





from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter





def    __UpperCamelCase      (    a,     a,     a = 1 / sqrt(2)) ->Union[str, Any]:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)

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

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

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





def    __UpperCamelCase      (    a,     a,     a = 1 / sqrt(2)) ->Dict:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)

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

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

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





def    __UpperCamelCase      (    a,     a,     a = 1 / sqrt(2)) ->Tuple:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)

					lowerCamelCase__       =       _sin / 2
					lowerCamelCase__       =       0
					lowerCamelCase__       =       -ba

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

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





def    __UpperCamelCase      (    a,     a,     a = 1 / sqrt(2)) ->Any:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)

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

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





def    __UpperCamelCase      (    a,     a,     a,     a = 1 / sqrt(2),     ) ->Union[str, Any]:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)
					lowerCamelCase__       =       10 ** (gain_db / 40)

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

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





def    __UpperCamelCase      (    a,     a,     a,     a = 1 / sqrt(2),     ) ->Tuple:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)
					lowerCamelCase__       =       10 ** (gain_db / 40)
					lowerCamelCase__       =       (big_a + 1) - (big_a - 1) * _cos
					lowerCamelCase__       =       (big_a + 1) + (big_a - 1) * _cos
					lowerCamelCase__       =       (big_a - 1) - (big_a + 1) * _cos
					lowerCamelCase__       =       (big_a - 1) + (big_a + 1) * _cos
					lowerCamelCase__       =       2 * sqrt(__UpperCamelCase) * alpha

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

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







def    __UpperCamelCase      (    a,     a,     a,     a = 1 / sqrt(2),     ) ->Dict:
					lowerCamelCase__       =       tau * frequency / samplerate
					lowerCamelCase__       =       sin(__UpperCamelCase)
					lowerCamelCase__       =       cos(__UpperCamelCase)
					lowerCamelCase__       =       _sin / (2 * q_factor)
					lowerCamelCase__       =       10 ** (gain_db / 40)
					lowerCamelCase__       =       (big_a + 1) - (big_a - 1) * _cos
					lowerCamelCase__       =       (big_a + 1) + (big_a - 1) * _cos
					lowerCamelCase__       =       (big_a - 1) - (big_a + 1) * _cos
					lowerCamelCase__       =       (big_a - 1) + (big_a + 1) * _cos
					lowerCamelCase__       =       2 * sqrt(__UpperCamelCase) * alpha

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

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








import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam

class SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										return datasets.DatasetInfo(
										    features=datasets.Features({"content": datasets.Value("string"	)}	)	,  supervised_keys=_lowerCAmelCase	,  )





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"examples": get_test_dummy_examples()}	)]






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

										return pipeline | "Load Examples" >> beam.Create(_lowerCAmelCase	)

class SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										return datasets.DatasetInfo(
										    features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"	)}	)}	)	,  supervised_keys=_lowerCAmelCase	,  )





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										return [
										    datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"examples": get_test_nested_examples()}	)
										]






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

										return pipeline | "Load Examples" >> beam.Create(_lowerCAmelCase	)





def    __UpperCamelCase      (    ) ->Optional[Any]:
					return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"])]





def    __UpperCamelCase      (    ) ->Optional[Any]:
					return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"])]







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train.arrow"	)	)	)
															self.assertDictEqual(builder.info.features	,  datasets.Features({"content": datasets.Value("string"	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															self.assertDictEqual(dset["train"][0]	,  get_test_dummy_examples()[0][1]	)
															self.assertDictEqual(
															    dset["train"][expected_num_examples - 1]	,  get_test_dummy_examples()[expected_num_examples - 1][1]	)
															self.assertTrue(
															    os.path.exists(os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset





					@require_beam
					def 					__magic_name__ ( self	):
										import apache_beam as beam

										lowerCamelCase__       =       beam.io.parquetio.WriteToParquet

										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															with patch("apache_beam.io.parquetio.WriteToParquet"	) as write_parquet_mock:
																				lowerCamelCase__       =       partial(_lowerCAmelCase	,  num_shards=2	)
																				builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            _lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train-00000-of-00002.arrow"	)	)	)
															self.assertTrue(
															    os.path.exists(
															        os.path.join(
															            _lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train-00000-of-00002.arrow"	)	)	)
															self.assertDictEqual(builder.info.features	,  datasets.Features({"content": datasets.Value("string"	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															# Order is not preserved when sharding, so we just check that all the elements are there
															self.assertListEqual(sorted(dset["train"]["content"]	)	,  sorted(["foo", "bar", "foobar"]	)	)
															self.assertTrue(
															    os.path.exists(os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset





					@require_beam
					def 					__magic_name__ ( self	):
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	)
															self.assertRaises(datasets.builder.MissingBeamOptions	,  builder.download_and_prepare	)






					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_nested_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       NestedBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															builder.download_and_prepare()
															self.assertTrue(
															    os.path.exists(
															        os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  F"{builder.name}-train.arrow"	)	)	)
															self.assertDictEqual(
															    builder.info.features	,  datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"	)}	)}	)	)
															lowerCamelCase__       =       builder.as_dataset()
															self.assertEqual(dset["train"].num_rows	,  _lowerCAmelCase	)
															self.assertEqual(dset["train"].info.splits["train"].num_examples	,  _lowerCAmelCase	)
															self.assertDictEqual(dset["train"][0]	,  get_test_nested_examples()[0][1]	)
															self.assertDictEqual(
															    dset["train"][expected_num_examples - 1]	,  get_test_nested_examples()[expected_num_examples - 1][1]	)
															self.assertTrue(
															    os.path.exists(os.path.join(_lowerCAmelCase	,  builder.name	,  "default"	,  "0.0.0"	,  "dataset_info.json"	)	)	)
															del dset

