
'''simple docstring'''







from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = 1 / sqrt(2	)	)       ->	IIRFilter:
	_snake_case   						=		tau * frequency / samplerate
	_snake_case   						=		sin(__A	)
	_snake_case   						=		cos(__A	)
	_snake_case   						=		_sin / (2 * q_factor)

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

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

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = 1 / sqrt(2	)	)       ->	IIRFilter:
	_snake_case   						=		tau * frequency / samplerate
	_snake_case   						=		sin(__A	)
	_snake_case   						=		cos(__A	)
	_snake_case   						=		_sin / (2 * q_factor)

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

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

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = 1 / sqrt(2	)	)       ->	IIRFilter:
	_snake_case   						=		tau * frequency / samplerate
	_snake_case   						=		sin(__A	)
	_snake_case   						=		cos(__A	)
	_snake_case   						=		_sin / (2 * q_factor)

	_snake_case   						=		_sin / 2
	_snake_case   						=		0
	_snake_case   						=		-ba

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

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = 1 / sqrt(2	)	)       ->	IIRFilter:
	_snake_case   						=		tau * frequency / samplerate
	_snake_case   						=		sin(__A	)
	_snake_case   						=		cos(__A	)
	_snake_case   						=		_sin / (2 * q_factor)

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

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







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

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

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







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

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

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







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

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

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



'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   dtype=tf.floataa    ,   )
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,   expected_slice.numpy()    ,   atol=1E-4  )  )

