
"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class 		a__						(      a_						):
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42


class 		a__						(      a_,			a_						):
   __lowerCAmelCase            =				1
   @register_to_config
   def __init__(			self	,    _a = 2_000	,    _a = 0.1_5	,    _a = 0.0_1	,    _a = 1_3_4_8.0	,    _a = 1E-5	,    _a = 1	,    ):
          # standard deviation of the initial noise distribution
          lowercase    :				Dict       		=							sigma_max

          # setable values
          lowercase    :				List[str]       		=							None

          self.set_sigmas(_a	,    _a	,    _a	,    _a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          return sample
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None				):
          lowercase    :				List[Any]       		=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

          lowercase    :				Tuple       		=							torch.linspace(1	,    _a	,    _a	,    device=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None				):
          lowercase    :				List[Any]       		=							sigma_min if sigma_min is not None else self.config.sigma_min
          lowercase    :				Tuple       		=							sigma_max if sigma_max is not None else self.config.sigma_max
          lowercase    :				List[str]       		=							sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
                 self.set_timesteps(_a	,    _a				)

          lowercase    :				Optional[Any]       		=							sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          lowercase    :				Union[str, Any]       		=							torch.exp(torch.linspace(math.log(_a				)	,    math.log(_a				)	,    _a				)				)
          lowercase    :				int       		=							torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)
   def 			__magic_name__			(			self	,    _a	,    _a				):
          return torch.where(
              timesteps == 0	,    torch.zeros_like(t.to(timesteps.device				)				)	,    self.discrete_sigmas[timesteps - 1].to(timesteps.device				)	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a = None	,    _a = True	,    ):
          if self.timesteps is None:
                 raise ValueError(
                     "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

          lowercase    :				Tuple       		=							timestep * torch.ones(
              sample.shape[0]	,    device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
          lowercase    :				int       		=							(timestep * (len(self.timesteps				) - 1)).long()

          # mps requires indices to be in the same device, so we use cpu as is the default with cuda
          lowercase    :				str       		=							timesteps.to(self.discrete_sigmas.device				)

          lowercase    :				int       		=							self.discrete_sigmas[timesteps].to(sample.device				)
          lowercase    :				Dict       		=							self.get_adjacent_sigma(_a	,    _a				).to(sample.device				)
          lowercase    :				Tuple       		=							torch.zeros_like(_a				)
          lowercase    :				List[str]       		=							(sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          lowercase    :				List[Any]       		=							diffusion.flatten()
          while len(diffusion.shape				) < len(sample.shape				):
                 lowercase    :				List[Any]       		=							diffusion.unsqueeze(-1				)
          lowercase    :				List[str]       		=							drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          lowercase    :				Tuple       		=							randn_tensor(
              sample.shape	,    layout=sample.layout	,    generator=_a	,    device=sample.device	,    dtype=sample.dtype				)
          lowercase    :				Union[str, Any]       		=							sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          lowercase    :				int       		=							prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
                 return (prev_sample, prev_sample_mean)

          return SdeVeOutput(prev_sample=_a	,    prev_sample_mean=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    _a = True	,    ):
          if self.timesteps is None:
                 raise ValueError(
                     "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          lowercase    :				List[Any]       		=							randn_tensor(sample.shape	,    layout=sample.layout	,    generator=_a				).to(sample.device				)

          # compute step size from the model_output, the noise, and the snr
          lowercase    :				Any       		=							torch.norm(model_output.reshape(model_output.shape[0]	,    -1				)	,    dim=-1				).mean()
          lowercase    :				List[Any]       		=							torch.norm(noise.reshape(noise.shape[0]	,    -1				)	,    dim=-1				).mean()
          lowercase    :				List[str]       		=							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
          lowercase    :				Dict       		=							step_size * torch.ones(sample.shape[0]				).to(sample.device				)
          # self.repeat_scalar(step_size, sample.shape[0])

          # compute corrected sample: model_output term and noise term
          lowercase    :				Any       		=							step_size.flatten()
          while len(step_size.shape				) < len(sample.shape				):
                 lowercase    :				List[str]       		=							step_size.unsqueeze(-1				)
          lowercase    :				List[str]       		=							sample + step_size * model_output
          lowercase    :				Optional[Any]       		=							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    ):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          lowercase    :				Union[str, Any]       		=							timesteps.to(original_samples.device				)
          lowercase    :				int       		=							self.discrete_sigmas.to(original_samples.device				)[timesteps]
          lowercase    :				Tuple       		=							(
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(_a				) * sigmas[:, None, None, None]
          )
          lowercase    :				str       		=							noise + original_samples
          return noisy_samples

   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


_A			:       List[Any]   		=       logging.get_logger(__name__)

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

          lowercase    :				int       		=							do_resize
          lowercase    :				Optional[int]       		=							size
          lowercase    :				str       		=							resample
          lowercase    :				List[Any]       		=							do_rescale
          lowercase    :				Union[str, Any]       		=							rescale_factor
          lowercase    :				Optional[int]       		=							do_center_crop
          lowercase    :				Union[str, Any]       		=							crop_size
          lowercase    :				Optional[Any]       		=							do_flip_channel_order
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PIL.Image.BILINEAR	,    _a = None	,    **_a	,    ):
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
          lowercase    :				Union[str, Any]       		=							get_resize_output_image_size(_a	,    size=size["shortest_edge"]	,    default_to_square=_a				)
          return resize(_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				str       		=							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()}"""				)
          return center_crop(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          return flip_channel_order(_a	,    data_format=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Optional[Any]       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Tuple       		=							resample if resample is not None else self.resample
          lowercase    :				List[str]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				List[Any]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				Union[str, Any]       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				Optional[int]       		=							(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          lowercase    :				str       		=							size if size is not None else self.size
          lowercase    :				Any       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				int       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Any       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							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:
                 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.
          lowercase    :				Any       		=							[to_numpy_array(_a				) for image in images]

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

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

          if do_rescale:
                 lowercase    :				Union[str, Any]       		=							[self.rescale(image=_a	,    scale=_a				) for image in images]

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

          lowercase    :				int       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

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

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[int]       		=							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				):
                        lowercase    :				Tuple       		=							target_sizes.numpy()

                 lowercase    :				List[Any]       		=							[]

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

          return semantic_segmentation


