





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCAmelCase 				=     logging.get_logger(__name__)


if is_vision_available():
      import PIL

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							['''pixel_values''']
      def __init__(		self      ,       a__ = True      ,       a__ = None      ,       a__ = PILImageResampling.BICUBIC      ,       a__ = True      ,       a__ = None      ,       a__ = True      ,       a__ = 1 / 255      ,       a__ = True      ,       a__ = None      ,       a__ = None      ,       a__ = True      ,       **a__      ,       ):
         super().__init__(**a__       )
         A_     :				int			      =	size if size is not None else {"""shortest_edge""": 224}
         A_     :				str			      =	get_size_dict(a__      ,       default_to_square=a__       )
         A_     :				str			      =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         A_     :				Optional[int]			      =	get_size_dict(a__      ,       default_to_square=a__      ,       param_name="""crop_size"""       )

         A_     :				Optional[int]			      =	do_resize
         A_     :				Any			      =	size
         A_     :				Optional[Any]			      =	resample
         A_     :				int			      =	do_center_crop
         A_     :				Optional[int]			      =	crop_size
         A_     :				Dict			      =	do_rescale
         A_     :				List[Any]			      =	rescale_factor
         A_     :				str			      =	do_normalize
         A_     :				int			      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         A_     :				List[str]			      =	image_std if image_std is not None else OPENAI_CLIP_STD
         A_     :				Dict			      =	do_convert_rgb
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = PILImageResampling.BICUBIC      ,       a__ = None      ,       **a__      ,       ):
         A_     :				Any			      =	get_size_dict(a__      ,       default_to_square=a__       )
         if "shortest_edge" not in size:
            raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
         A_     :				int			      =	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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = None      ,       **a__      ,       ):
         A_     :				Tuple			      =	get_size_dict(a__       )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
         return center_crop(a__      ,       size=(size["""height"""], size["""width"""])      ,       data_format=a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = None      ,       **a__      ,       ):
         return rescale(a__      ,       scale=a__      ,       data_format=a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__ = None      ,       **a__      ,       ):
         return normalize(a__      ,       mean=a__      ,       std=a__      ,       data_format=a__      ,       **a__       )






      def 		_lowerCamelCase      (		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__ = None      ,       a__ = ChannelDimension.FIRST      ,       **a__      ,       ):
         A_     :				Optional[int]			      =	do_resize if do_resize is not None else self.do_resize
         A_     :				str			      =	size if size is not None else self.size
         A_     :				Union[str, Any]			      =	get_size_dict(a__      ,       param_name="""size"""      ,       default_to_square=a__       )
         A_     :				List[Any]			      =	resample if resample is not None else self.resample
         A_     :				Any			      =	do_center_crop if do_center_crop is not None else self.do_center_crop
         A_     :				str			      =	crop_size if crop_size is not None else self.crop_size
         A_     :				str			      =	get_size_dict(a__      ,       param_name="""crop_size"""      ,       default_to_square=a__       )
         A_     :				int			      =	do_rescale if do_rescale is not None else self.do_rescale
         A_     :				Optional[Any]			      =	rescale_factor if rescale_factor is not None else self.rescale_factor
         A_     :				Dict			      =	do_normalize if do_normalize is not None else self.do_normalize
         A_     :				int			      =	image_mean if image_mean is not None else self.image_mean
         A_     :				str			      =	image_std if image_std is not None else self.image_std
         A_     :				str			      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         A_     :				Any			      =	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_center_crop and crop_size is None:
            raise ValueError("""Crop size must be specified if do_center_crop 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."""       )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
            A_     :				Union[str, Any]			      =	[convert_to_rgb(a__       ) for image in images]

         # All transformations expect numpy arrays.
         A_     :				Tuple			      =	[to_numpy_array(a__       ) for image in images]

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

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

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

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

         A_     :				Union[str, Any]			      =	[to_channel_dimension_format(a__      ,       a__       ) for image in images]

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













from __future__ import annotations

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	coefficient_matrix.shape
   A_      ,				A_     :				Tuple			      =	constant_matrix.shape

   if rowsa != colsa:
      A_     :				int			      =	f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if colsa != 1:
      A_     :				List[Any]			      =	f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
      raise ValueError(_lowerCAmelCase     )

   if rowsa != rowsa:
      A_     :				str			      =	(
          """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:
      A_     :				Any			      =	(
          """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"""     )

   A_     :				NDArray[floataa]			      =	np.concatenate(
       (coefficient_matrix, constant_matrix)  ,axis=1     )

   A_      ,				A_     :				str			      =	table.shape

   strictly_diagonally_dominant(_lowerCAmelCase     )

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

   return [float(_lowerCAmelCase     ) for i in new_val]



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_      ,				A_     :				str			      =	table.shape

   A_     :				Any			      =	True

   for i in range(0  ,_lowerCAmelCase     ):
      A_     :				Optional[Any]			      =	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()






