
from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__          =logging.get_logger(__name__)



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				["pixel_values"]
   def __init__(self			: Optional[int] ,    snake_case_			: bool = True ,    snake_case_			: int = 3_2 ,    snake_case_			: List[str]=PILImageResampling.BILINEAR ,    snake_case_			: bool = True ,    **snake_case_			: Optional[Any] ,    ):
    __a			:  List[str]		    =  do_resize
    __a			:  int		    =  do_rescale
    __a			:  List[Any]		    =  size_divisor
    __a			:  List[Any]		    =  resample
    super().__init__(**snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: np.ndarray ,    snake_case_			: int ,    snake_case_			: str ,    snake_case_			: Optional[ChannelDimension] = None ,    **snake_case_			: Any       ):
    __a    ,	__a			:  int		    =  get_image_size(snake_case_       )
    # Rounds the height and width down to the closest multiple of size_divisor
    __a			:  List[str]		    =  height // size_divisor * size_divisor
    __a			:  Optional[Any]		    =  width // size_divisor * size_divisor
    __a			:  Dict		    =  resize(snake_case_ ,    (new_h, new_w) ,    resample=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
    return image
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: np.ndarray ,    snake_case_			: float ,    snake_case_			: Optional[ChannelDimension] = None ,    **snake_case_			: int       ):
    return rescale(image=snake_case_ ,    scale=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )







   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,    snake_case_			: Optional[bool] = None ,    snake_case_			: Optional[int] = None ,    snake_case_			: Union[str, Any]=None ,    snake_case_			: Optional[bool] = None ,    snake_case_			: Optional[Union[TensorType, str]] = None ,    snake_case_			: ChannelDimension = ChannelDimension.FIRST ,    **snake_case_			: Optional[int] ,    ):
    __a			:  int		    =  do_resize if do_resize is not None else self.do_resize
    __a			:  Dict		    =  do_rescale if do_rescale is not None else self.do_rescale
    __a			:  List[Any]		    =  size_divisor if size_divisor is not None else self.size_divisor
    __a			:  str		    =  resample if resample is not None else self.resample

    if do_resize and size_divisor is None:
     raise ValueError('''size_divisor is required for resizing'''       )

    __a			:  Optional[Any]		    =  make_list_of_images(snake_case_       )

    if not valid_images(snake_case_       ):
     raise ValueError('''Invalid image(s)'''       )

    # All transformations expect numpy arrays.
    __a			:  Optional[Any]		    =  [to_numpy_array(snake_case_       ) for img in images]

    if do_resize:
     __a			:  Optional[Any]		    =  [self.resize(snake_case_ ,    size_divisor=snake_case_ ,    resample=snake_case_       ) for image in images]

    if do_rescale:
     __a			:  Any		    =  [self.rescale(snake_case_ ,    scale=1 / 2_5_5       ) for image in images]

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

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


def        __UpperCamelCase					(    lowerCAmelCase__							:    list[list[int | float]]  ):
 __a			:  int		    =  len(lowerCAmelCase__  )
 __a			:  Dict		    =  len(matrix[0]  )
 __a			:  Union[str, Any]		    =  min(lowerCAmelCase__  ,       lowerCAmelCase__  )

 for row in range(lowerCAmelCase__  ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  matrix[col][row] / matrix[row][row]
    for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __a			:  Optional[int]		    =  True
   for i in range(row + 1  ,       lowerCAmelCase__  ):
    if matrix[i][row] != 0:
     __a    ,	__a			:  Any		    =  matrix[i], matrix[row]
     __a			:  Union[str, Any]		    =  False
     break
   if reduce:
    rank -= 1
    for i in range(lowerCAmelCase__  ):
     __a			:  Optional[Any]		    =  matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()
