
							A_     :						Any     =					self.ets[-1]
				elif len(self.ets							) == 2:
							A_     :						Optional[int]     =					(3 * self.ets[-1] - self.ets[-2]) / 2
				elif len(self.ets							) == 3:
							A_     :						Optional[Any]     =					(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
				else:
							A_     :						Union[str, Any]     =					(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

				A_     :						List[Any]     =					self._get_prev_sample(lowercase	,				lowercase	,				lowercase	,				lowercase							)

				if not return_dict:
							return (prev_sample,)

				return SchedulerOutput(prev_sample=lowercase							)


	def    _a     (self	,				lowercase	,				*lowercase	,				**lowercase							):
				return sample


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[Any]     =					self.alphas[timestep_index]
				A_     :						Union[str, Any]     =					self.betas[timestep_index]

				A_     :						int     =					self.alphas[prev_timestep_index]
				A_     :						Optional[Any]     =					self.betas[prev_timestep_index]

				A_     :						List[Any]     =					(sample - sigma * ets) / max(lowercase	,				1E-8							)
				A_     :						Union[str, Any]     =					next_alpha * pred + ets * next_sigma

				return prev_sample


	def __len__(self							):
				return self.config.num_train_timesteps




'''simple docstring'''




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

import numpy as np

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


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PIL.Image.BILINEAR	,				lowercase = None	,				**lowercase	,				):
				A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Any     =					get_resize_output_image_size(lowercase	,				size=size["""shortest_edge"""]	,				default_to_square=lowercase							)
				return resize(lowercase	,				size=lowercase	,				resample=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					get_size_dict(lowercase							)
				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()}'							)
				return center_crop(lowercase	,				size=(size["""height"""], size["""width"""])	,				data_format=lowercase	,				**lowercase							)


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


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							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:
							raise ValueError("""Size 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_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

				# All transformations expect numpy arrays.
				A_     :						Optional[Any]     =					[to_numpy_array(lowercase							) for image in images]

				if do_resize:
							A_     :						List[Any]     =					[self.resize(image=lowercase	,				size=lowercase	,				resample=lowercase							) for image in images]

				if do_center_crop:
							A_     :						str     =					[self.center_crop(image=lowercase	,				size=lowercase							) for image in images]

				if do_rescale:
							A_     :						Dict     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

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

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

							for idx in range(len(lowercase							)							):
										A_     :						Union[str, Any]     =					torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0							)	,				size=target_sizes[idx]	,				mode="""bilinear"""	,				align_corners=lowercase							)
										A_     :						Any     =					resized_logits[0].argmax(dim=0							)
										semantic_segmentation.append(lowercase							)
				else:
							A_     :						str     =					logits.argmax(dim=1							)
							A_     :						Optional[Any]     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation