
"""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 LevitImageProcessor






class     _UpperCAmelCase(    unittest.TestCase	):



		def __init__(					self				,		__a				,		__a=7				,		__a=3				,		__a=18				,		__a=30				,		__a=4_00				,		__a=True				,		__a=None				,		__a=True				,		__a=None				,		__a=True				,		__a=[0.5, 0.5, 0.5]				,		__a=[0.5, 0.5, 0.5]				,		)       ->       Dict:






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




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






							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}






@require_torch
@require_vision
class     _UpperCAmelCase(    lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      LevitImageProcessor if is_vision_available() else None



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  LevitImageProcessingTester(self)



		@property
		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(_snake_case				,		'''image_mean'''))
							self.assertTrue(hasattr(_snake_case				,		'''image_std'''))
							self.assertTrue(hasattr(_snake_case				,		'''do_normalize'''))
							self.assertTrue(hasattr(_snake_case				,		'''do_resize'''))
							self.assertTrue(hasattr(_snake_case				,		'''do_center_crop'''))
							self.assertTrue(hasattr(_snake_case				,		'''size'''))



		def 						UpperCAmelCase							(					self)       ->       Union[str, Any]:






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



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							pass



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






							'''simple docstring'''
							_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 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							_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 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_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'''],
							    )				,		)








from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 - _cos) / 2
				__a										=    1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 + _cos) / 2
				__a										=    -1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    _sin / 2
				__a										=    0
				__a										=    -ba

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    1 - alpha
				__a										=    -2 * _cos
				__a										=    1 + alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([ba, ba, ba]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)

				__a										=    1 + alpha * big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha * big_a
				__a										=    1 + alpha / big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha / big_a

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (pmc + aaa)
				__a										=    2 * big_a * mpc
				__a										=    big_a * (pmc - aaa)
				__a										=    ppmc + aaa
				__a										=    -2 * pmpc
				__a										=    ppmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt






def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (ppmc + aaa)
				__a										=    -2 * big_a * pmpc
				__a										=    big_a * (ppmc - aaa)
				__a										=    pmc + aaa
				__a										=    2 * mpc
				__a										=    pmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt