




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 OwlViTImageProcessor

class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def __init__(  self       :			str			,							snake_case       :			Any			,							snake_case       :			Union[str, Any]=7			,							snake_case       :			Optional[Any]=3			,							snake_case       :			str=18			,							snake_case       :			Union[str, Any]=30			,							snake_case       :			List[Any]=400			,							snake_case       :			List[Any]=True			,							snake_case       :			int=None			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			str=None			,							snake_case       :			Any=True			,							snake_case       :			Optional[Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,							snake_case       :			Optional[int]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,							snake_case       :			List[Any]=True			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    num_channels
          __UpperCamelCase			=    image_size
          __UpperCamelCase			=    min_resolution
          __UpperCamelCase			=    max_resolution
          __UpperCamelCase			=    do_resize
          __UpperCamelCase			=    size if size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_center_crop
          __UpperCamelCase			=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_normalize
          __UpperCamelCase			=    image_mean
          __UpperCamelCase			=    image_std
          __UpperCamelCase			=    do_convert_rgb







      def    snake_case						(  self       :			Union[str, Any]					):
          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,
              "do_convert_rgb": self.do_convert_rgb,
          }







@require_torch
@require_vision
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			List[str]     =       OwlViTImageProcessor if is_vision_available() else None







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    OwlViTImageProcessingTester(self					)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return self.image_processor_tester.prepare_image_processor_dict()







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    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'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_convert_rgb'''					)					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size			,							{'''height''': 18, '''width''': 18}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 18, '''width''': 18}					)

          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict			,							size=42			,							crop_size=84					)
          self.assertEqual(image_processor.size			,							{'''height''': 42, '''width''': 42}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 84, '''width''': 84}					)







      def    snake_case						(  self       :			Tuple					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          __UpperCamelCase			=    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			=    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
          __UpperCamelCase			=    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    snake_case						(  self       :			List[str]					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          __UpperCamelCase			=    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			=    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
          __UpperCamelCase			=    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    snake_case						(  self       :			int					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          __UpperCamelCase			=    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			=    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
          __UpperCamelCase			=    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       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





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





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  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(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\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()






