





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


UpperCAmelCase     :     Any							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       np.ndarray	,	SCREAMING_SNAKE_CASE					:       Union[int, Iterable[int]]	,	SCREAMING_SNAKE_CASE					:       bool	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  def constraint_to_multiple_of(SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Optional[Any]=0	,	SCREAMING_SNAKE_CASE					:       Dict=None						):
    a__     : Optional[Any]  =round(val / multiple						) * multiple

    if max_val is not None and x > max_val:
      a__     : Tuple  =math.floor(val / multiple						) * multiple

    if x < min_val:
      a__     : Any  =math.ceil(val / multiple						) * multiple

    return x

  a__     : List[Any]  =(output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) else output_size

  a__     : int  =get_image_size(SCREAMING_SNAKE_CASE						)
  a__     : Union[str, Any]  =output_size

  # determine new height and width
  a__     : List[Any]  =output_height / input_height
  a__     : Tuple  =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__     : List[str]  =scale_width
    else:
      # fit height
      a__     : Optional[int]  =scale_height

  a__     : List[Any]  =constraint_to_multiple_of(scale_height * input_height	,	multiple=SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =constraint_to_multiple_of(scale_width * input_width	,	multiple=SCREAMING_SNAKE_CASE						)

  return (new_height, new_width)







class     __lowerCAmelCase	(   snake_case_):
      _lowercase    : Any            =      ["""pixel_values"""]







      def __init__(   self		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = PILImageResampling.BILINEAR		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = 1 / 2_5_5		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						None:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)
        a__     : Dict  =size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
        a__     : str  =get_size_dict(lowerCAmelCase__							)
        a__     : List[Any]  =do_resize
        a__     : List[str]  =size
        a__     : Optional[Any]  =keep_aspect_ratio
        a__     : Optional[Any]  =ensure_multiple_of
        a__     : List[Any]  =resample
        a__     : Optional[int]  =do_rescale
        a__     : Union[str, Any]  =rescale_factor
        a__     : Optional[Any]  =do_normalize
        a__     : Any  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        a__     : Any  =image_std if image_std is not None else IMAGENET_STANDARD_STD







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = PILImageResampling.BICUBIC		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



        '''simple docstring'''




        a__     : str  =get_size_dict(lowerCAmelCase__							)
        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__     : Union[str, Any]  =get_resize_output_image_size(
            lowerCAmelCase__		,				output_size=(size["height"], size["width"])		,				keep_aspect_ratio=lowerCAmelCase__		,				multiple=lowerCAmelCase__		,				)
        return resize(lowerCAmelCase__		,				size=lowerCAmelCase__		,				resample=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						List[Any]:



        '''simple docstring'''




        return rescale(lowerCAmelCase__		,				scale=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



        '''simple docstring'''




        return normalize(lowerCAmelCase__		,				mean=lowerCAmelCase__		,				std=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







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



        '''simple docstring'''




        a__     : Dict  =do_resize if do_resize is not None else self.do_resize
        a__     : List[str]  =size if size is not None else self.size
        a__     : Union[str, Any]  =get_size_dict(lowerCAmelCase__							)
        a__     : List[Any]  =keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        a__     : int  =ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        a__     : Union[str, Any]  =resample if resample is not None else self.resample
        a__     : int  =do_rescale if do_rescale is not None else self.do_rescale
        a__     : List[Any]  =rescale_factor if rescale_factor is not None else self.rescale_factor
        a__     : Optional[int]  =do_normalize if do_normalize is not None else self.do_normalize
        a__     : str  =image_mean if image_mean is not None else self.image_mean
        a__     : Any  =image_std if image_std is not None else self.image_std

        a__     : List[str]  =make_list_of_images(lowerCAmelCase__							)

        if not valid_images(lowerCAmelCase__							):
          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__     : str  =[to_numpy_array(lowerCAmelCase__							) for image in images]

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

        if do_rescale:
          a__     : Tuple  =[self.rescale(image=lowerCAmelCase__		,				scale=lowerCAmelCase__							) for image in images]

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

        a__     : Dict  =[to_channel_dimension_format(lowerCAmelCase__		,				lowerCAmelCase__							) for image in images]

        a__     : Optional[int]  ={'pixel_values': images}
        return BatchFeature(data=lowerCAmelCase__		,				tensor_type=lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						List[str]:



        '''simple docstring'''




        a__     : Any  =outputs.logits

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

          if is_torch_tensor(lowerCAmelCase__							):
            a__     : int  =target_sizes.numpy()

          a__     : Tuple  =[]

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

        return semantic_segmentation













from __future__ import annotations







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =TypeError(
            "Matrices must be formed from a list of zero or more lists containing at "
            "least one and the same number of values, each of which must be of type "
            "int or float."							)
        if len(lowerCAmelCase__							) != 0:
          a__     : List[str]  =len(rows[0]							)
          if cols == 0:
            raise error
          for row in rows:
            if len(lowerCAmelCase__							) != cols:
              raise error
            for value in row:
              if not isinstance(lowerCAmelCase__		,				(int, float)							):
                raise error
          a__     : List[Any]  =rows
        else:
          a__     : str  =[]







      def        _lowercase  (   self							)       ->						list[list[int]]:



        '''simple docstring'''




        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows[0]							)







      @property
      def        _lowercase  (   self							)       ->						tuple[int, int]:



        '''simple docstring'''




        return (self.num_rows, self.num_columns)







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return self.order[0] == self.order[1]







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : str  =[
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
            for row_num in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        if not self.is_square:
          return 0
        if self.order == (0, 0):
          return 1
        if self.order == (1, 1):
          return int(self.rows[0][0]							)
        if self.order == (2, 2):
          return int(
              (self.rows[0][0] * self.rows[1][1])
              - (self.rows[0][1] * self.rows[1][0])							)
        else:
          return sum(
              self.rows[0][column] * self.cofactors().rows[0][column]
              for column in range(self.num_columns							)							)







      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return bool(self.determinant()							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  =[
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns							)
                if other_column != column
            ]
            for other_row in range(self.num_rows							)
            if other_row != row
        ]
        return Matrix(lowerCAmelCase__							).determinant()







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        if (row + column) % 2 == 0:
          return self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)
        return -1 * self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							) for column in range(self.num_columns							)]
                for row in range(self.num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns							)
                ]
                for row in range(self.minors().num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Dict  =[
            [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
            for row in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.determinant()
        if not determinant:
          raise TypeError("Only matrices with a non-zero determinant have an inverse"							)
        return self.adjugate() * (1 / determinant)







      def __repr__(   self							)       ->						str:



        '''simple docstring'''




        return str(self.rows							)







      def __str__(   self							)       ->						str:



        '''simple docstring'''




        if self.num_rows == 0:
          return "[]"
        if self.num_rows == 1:
          return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    "[" + ". ".join([str(lowerCAmelCase__							) for value in row]							) + ".]"
                    for row in self.rows
                ]							)
            + "]"
        )







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : List[str]  =TypeError("Row must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in row:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_columns:
          raise ValueError(
              "Row must be equal in length to the other rows in the matrix"							)
        if position is None:
          self.rows.append(lowerCAmelCase__							)
        else:
          a__     : Tuple  =self.rows[0:position] + [row] + self.rows[position:]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : str  =TypeError(
            "Column must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in column:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_rows:
          raise ValueError(
              "Column must be equal in length to the other columns in the matrix"							)
        if position is None:
          a__     : Optional[Any]  =[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
        else:
          a__     : Any  =[
              self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
              for i in range(self.num_rows							)
          ]







      def __eq__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          return NotImplemented
        return self.rows == other.rows







      def __ne__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        return not self == other







      def __neg__(   self							)       ->						Matrix:



        '''simple docstring'''




        return self * -1







      def __add__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Addition requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __sub__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Subtraction requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __mul__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				(int, float)							):
          return Matrix(
              [[int(element * other							) for element in row] for row in self.rows]							)
        elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          if self.num_columns != other.num_rows:
            raise ValueError(
                "The number of columns in the first matrix must "
                "be equal to the number of rows in the second"							)
          return Matrix(
              [
                  [Matrix.dot_product(lowerCAmelCase__		,				lowerCAmelCase__							) for column in other.columns()]
                  for row in self.rows
              ]							)
        else:
          raise TypeError(
              "A Matrix can only be multiplied by an int, float, or another matrix"							)







      def __pow__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise TypeError("A Matrix can only be raised to the power of an int"							)
        if not self.is_square:
          raise ValueError("Only square matrices can be raised to a power"							)
        if other == 0:
          return self.identity()
        if other < 0:
          if self.is_invertable():
            return self.inverse() ** (-other)
          raise ValueError(
              "Only invertable matrices can be raised to a negative power"							)
        a__     : Tuple  =self
        for _ in range(other - 1							):
          result *= self
        return result







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        return sum(row[i] * column[i] for i in range(len(lowerCAmelCase__							)							)							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






