


"""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


_lowerCamelCase  =	logging.get_logger(__name__)


class       lowerCamelCase_		(      lowerCamelCase__ ):





				"""simple docstring"""







				_lowerCAmelCase   :   Optional[Any]   				=       ["pixel_values"]



				def __init__(    self							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = PIL.Image.NEAREST							,		UpperCAmelCase__ = False							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = 1 / 255							,		UpperCAmelCase__ = False							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = True							,		**UpperCAmelCase__							,		):
								super().__init__(**__A  )
								SCREAMING_SNAKE_CASE__  				=					size if size is not None else {'''height''': 346, '''width''': 346}
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(__A  )
								SCREAMING_SNAKE_CASE__  				=					crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(__A							,		param_name="crop_size"  )

								SCREAMING_SNAKE_CASE__  				=					do_resize
								SCREAMING_SNAKE_CASE__  				=					size
								SCREAMING_SNAKE_CASE__  				=					resample
								SCREAMING_SNAKE_CASE__  				=					do_center_crop
								SCREAMING_SNAKE_CASE__  				=					crop_size
								SCREAMING_SNAKE_CASE__  				=					do_rescale
								SCREAMING_SNAKE_CASE__  				=					rescale_factor
								SCREAMING_SNAKE_CASE__  				=					rescale_offset
								SCREAMING_SNAKE_CASE__  				=					do_normalize
								SCREAMING_SNAKE_CASE__  				=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								SCREAMING_SNAKE_CASE__  				=					image_std if image_std is not None else IMAGENET_STANDARD_STD
								SCREAMING_SNAKE_CASE__  				=					include_top



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = PIL.Image.NEAREST							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(__A  )
								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(
								    __A							,		size=(size["height"], size["width"])							,		resample=__A							,		data_format=__A							,		**__A  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(__A  )
								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(__A							,		size=(size["height"], size["width"])							,		data_format=__A							,		**__A  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								if offset:
												SCREAMING_SNAKE_CASE__  				=					(image - 127.5) * scale
												if data_format is not None:
																SCREAMING_SNAKE_CASE__  				=					to_channel_dimension_format(__A							,		__A  )
												SCREAMING_SNAKE_CASE__  				=					rescaled_image.astype(np.floataa  )
								else:
												SCREAMING_SNAKE_CASE__  				=					rescale(__A							,		scale=__A							,		data_format=__A							,		**__A  )
								return rescaled_image



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								return normalize(__A							,		mean=__A							,		std=__A							,		data_format=__A							,		**__A  )






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__=None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = ChannelDimension.FIRST							,		**UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE__  				=					resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE__  				=					do_center_crop if do_center_crop is not None else self.do_center_crop
								SCREAMING_SNAKE_CASE__  				=					do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE__  				=					rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE__  				=					rescale_offset if rescale_offset is not None else self.rescale_offset
								SCREAMING_SNAKE_CASE__  				=					do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE__  				=					image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE__  				=					image_std if image_std is not None else self.image_std
								SCREAMING_SNAKE_CASE__  				=					include_top if include_top is not None else self.include_top

								SCREAMING_SNAKE_CASE__  				=					size if size is not None else self.size
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(__A  )
								SCREAMING_SNAKE_CASE__  				=					crop_size if crop_size is not None else self.crop_size
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(__A							,		param_name="crop_size"  )

								SCREAMING_SNAKE_CASE__  				=					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 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.
								SCREAMING_SNAKE_CASE__  				=					[to_numpy_array(__A  ) for image in images]

								if do_resize:
												SCREAMING_SNAKE_CASE__  				=					[self.resize(image=__A							,		size=__A							,		resample=__A  ) for image in images]

								if do_center_crop:
												SCREAMING_SNAKE_CASE__  				=					[self.center_crop(image=__A							,		size=__A  ) for image in images]

								if do_rescale:
												SCREAMING_SNAKE_CASE__  				=					[self.rescale(image=__A							,		scale=__A							,		offset=__A  ) for image in images]

								if do_normalize:
												SCREAMING_SNAKE_CASE__  				=					[self.normalize(image=__A							,		mean=__A							,		std=__A  ) for image in images]

								if include_top:
												SCREAMING_SNAKE_CASE__  				=					[self.normalize(image=__A							,		mean=[0, 0, 0]							,		std=__A  ) for image in images]

								SCREAMING_SNAKE_CASE__  				=					[to_channel_dimension_format(__A							,		__A  ) for image in images]

								SCREAMING_SNAKE_CASE__  				=					{'''pixel_values''': images}
								return BatchFeature(data=__A							,		tensor_type=__A  )



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()





