





"""simple docstring"""




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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


lowerCamelCase_	:		List[Any]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  ["""pixel_values"""]

      def __init__(	self   , snake_case_ = True   , snake_case_ = None   , snake_case_ = True   , snake_case_ = None   , snake_case_ = PILImageResampling.BICUBIC   , snake_case_ = True   , snake_case_ = 1 / 2_5_5   , snake_case_ = True   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(**snake_case_    )
        A_    :			List[Any]									=    crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
        A_    :			str									=    get_size_dict(snake_case_   , param_name='crop_size'    )
        A_    :			Any									=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A_    :			Any									=    get_size_dict(snake_case_    )

        A_    :			List[str]									=    do_center_crop
        A_    :			Optional[int]									=    crop_size
        A_    :			str									=    do_resize
        A_    :			str									=    size
        A_    :			List[Any]									=    resample
        A_    :			Optional[int]									=    do_rescale
        A_    :			Dict									=    rescale_factor
        A_    :			Optional[int]									=    do_normalize
        A_    :			List[Any]									=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        A_    :			Tuple									=    image_std if image_std is not None else IMAGENET_DEFAULT_STD

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.size if size is None else size
        A_    :			Optional[Any]									=    get_size_dict(snake_case_    )
        A_    :			Union[str, Any]									=    get_size_dict(snake_case_   , param_name='crop_size'    )

        A_		,			A_    :			Union[str, Any]									=    get_image_size(snake_case_    )
        A_    :			str									=    min(snake_case_   , snake_case_    )
        A_    :			Tuple									=    (size['height'] / crop_size['height']) * min_dim
        A_    :			Any									=    (size['width'] / crop_size['width']) * min_dim
        return center_crop(snake_case_   , size=(cropped_height, cropped_width)   , data_format=snake_case_   , **snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = PIL.Image.BICUBIC   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			int									=    get_size_dict(snake_case_    )
        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()}"""    )
        return resize(
            snake_case_   , size=(size['height'], size['width'])   , resample=snake_case_   , data_format=snake_case_   , **snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = ChannelDimension.FIRST   , **snake_case_   , ):

        """simple docstring"""

        A_    :			List[Any]									=    do_center_crop if do_center_crop is not None else self.do_center_crop
        A_    :			List[str]									=    crop_size if crop_size is not None else self.crop_size
        A_    :			str									=    get_size_dict(snake_case_   , param_name='crop_size'    )
        A_    :			Union[str, Any]									=    do_resize if do_resize is not None else self.do_resize
        A_    :			Any									=    size if size is not None else self.size
        A_    :			Dict									=    get_size_dict(snake_case_    )
        A_    :			int									=    resample if resample is not None else self.resample
        A_    :			Any									=    do_rescale if do_rescale is not None else self.do_rescale
        A_    :			List[str]									=    rescale_factor if rescale_factor is not None else self.rescale_factor
        A_    :			List[Any]									=    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_    :			Tuple									=    image_std if image_std is not None else self.image_std

        A_    :			Tuple									=    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_center_crop and crop_size is None:
          raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'    )

        if do_resize and size is None:
          raise ValueError('Size 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 image standard deviation must be specified if do_normalize is True.'    )

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

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

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

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

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

        A_    :			Optional[int]									=    [to_channel_dimension_format(snake_case_   , snake_case_    ) for image in images]

        A_    :			str									=    {'pixel_values': images}
        return BatchFeature(data=snake_case_   , tensor_type=snake_case_    )


import timeit


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_						):  # Initializes a matrix identical to the one provided.
            A__        =  arg
            A__        =  len(lowercase_						)
        else:  # Initializes a square matrix of the given size and set values to zero.
            A__        =  arg
            A__        =  [[0 for _ in range(self.n						)] for _ in range(self.n						)]


    def __mul__( self					:			Union[str, Any],lowercase_					:			List[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  Matrix(self.n						)
        for i in range(self.n						):
            for j in range(self.n						):
                for k in range(self.n						):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  Matrix([[1, 0], [0, 1]] )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> List[str]:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa
    A__        =  Matrix([[1, 1], [1, 0]] )
    A__        =  modular_exponentiation(SCREAMING_SNAKE_CASE__       ,     n - 2 )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Dict:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa

    A__        =  fa
    A__        =  fa
    n -= 2

    while n > 0:
        A__ ,		A__        =  fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
    A__        =  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print('With matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
    A__        =  'simple_fibonacci(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print(
        'Without matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()

