






"""simple docstring"""





def     __lowercase					(    a :				int       ,		a :				int )					->					float:
      return base * power(a       ,		(exponent - 1) ) if exponent else 1


if __name__ == "__main__":
    print("""Raise base to the power of exponent using recursion...""")
    UpperCamelCase_	:				List[Any]										= int(input("""Enter the base: """).strip())
    UpperCamelCase_	:				Tuple										= int(input("""Enter the exponent: """).strip())
    UpperCamelCase_	:				Tuple										= power(base, abs(exponent))
    if exponent < 0:  # power() does not properly deal w/ negative exponents
        UpperCamelCase_	:				Any										= 1 / result
    print(F'''{base} to the power of {exponent} is {result}''')










"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class      _lowercase (     unittest.TestCase						):


      def __init__(    self				:	List[Any]							,   a				:	int							,   a				:	Dict=7							,   a				:	int=3							,   a				:	int=1_8							,   a				:	Dict=3_0							,   a				:	Dict=4_0_0							,   a				:	Optional[Any]=True							,   a				:	Dict=None							,   a				:	int=True							,   a				:	Dict=False							,   a				:	int=True							,   a				:	str=True							,   a				:	List[str]=[0.5, 0.5, 0.5]							,   a				:	Optional[Any]=[0.5, 0.5, 0.5]							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=parent
            __snake_case      :    List[Any]  			=batch_size
            __snake_case      :    str  			=num_channels
            __snake_case      :    Dict  			=image_size
            __snake_case      :    str  			=min_resolution
            __snake_case      :    Tuple  			=max_resolution
            __snake_case      :    str  			=do_resize
            __snake_case      :    Any  			=size if size is not None else {'''height''': 1_8, '''width''': 2_0}
            __snake_case      :    List[Any]  			=do_thumbnail
            __snake_case      :    Tuple  			=do_align_axis
            __snake_case      :    Any  			=do_pad
            __snake_case      :    Dict  			=do_normalize
            __snake_case      :    List[Any]  			=image_mean
            __snake_case      :    Any  			=image_std







      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_thumbnail": self.do_thumbnail,
                "do_align_long_axis": self.do_align_axis,
                "do_pad": self.do_pad,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }



@require_torch
@require_vision
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     str								=   DonutImageProcessor if is_vision_available() else None


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=DonutImageProcessingTester(self						)


      @property
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(a							,   '''do_resize'''						)						)
            self.assertTrue(hasattr(a							,   '''size'''						)						)
            self.assertTrue(hasattr(a							,   '''do_thumbnail'''						)						)
            self.assertTrue(hasattr(a							,   '''do_align_long_axis'''						)						)
            self.assertTrue(hasattr(a							,   '''do_pad'''						)						)
            self.assertTrue(hasattr(a							,   '''do_normalize'''						)						)
            self.assertTrue(hasattr(a							,   '''image_mean'''						)						)
            self.assertTrue(hasattr(a							,   '''image_std'''						)						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.image_processing_class.from_dict(self.image_processor_dict						)
            self.assertEqual(image_processor.size							,   {'''height''': 1_8, '''width''': 2_0}						)

            __snake_case      :    List[Any]  			=self.image_processing_class.from_dict(self.image_processor_dict							,   size=4_2						)
            self.assertEqual(image_processor.size							,   {'''height''': 4_2, '''width''': 4_2}						)

            # Previous config had dimensions in (width, height) order
            __snake_case      :    Tuple  			=self.image_processing_class.from_dict(self.image_processor_dict							,   size=(4_2, 8_4)						)
            self.assertEqual(image_processor.size							,   {'''height''': 8_4, '''width''': 4_2}						)


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            pass


      @is_flaky()
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PIL images
            __snake_case      :    List[Any]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   Image.Image						)

            # Test not batched input
            __snake_case      :    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
            __snake_case      :    Optional[int]  			=image_processing(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'''],
                )							,   )


      @is_flaky()
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.image_processing_class(**self.image_processor_dict						)
            # create random numpy tensors
            __snake_case      :    Optional[int]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   numpify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   np.ndarray						)

            # Test not batched input
            __snake_case      :    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
            __snake_case      :    int  			=image_processing(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'''],
                )							,   )







      @is_flaky()
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







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

            # Test not batched input
            __snake_case      :    Dict  			=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
            __snake_case      :    Optional[int]  			=image_processing(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'''],
                )							,   )


