
'''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, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor

class 	__lowercase				(  unittest.TestCase    ):

   def __init__(self			,   A			,   A=7			,   A=3			,   A=1_8			,   A=3_0			,   A=4_0_0			,   A=True			,   A=3_2			,   A=True			,   ):
        lowerCamelCase_					:      str     =		parent
        lowerCamelCase_					:      List[str]     =		batch_size
        lowerCamelCase_					:      Any     =		num_channels
        lowerCamelCase_					:      List[Any]     =		image_size
        lowerCamelCase_					:      Optional[Any]     =		min_resolution
        lowerCamelCase_					:      Union[str, Any]     =		max_resolution
        lowerCamelCase_					:      List[Any]     =		do_resize
        lowerCamelCase_					:      List[str]     =		size_divisor
        lowerCamelCase_					:      Tuple     =		do_rescale






   def        UpperCAmelCase__ (self      ):
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }

@require_torch
@require_vision
class 	__lowercase				(  _lowercase      ,     unittest.TestCase    ):
   lowerCamelCase			:		Optional[int]	      =			GLPNImageProcessor if is_vision_available() else None

   def        UpperCAmelCase__ (self      ):
        lowerCamelCase_					:      List[str]     =		GLPNImageProcessingTester(self      )

   @property
   def        UpperCAmelCase__ (self      ):
        return self.image_processor_tester.prepare_image_processor_dict()

   def        UpperCAmelCase__ (self      ):
        lowerCamelCase_					:      str     =		self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(A			,   '''do_resize'''      )      )
        self.assertTrue(hasattr(A			,   '''size_divisor'''      )      )
        self.assertTrue(hasattr(A			,   '''resample'''      )      )
        self.assertTrue(hasattr(A			,   '''do_rescale'''      )      )

   def        UpperCAmelCase__ (self      ):
        pass

   def        UpperCAmelCase__ (self      ):
        lowerCamelCase_					:      Tuple     =		self.image_processing_class(**self.image_processor_dict      )
        # create random PIL images
        lowerCamelCase_					:      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 (GLPNImageProcessor doesn't support batching)
        lowerCamelCase_					:      Dict     =		image_processing(image_inputs[0]			,   return_tensors='''pt'''      ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )

   def        UpperCAmelCase__ (self      ):
        lowerCamelCase_					:      List[str]     =		self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        lowerCamelCase_					:      Dict     =		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 (GLPNImageProcessor doesn't support batching)
        lowerCamelCase_					:      str     =		image_processing(image_inputs[0]			,   return_tensors='''pt'''      ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )






   def        UpperCAmelCase__ (self      ):
        lowerCamelCase_					:      str     =		self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        lowerCamelCase_					:      Tuple     =		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 (GLPNImageProcessor doesn't support batching)
        lowerCamelCase_					:      Optional[int]     =		image_processing(image_inputs[0]			,   return_tensors='''pt'''      ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




'''simple docstring'''


from __future__ import annotations

from math import pi, sqrt




def lowercase_	(   _lowercase			,       _lowercase				)     ->							tuple:
					'''simple docstring'''

					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''				)

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''				)

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance				)))				),
										)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


