





import timeit



class 				_UpperCamelCase       :

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[Any]:




              """simple docstring"""


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



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




              """simple docstring"""


              UpperCamelCase_ 							=	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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	Matrix([[1, 0], [0, 1]]	)
       while b > 0:
              if b & 1:
                     matrix *= a
              a *= a
              b >>= 1
       return matrix




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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[Any]:
       # Trivial Cases
       if n == 1:
              return fa
       elif n == 2:
              return fa

       UpperCamelCase_ 							=	fa
       UpperCamelCase_ 							=	fa
       n -= 2

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

       return fn_a




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




def 			lowerCAmelCase_			(				)  ->		Tuple:
       UpperCamelCase_ 							=	"\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
       UpperCamelCase_ 							=	"simple_fibonacci(randint(1,70000), 1, 1)"
       UpperCamelCase_ 							=	timeit.timeit(setup=UpperCamelCase_       ,		stmt=UpperCamelCase_       ,		number=100	)
       print(
           "Without matrix exponentiation the average execution time is "       ,		exec_time / 100	)
       return exec_time




def 			lowerCAmelCase_			(				)  ->		List[Any]:
       matrix_exponentiation_time()
       simple_fibonacci_time()


if __name__ == "__main__":
     main()






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 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, logging


UpperCamelCase_									=   logging.get_logger(__name__)







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






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


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Dict[str, int]] = None  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[int, float] = 1 / 255  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,**lowerCamelCase_:     Dict  ,)	->      None:
       super().__init__(**lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					size if size is not None else {"""height""": 224, """width""": 224}
       UpperCAmelCase_     :       str  		=					get_size_dict(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					do_resize
       UpperCAmelCase_     :       Optional[Any]  		=					do_rescale
       UpperCAmelCase_     :       Optional[Any]  		=					do_normalize
       UpperCAmelCase_     :       int  		=					size
       UpperCAmelCase_     :       List[Any]  		=					resample
       UpperCAmelCase_     :       Optional[Any]  		=					rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase_     :       Any  		=					image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Dict[str, int]  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      np.ndarray:
       UpperCAmelCase_     :       Dict  		=					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()}'''							)
       return resize(
           lowerCamelCase_  ,size=(size["""height"""], size["""width"""])  ,resample=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     float  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     List[Any]							)	->      np.ndarray:
       return rescale(lowerCamelCase_  ,scale=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Union[float, List[float]]  ,lowerCamelCase_:     Union[float, List[float]]  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     List[Any]  ,)	->      np.ndarray:
       return normalize(lowerCamelCase_  ,mean=lowerCamelCase_  ,std=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)






   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     ImageInput  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Dict[str, int] = None  ,lowerCamelCase_:     PILImageResampling = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[float] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,lowerCamelCase_:     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**lowerCamelCase_:     int  ,)	->      List[Any]:
       UpperCAmelCase_     :       str  		=					do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_     :       Dict  		=					do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_     :       Dict  		=					do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_     :       int  		=					resample if resample is not None else self.resample
       UpperCAmelCase_     :       str  		=					rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_     :       int  		=					image_std if image_std is not None else self.image_std

       UpperCAmelCase_     :       Tuple  		=					size if size is not None else self.size
       UpperCAmelCase_     :       Union[str, Any]  		=					get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					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:
           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."""							)

       # All transformations expect numpy arrays.
       UpperCAmelCase_     :       Optional[Any]  		=					[to_numpy_array(lowerCamelCase_							) for image in images]

       if do_resize:
           UpperCAmelCase_     :       int  		=					[self.resize(image=lowerCamelCase_  ,size=lowerCamelCase_  ,resample=lowerCamelCase_							) for image in images]

       if do_rescale:
           UpperCAmelCase_     :       Optional[Any]  		=					[self.rescale(image=lowerCamelCase_  ,scale=lowerCamelCase_							) for image in images]

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

       UpperCAmelCase_     :       Any  		=					[to_channel_dimension_format(lowerCamelCase_  ,lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       List[str]  		=					{"""pixel_values""": images}
       return BatchFeature(data=lowerCamelCase_  ,tensor_type=lowerCamelCase_							)
