

"""simple docstring"""





from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def   _A  (  UpperCamelCase_     :       NDArray[floataa],			UpperCamelCase_     :       NDArray[floataa],			UpperCamelCase_     :       list[int],			UpperCamelCase_     :       int,			)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						coefficient_matrix.shape
   __lowercase								=						constant_matrix.shape

   if rowsa != colsa:
      __lowercase								=						F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
      raise ValueError(lowerCAmelCase__)

   if colsa != 1:
      __lowercase								=						F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
      raise ValueError(lowerCAmelCase__)

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

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

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

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

   __lowercase								=						table.shape

   strictly_diagonally_dominant(lowerCAmelCase__)

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

   return [float(lowerCAmelCase__) for i in new_val]




def   _A  (  UpperCamelCase_     :       NDArray[floataa])	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						table.shape

   __lowercase								=						True

   for i in range(0,			lowerCAmelCase__):
      __lowercase								=						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()






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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowercase__       =logging.get_logger(__name__)



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      ["pixel_values"]
   def __init__(self :					str					,				snake_case_ :					bool = True					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = PILImageResampling.BILINEAR					,				snake_case_ :					bool = True					,				snake_case_ :					Union[int, float] = 1 / 2_5_5					,				snake_case_ :					bool = True					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				**snake_case_ :					Tuple					,				):
    super().__init__(**snake_case_   )
    __a :  Optional[Any]						=  size if size is not None else {'''shortest_edge''': 3_8_4}
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  Tuple						=  do_resize
    __a :  List[str]						=  size
    # Default value set here for backwards compatibility where the value in config is None
    __a :  List[str]						=  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
    __a :  Tuple						=  resample
    __a :  Any						=  do_rescale
    __a :  Optional[Any]						=  rescale_factor
    __a :  int						=  do_normalize
    __a :  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __a :  Union[str, Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					np.ndarray					,				snake_case_ :					Dict[str, int]					,				snake_case_ :					float					,				snake_case_ :					PILImageResampling = PILImageResampling.BICUBIC					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					str					,				):
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )
    if "shortest_edge" not in size:
     raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"   )
    __a :  Any						=  size['''shortest_edge''']

    if shortest_edge < 3_8_4:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     __a :  Union[str, Any]						=  int(shortest_edge / crop_pct   )
     __a :  Tuple						=  get_resize_output_image_size(snake_case_					,				size=snake_case_					,				default_to_square=snake_case_   )
     __a :  List[str]						=  resize(image=snake_case_					,				size=snake_case_					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=snake_case_					,				size=(shortest_edge, shortest_edge)					,				data_format=snake_case_					,				**snake_case_   )
    else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         snake_case_					,				size=(shortest_edge, shortest_edge)					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[int, float]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return rescale(snake_case_					,				scale=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return normalize(snake_case_					,				mean=snake_case_					,				std=snake_case_					,				data_format=snake_case_					,				**snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					ImageInput					,				snake_case_ :					bool = None					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = None					,				snake_case_ :					bool = None					,				snake_case_ :					float = None					,				snake_case_ :					bool = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[str, TensorType]] = None					,				snake_case_ :					ChannelDimension = ChannelDimension.FIRST					,				**snake_case_ :					Dict					,				):
    __a :  List[Any]						=  do_resize if do_resize is not None else self.do_resize
    __a :  Dict						=  crop_pct if crop_pct is not None else self.crop_pct
    __a :  Tuple						=  resample if resample is not None else self.resample
    __a :  Optional[int]						=  do_rescale if do_rescale is not None else self.do_rescale
    __a :  Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a :  int						=  do_normalize if do_normalize is not None else self.do_normalize
    __a :  Optional[int]						=  image_mean if image_mean is not None else self.image_mean
    __a :  Optional[int]						=  image_std if image_std is not None else self.image_std

    __a :  Dict						=  size if size is not None else self.size
    __a :  List[str]						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  List[str]						=  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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
     raise ValueError('''crop_pct must be specified if size < 384.'''   )

    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 :  List[Any]						=  [to_numpy_array(snake_case_   ) for image in images]

    if do_resize:
     __a :  List[str]						=  [self.resize(image=snake_case_					,				size=snake_case_					,				crop_pct=snake_case_					,				resample=snake_case_   ) for image in images]

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

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

    __a :  str						=  [to_channel_dimension_format(snake_case_					,				snake_case_   ) for image in images]

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