def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	list[list[int]]:






      '''simple docstring'''





      UpperCAmelCase     :			Tuple     	=[]
      UpperCAmelCase     :			Optional[int]     	=len(__lowerCAmelCase    )
      for i in range(__lowerCAmelCase    ):
            UpperCAmelCase     :			Optional[Any]     	=[]
            for j in range(__lowerCAmelCase    ):
                  UpperCAmelCase     :			Union[str, Any]     	=0
                  for k in range(__lowerCAmelCase    ):
                        UpperCAmelCase     :			Tuple     	=val + matrix_a[i][k] * matrix_b[k][j]
                  list_a.append(__lowerCAmelCase    )
            matrix_c.append(__lowerCAmelCase    )
      return matrix_c





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	list[list[int]]:






      '''simple docstring'''





      return [[int(row == column    ) for column in range(__lowerCAmelCase    )] for row in range(__lowerCAmelCase    )]





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	int:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	int:






      '''simple docstring'''





      if n <= 1:
            return n
      UpperCAmelCase     :			Union[str, Any]     	=0
      UpperCAmelCase     :			Optional[int]     	=1
      for _ in range(2		,       n + 1    ):
            UpperCAmelCase	,    UpperCAmelCase     :			List[Any]     	=fiba, fiba + fiba
      return fiba





def 	lowerCAmelCase_			(					)->	None:






      '''simple docstring'''





      for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
            UpperCAmelCase     :			str     	=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()

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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, 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]		, )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=size if size is not None else {'''height''': 18, '''width''': 18}
       UpperCAmelCase     :			Tuple     	=parent
       UpperCAmelCase     :			List[str]     	=batch_size
       UpperCAmelCase     :			int     	=num_channels
       UpperCAmelCase     :			List[str]     	=image_size
       UpperCAmelCase     :			str     	=min_resolution
       UpperCAmelCase     :			Optional[int]     	=max_resolution
       UpperCAmelCase     :			Optional[int]     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Union[str, Any]     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			List[Any]     	=image_std






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }







@require_torch
@require_vision
class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size		, {'''height''': 18, '''width''': 18}    )

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       UpperCAmelCase     :			Optional[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
       UpperCAmelCase     :			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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			int     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			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
       UpperCAmelCase     :			str     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			Union[str, 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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       UpperCAmelCase     :			int     	=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
       UpperCAmelCase     :			List[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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			List[str]     	=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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )
