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        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=7				,       lowerCAmelCase__=3				,       lowerCAmelCase__=18				,       lowerCAmelCase__=30				,       lowerCAmelCase__=4_00				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       lowerCAmelCase__=True				,       lowerCAmelCase__=[0.4814_5466, 0.457_8275, 0.4082_1073]				,       lowerCAmelCase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,       lowerCAmelCase__=True				,       )					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							num_channels
							__lowercase			      =							image_size
							__lowercase			      =							min_resolution
							__lowercase			      =							max_resolution
							__lowercase			      =							do_resize
							__lowercase			      =							size if size is not None else {'''height''': 18, '''width''': 18}
							__lowercase			      =							do_center_crop
							__lowercase			      =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							__lowercase			      =							do_normalize
							__lowercase			      =							image_mean
							__lowercase			      =							image_std
							__lowercase			      =							do_convert_rgb





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							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        _UpperCamelCase				(						_UpperCAmelCase						,unittest.TestCase			):


			"""simple docstring"""






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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							OwlViTImageProcessingTester(self						)





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





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

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''do_resize'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''size'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''do_center_crop'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''center_crop'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''do_normalize'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''image_mean'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''image_std'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__				,       '''do_convert_rgb'''						)						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							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}						)

							__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							__lowercase			      =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCAmelCase__						)

							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__				,       Image.Image						)

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__lowercase			      =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCAmelCase__				,       numpify=lowerCAmelCase__						)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__				,       np.ndarray						)

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__lowercase			      =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCAmelCase__				,       torchify=lowerCAmelCase__						)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__				,       torch.Tensor						)

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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'''],
							    )				,       )
import timeit




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


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





			def __mul__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							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     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							Matrix([[1, 0], [0, 1]]							)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa
				__lowercase			      =							Matrix([[1, 1], [1, 0]]							)
				__lowercase			      =							modular_exponentiation(lowercase							,      n - 2							)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa

				__lowercase			      =							fa
				__lowercase			      =							fa
				n -= 2

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

				return fn_a





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
				__lowercase			      =							'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print('''With matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import simple_fibonacci
'''
				__lowercase			      =							'''simple_fibonacci(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print(
				    '''Without matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
		main()