
"""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_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


_a   :      Any       =				logging.get_logger(__name__)






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       ["""pixel_values"""]
    def __init__(     self    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = False    ,     snake_case_ = None    ,     snake_case_ = 1 / 2_55    ,     snake_case_ = False    ,     snake_case_ = True    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = True    ,     **snake_case_    ,     ):
      super().__init__(**snake_case_  )
      _snake_case  :    Tuple            = size if size is not None else {"height": 3_46, "width": 3_46}
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    List[str]            = crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
      _snake_case  :    List[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    Any            = do_resize
      _snake_case  :    Optional[int]            = size
      _snake_case  :    List[str]            = resample
      _snake_case  :    Union[str, Any]            = do_center_crop
      _snake_case  :    str            = crop_size
      _snake_case  :    str            = do_rescale
      _snake_case  :    Tuple            = rescale_factor
      _snake_case  :    Optional[Any]            = rescale_offset
      _snake_case  :    str            = do_normalize
      _snake_case  :    Dict            = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _snake_case  :    Optional[Any]            = image_std if image_std is not None else IMAGENET_STANDARD_STD
      _snake_case  :    Dict            = include_top
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have 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_    ,     ):
      _snake_case  :    int            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return center_crop(snake_case_    ,     size=(size["height"], size["width"])    ,     data_format=snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = True    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      if offset:
        _snake_case  :    Optional[int]            = (image - 127.5) * scale
        if data_format is not None:
          _snake_case  :    Union[str, Any]            = to_channel_dimension_format(snake_case_    ,     snake_case_  )
        _snake_case  :    Tuple            = rescaled_image.astype(np.floataa  )
      else:
        _snake_case  :    Optional[int]            = rescale(snake_case_    ,     scale=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
      return rescaled_image
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      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_ = None    ,     snake_case_ = None    ,     snake_case_ = ChannelDimension.FIRST    ,     **snake_case_    ,     ):
      _snake_case  :    Dict            = do_resize if do_resize is not None else self.do_resize
      _snake_case  :    Tuple            = resample if resample is not None else self.resample
      _snake_case  :    Any            = do_center_crop if do_center_crop is not None else self.do_center_crop
      _snake_case  :    str            = do_rescale if do_rescale is not None else self.do_rescale
      _snake_case  :    int            = rescale_factor if rescale_factor is not None else self.rescale_factor
      _snake_case  :    Any            = rescale_offset if rescale_offset is not None else self.rescale_offset
      _snake_case  :    Tuple            = do_normalize if do_normalize is not None else self.do_normalize
      _snake_case  :    str            = image_mean if image_mean is not None else self.image_mean
      _snake_case  :    Any            = image_std if image_std is not None else self.image_std
      _snake_case  :    Any            = include_top if include_top is not None else self.include_top

      _snake_case  :    List[str]            = size if size is not None else self.size
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    str            = crop_size if crop_size is not None else self.crop_size
      _snake_case  :    Optional[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    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_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."  )

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

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

      if do_center_crop:
        _snake_case  :    Optional[Any]            = [self.center_crop(image=snake_case_    ,     size=snake_case_  ) for image in images]

      if do_rescale:
        _snake_case  :    Tuple            = [self.rescale(image=snake_case_    ,     scale=snake_case_    ,     offset=snake_case_  ) for image in images]

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

      if include_top:
        _snake_case  :    Optional[int]            = [self.normalize(image=snake_case_    ,     mean=[0, 0, 0]    ,     std=snake_case_  ) for image in images]

      _snake_case  :    Union[str, Any]            = [to_channel_dimension_format(snake_case_    ,     snake_case_  ) for image in images]

      _snake_case  :    Any            = {"pixel_values": images}
      return BatchFeature(data=snake_case_    ,     tensor_type=snake_case_  )




import timeit





class    __UpperCAmelCase	:



   def __init__(				self				:	Dict,						__A				:	Union[str, Any]		):
     if isinstance(__A,						__A		):  # Initializes a matrix identical to the one provided.
       UpperCAmelCase			:				str										=					arg
       UpperCAmelCase			:				Optional[Any]										=					len(__A		)
     else:  # Initializes a square matrix of the given size and set values to zero.
       UpperCAmelCase			:				List[str]										=					arg
       UpperCAmelCase			:				List[Any]										=					[[0 for _ in range(self.n		)] for _ in range(self.n		)]





   def __mul__(				self				:	Dict,						__A				:	List[Any]		):
     UpperCAmelCase			:				Dict										=					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      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase			:				Tuple										=					Matrix([[1, 0], [0, 1]]     )
  while b > 0:
    if b & 1:
      matrix *= a
    a *= a
    b >>= 1
  return matrix

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		str:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa
  UpperCAmelCase			:				List[str]										=					Matrix([[1, 1], [1, 0]]     )
  UpperCAmelCase			:				Union[str, Any]										=					modular_exponentiation(UpperCAmelCase							,					n - 2     )
  return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[Any]     )    ->		Optional[Any]:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa

  UpperCAmelCase			:				str										=					fa
  UpperCAmelCase			:				List[str]										=					fa
  n -= 2

  while n > 0:
    UpperCAmelCase					,		UpperCAmelCase			:				str										=					fn_a + fn_a, fn_a
    n -= 1

  return fn_a

def      a__	(						)    ->		Any:
  UpperCAmelCase			:				int										=					'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
  UpperCAmelCase			:				Dict										=					'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				Tuple										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print('''With matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		int:
  UpperCAmelCase			:				str										=					'''
from random import randint
from __main__ import simple_fibonacci
'''
  UpperCAmelCase			:				List[str]										=					'''simple_fibonacci(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				List[str]										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print(
      '''Without matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		List[str]:
  matrix_exponentiation_time()
  simple_fibonacci_time()


if __name__ == "__main__":
     main()





