





'''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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BlipImageProcessor


class  a_					(    unittest.TestCase	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A=7			,		A=3			,		A=18			,		A=30			,		A=400			,		A=True			,		A=None			,		A=True			,		A=False			,		A=[0.4814_5466, 0.457_8275, 0.4082_1073]			,		A=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,		A=True			,		)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      size if size is not None else {"""height""": 20, """width""": 20}
        _SCREAMING_SNAKE_CASE     =      parent
        _SCREAMING_SNAKE_CASE     =      batch_size
        _SCREAMING_SNAKE_CASE     =      num_channels
        _SCREAMING_SNAKE_CASE     =      image_size
        _SCREAMING_SNAKE_CASE     =      min_resolution
        _SCREAMING_SNAKE_CASE     =      max_resolution
        _SCREAMING_SNAKE_CASE     =      do_resize
        _SCREAMING_SNAKE_CASE     =      size
        _SCREAMING_SNAKE_CASE     =      do_normalize
        _SCREAMING_SNAKE_CASE     =      image_mean
        _SCREAMING_SNAKE_CASE     =      image_std
        _SCREAMING_SNAKE_CASE     =      do_pad
        _SCREAMING_SNAKE_CASE     =      do_convert_rgb


    def       snake_case_(		self	)		->       List[Any]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }





    def       snake_case_(		self			,		A=False			,		A=False			,		A=False	)		->       Tuple:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
            _SCREAMING_SNAKE_CASE     =      []
            for i in range(self.batch_size	):
                image_inputs.append(
                    np.random.randint(
                        255			,		size=(self.num_channels, self.max_resolution, self.max_resolution)			,		dtype=np.uinta	)	)
        else:
            _SCREAMING_SNAKE_CASE     =      []
            for i in range(self.batch_size	):
                _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      np.random.choice(np.arange(self.min_resolution			,		self.max_resolution	)			,		2	)
                image_inputs.append(np.random.randint(255			,		size=(self.num_channels, width, height)			,		dtype=np.uinta	)	)

        if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            _SCREAMING_SNAKE_CASE     =      [Image.fromarray(np.moveaxis(A			,		0			,		-1	)	) for x in image_inputs]

        if torchify:
            _SCREAMING_SNAKE_CASE     =      [torch.from_numpy(A	) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class  a_					(    snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			BlipImageProcessor if is_vision_available() else None


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      BlipImageProcessingTester(self	)


    @property
    def       snake_case_(		self	)		->       Dict:
        return self.image_processor_tester.prepare_image_processor_dict()


    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A			,		"""do_resize"""	)	)
        self.assertTrue(hasattr(A			,		"""size"""	)	)
        self.assertTrue(hasattr(A			,		"""do_normalize"""	)	)
        self.assertTrue(hasattr(A			,		"""image_mean"""	)	)
        self.assertTrue(hasattr(A			,		"""image_std"""	)	)
        self.assertTrue(hasattr(A			,		"""do_convert_rgb"""	)	)


    def       snake_case_(		self	)		->       str:
        pass


    def       snake_case_(		self	)		->       List[str]:
        # Initialize image_processor
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _SCREAMING_SNAKE_CASE     =      self.image_processor_tester.prepare_inputs(equal_resolution=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		Image.Image	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      image_processor(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
        _SCREAMING_SNAKE_CASE     =      image_processor(A			,		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       snake_case_(		self	)		->       Optional[int]:
        # Initialize image_processor
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _SCREAMING_SNAKE_CASE     =      self.image_processor_tester.prepare_inputs(equal_resolution=A			,		numpify=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		np.ndarray	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      image_processor(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
        _SCREAMING_SNAKE_CASE     =      image_processor(A			,		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       snake_case_(		self	)		->       List[str]:
        # Initialize image_processor
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _SCREAMING_SNAKE_CASE     =      self.image_processor_tester.prepare_inputs(equal_resolution=A			,		torchify=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		torch.Tensor	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      image_processor(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
        _SCREAMING_SNAKE_CASE     =      image_processor(A			,		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"""],
            )			,		)




@require_torch
@require_vision
class  a_					(    snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			BlipImageProcessor if is_vision_available() else None


    def       snake_case_(		self	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      BlipImageProcessingTester(self			,		num_channels=4	)
        _SCREAMING_SNAKE_CASE     =      3


    @property
    def       snake_case_(		self	)		->       List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()


    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A			,		"""do_resize"""	)	)
        self.assertTrue(hasattr(A			,		"""size"""	)	)
        self.assertTrue(hasattr(A			,		"""do_normalize"""	)	)
        self.assertTrue(hasattr(A			,		"""image_mean"""	)	)
        self.assertTrue(hasattr(A			,		"""image_std"""	)	)
        self.assertTrue(hasattr(A			,		"""do_convert_rgb"""	)	)


    def       snake_case_(		self	)		->       Dict:
        pass





    def       snake_case_(		self	)		->       Optional[Any]:
        # Initialize image_processor
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _SCREAMING_SNAKE_CASE     =      self.image_processor_tester.prepare_inputs(equal_resolution=A	)
        for image in image_inputs:
            self.assertIsInstance(A			,		Image.Image	)

        # Test not batched input
        _SCREAMING_SNAKE_CASE     =      image_processor(image_inputs[0]			,		return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_images.shape			,		(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )			,		)

        # Test batched
        _SCREAMING_SNAKE_CASE     =      image_processor(A			,		return_tensors="""pt"""	).pixel_values
        self.assertEqual(
            encoded_images.shape			,		(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )			,		)










'''simple docstring'''

import timeit


class  a_					:

    '''simple docstring'''


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





    def __mul__(		self			,		A	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      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						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Optional[int]   )						->int:
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 0], [0, 1]]   )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Tuple   )						->Dict:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 1], [1, 0]]   )
    _SCREAMING_SNAKE_CASE     =      modular_exponentiation(__lowerCamelCase ,	n - 2   )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple   )						->str:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    _SCREAMING_SNAKE_CASE     =      fa
    _SCREAMING_SNAKE_CASE     =      fa
    n -= 2

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

    return fn_a





def lowerCamelCase						(					)						->str:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    _SCREAMING_SNAKE_CASE     =      """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print("""With matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import simple_fibonacci
"""
    _SCREAMING_SNAKE_CASE     =      """simple_fibonacci(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print(
        """Without matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->List[str]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
      main()



