
'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor


class 					a_					(unittest.TestCase      ):




							def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=True				,				snake_case_=None				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
									_lowerCAmelCase    :					Optional[Any]							=   size if size is not None else {"""height""": 2_0, """width""": 2_0}
									_lowerCAmelCase    :					List[Any]							=   crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}

									_lowerCAmelCase    :					int							=   parent
									_lowerCAmelCase    :					int							=   batch_size
									_lowerCAmelCase    :					Dict							=   num_channels
									_lowerCAmelCase    :					List[Any]							=   image_size
									_lowerCAmelCase    :					str							=   min_resolution
									_lowerCAmelCase    :					Union[str, Any]							=   max_resolution
									_lowerCAmelCase    :					Union[str, Any]							=   do_resize
									_lowerCAmelCase    :					int							=   size
									_lowerCAmelCase    :					Optional[int]							=   do_center_crop
									_lowerCAmelCase    :					Any							=   crop_size
									_lowerCAmelCase    :					Any							=   do_normalize
									_lowerCAmelCase    :					Optional[Any]							=   image_mean
									_lowerCAmelCase    :					Optional[Any]							=   image_std







							def 							__UpperCamelCase    (       self					):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
							__lowerCAmelCase      :			int      = DeiTImageProcessor if is_vision_available() else None
							__lowerCAmelCase      :			Any      = True




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					int							=   DeiTImageProcessingTester(self					)




							@property
							def 							__UpperCamelCase    (       self					):
									return self.image_processor_tester.prepare_image_processor_dict()




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Any							=   self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
									self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
									self.assertTrue(hasattr(snake_case_				,				"""do_center_crop"""					)					)
									self.assertTrue(hasattr(snake_case_				,				"""center_crop"""					)					)
									self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
									self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
									self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					int							=   self.image_processing_class.from_dict(self.image_processor_dict					)
									self.assertEqual(image_processor.size				,				{"""height""": 2_0, """width""": 2_0}					)
									self.assertEqual(image_processor.crop_size				,				{"""height""": 1_8, """width""": 1_8}					)

									_lowerCAmelCase    :					int							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2				,				crop_size=8_4					)
									self.assertEqual(image_processor.size				,				{"""height""": 4_2, """width""": 4_2}					)
									self.assertEqual(image_processor.crop_size				,				{"""height""": 8_4, """width""": 8_4}					)




							def 							__UpperCamelCase    (       self					):
									pass




							def 							__UpperCamelCase    (       self					):
									# Initialize image_processing
									_lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									_lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
									for image in image_inputs:
											self.assertIsInstance(snake_case_				,				Image.Image					)

									# Test not batched input
									_lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,				)

									# Test batched
									_lowerCAmelCase    :					Any							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,				(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,				)




							def 							__UpperCamelCase    (       self					):
									# Initialize image_processing
									_lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									_lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
									for image in image_inputs:
											self.assertIsInstance(snake_case_				,				np.ndarray					)

									# Test not batched input
									_lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,				)

									# Test batched
									_lowerCAmelCase    :					Dict							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,				(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,				)







							def 							__UpperCamelCase    (       self					):
									# Initialize image_processing
									_lowerCAmelCase    :					Any							=   self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									_lowerCAmelCase    :					Tuple							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
									for image in image_inputs:
											self.assertIsInstance(snake_case_				,				torch.Tensor					)

									# Test not batched input
									_lowerCAmelCase    :					int							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,				(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,				)

									# Test batched
									_lowerCAmelCase    :					Dict							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,				(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,				)




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    list[list[int]]					, _lowerCamelCase  :    list[list[int]]						)      ->  list[list[int]]:
  _lowerCAmelCase    :					int							=   []
  _lowerCAmelCase    :					Union[str, Any]							=   len(_lowerCamelCase						)
  for i in range(_lowerCamelCase						):
    _lowerCAmelCase    :					List[str]							=   []
    for j in range(_lowerCamelCase						):
      _lowerCAmelCase    :					Union[str, Any]							=   0
      for k in range(_lowerCamelCase						):
        _lowerCAmelCase    :					Any							=   val + matrix_a[i][k] * matrix_b[k][j]
      list_a.append(_lowerCamelCase						)
    matrix_c.append(_lowerCamelCase						)
  return matrix_c


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  list[list[int]]:
  return [[int(row == column						) for column in range(_lowerCamelCase						)] for row in range(_lowerCamelCase						)]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Tuple							=   identity(2						)
  _lowerCAmelCase    :					Union[str, Any]							=   [[1, 1], [1, 0]]
  _lowerCAmelCase    :					Optional[int]							=   n - 1
  while n > 0:
    if n % 2 == 1:
      _lowerCAmelCase    :					str							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Tuple							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Dict							=   int(n / 2						)
  return res_matrix[0][0]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Any							=   0
  _lowerCAmelCase    :					Dict							=   1
  for _ in range(2					, n + 1						):
    _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   fiba, fiba + fiba
  return fiba


def 		_UpperCAmelCase			(					)      ->  None:
  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    _lowerCAmelCase    :					Union[str, Any]							=   int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
    print(
        f'{ordinal} fibonacci number using matrix exponentiation is '
        f'{nth_fibonacci_matrix(_lowerCamelCase						)} and using bruteforce is '
        f'{nth_fibonacci_bruteforce(_lowerCamelCase						)}\n'						)
  # from timeit import timeit
  # print(timeit("nth_fibonacci_matrix(1000000)",
  #              "from main import nth_fibonacci_matrix", number=5))
  # print(timeit("nth_fibonacci_bruteforce(1000000)",
  #              "from main import nth_fibonacci_bruteforce", number=5))
  # 2.3342058970001744
  # 57.256506615000035


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()


