

'''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


__lowerCamelCase						    =      logging.get_logger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Tuple[int, int]:
       def constraint_to_multiple_of(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__=0,    UpperCAmelCase__=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(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE			) else output_size

       A_		   = get_image_size(__SCREAMING_SNAKE_CASE			)
       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=__SCREAMING_SNAKE_CASE			)
       A_		   = constraint_to_multiple_of(scale_width * input_width,    multiple=__SCREAMING_SNAKE_CASE			)

       return (new_height, new_width)







class 						A__ ( __snake_case     ):
    lowercase		   =						["""pixel_values"""]



    def __init__(   self						,			UpperCamelCase__ = True						,			UpperCamelCase__ = None						,			UpperCamelCase__ = PILImageResampling.BILINEAR						,			UpperCamelCase__ = False						,			UpperCamelCase__ = 1						,			UpperCamelCase__ = True						,			UpperCamelCase__ = 1 / 255						,			UpperCamelCase__ = True						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> None:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__    )
           A_		   = size if size is not None else {"height": 384, "width": 384}
           A_		   = get_size_dict(UpperCamelCase__    )
           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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = False						,			UpperCamelCase__ = 1						,			UpperCamelCase__ = PILImageResampling.BICUBIC						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> np.ndarray:





           '''simple docstring'''
           A_		   = get_size_dict(UpperCamelCase__    )
           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(
               UpperCamelCase__						,			output_size=(size["""height"""], size["""width"""])						,			keep_aspect_ratio=UpperCamelCase__						,			multiple=UpperCamelCase__						,			)
           return resize(UpperCamelCase__						,			size=UpperCamelCase__						,			resample=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> int:





           '''simple docstring'''
           return rescale(UpperCamelCase__						,			scale=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> np.ndarray:





           '''simple docstring'''
           return normalize(UpperCamelCase__						,			mean=UpperCamelCase__						,			std=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = ChannelDimension.FIRST						,			**UpperCamelCase__						,			)  -> PIL.Image.Image:





           '''simple docstring'''
           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(UpperCamelCase__    )
           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(UpperCamelCase__    )

           if not valid_images(UpperCamelCase__    ):
                  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(UpperCamelCase__    ) for image in images]

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

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

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

           A_		   = [to_channel_dimension_format(UpperCamelCase__						,			UpperCamelCase__    ) for image in images]

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







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = outputs.logits

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

                  if is_torch_tensor(UpperCamelCase__    ):
                         A_		   = target_sizes.numpy()

                  A_		   = []

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

           return semantic_segmentation



'''simple docstring'''







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    )     ->     list[float]:
       A_   ,     A_		   = coefficient_matrix.shape
       A_   ,     A_		   = constant_matrix.shape

       if rowsa != colsa:
              A_		   = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if colsa != 1:
              A_		   = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if rowsa != rowsa:
              A_		   = (
                  """Coefficient and constant matrices dimensions must be nxn and nx1 but """
                  F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if len(UpperCAmelCase__			) != rowsa:
              A_		   = (
                  """Number of initial values must be equal to number of rows in coefficient """
                  F'''matrix but received {len(UpperCAmelCase__			)} and {rowsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if iterations <= 0:
              raise ValueError("""Iterations must be at least 1"""			)

       A_		   = np.concatenate(
           (coefficient_matrix, constant_matrix),    axis=1			)

       A_   ,     A_		   = table.shape

       strictly_diagonally_dominant(UpperCAmelCase__			)

       # Iterates the whole matrix for given number of times
       for _ in range(UpperCAmelCase__			):
              A_		   = []
              for row in range(UpperCAmelCase__			):
                     A_		   = 0
                     for col in range(UpperCAmelCase__			):
                            if col == row:
                                   A_		   = table[row][col]
                            elif col == cols - 1:
                                   A_		   = table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     A_		   = (temp + val) / denom
                     new_val.append(UpperCAmelCase__			)
              A_		   = new_val

       return [float(UpperCAmelCase__			) for i in new_val]
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     bool:
       A_   ,     A_		   = table.shape

       A_		   = True

       for i in range(0,    UpperCAmelCase__			):
              A_		   = 0
              for j in range(0,    cols - 1			):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""			)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()
