






from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)			) ->	IIRFilter:
     UpperCamelCase__			:   Union[str, Any]       				=  tau * frequency / samplerate
     UpperCamelCase__			:   int       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[Any]       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   str       				=  _sin / (2 * q_factor)

     UpperCamelCase__			:   Dict       				=  (1 - _cos) / 2
     UpperCamelCase__			:   Optional[Any]       				=  1 - _cos

     UpperCamelCase__			:   Dict       				=  1 + alpha
     UpperCamelCase__			:   int       				=  -2 * _cos
     UpperCamelCase__			:   str       				=  1 - alpha

     UpperCamelCase__			:   str       				=  IIRFilter(2			)
     filt.set_coefficients([aa, aa, aa]					,    [ba, ba, ba]			)
     return filt







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)			) ->	IIRFilter:
     UpperCamelCase__			:   List[str]       				=  tau * frequency / samplerate
     UpperCamelCase__			:   Tuple       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   List[Any]       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   Any       				=  _sin / (2 * q_factor)

     UpperCamelCase__			:   Optional[Any]       				=  (1 + _cos) / 2
     UpperCamelCase__			:   List[str]       				=  -1 - _cos

     UpperCamelCase__			:   Tuple       				=  1 + alpha
     UpperCamelCase__			:   Tuple       				=  -2 * _cos
     UpperCamelCase__			:   Any       				=  1 - alpha

     UpperCamelCase__			:   Optional[Any]       				=  IIRFilter(2			)
     filt.set_coefficients([aa, aa, aa]					,    [ba, ba, ba]			)
     return filt







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)			) ->	IIRFilter:
     UpperCamelCase__			:   List[str]       				=  tau * frequency / samplerate
     UpperCamelCase__			:   List[Any]       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   Dict       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[int]       				=  _sin / (2 * q_factor)

     UpperCamelCase__			:   List[Any]       				=  _sin / 2
     UpperCamelCase__			:   Union[str, Any]       				=  0
     UpperCamelCase__			:   List[Any]       				=  -ba

     UpperCamelCase__			:   Union[str, Any]       				=  1 + alpha
     UpperCamelCase__			:   Union[str, Any]       				=  -2 * _cos
     UpperCamelCase__			:   Dict       				=  1 - alpha

     UpperCamelCase__			:   int       				=  IIRFilter(2			)
     filt.set_coefficients([aa, aa, aa]					,    [ba, ba, ba]			)
     return filt







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)			) ->	IIRFilter:
     UpperCamelCase__			:   Tuple       				=  tau * frequency / samplerate
     UpperCamelCase__			:   str       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   Union[str, Any]       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[int]       				=  _sin / (2 * q_factor)

     UpperCamelCase__			:   Optional[Any]       				=  1 - alpha
     UpperCamelCase__			:   int       				=  -2 * _cos
     UpperCamelCase__			:   int       				=  1 + alpha

     UpperCamelCase__			:   Dict       				=  IIRFilter(2			)
     filt.set_coefficients([ba, ba, ba]					,    [ba, ba, ba]			)
     return filt







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)					,    ) ->	IIRFilter:
     UpperCamelCase__			:   Optional[Any]       				=  tau * frequency / samplerate
     UpperCamelCase__			:   Union[str, Any]       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   Any       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   Any       				=  _sin / (2 * q_factor)
     UpperCamelCase__			:   Dict       				=  10 ** (gain_db / 40)

     UpperCamelCase__			:   Optional[int]       				=  1 + alpha * big_a
     UpperCamelCase__			:   List[Any]       				=  -2 * _cos
     UpperCamelCase__			:   Optional[Any]       				=  1 - alpha * big_a
     UpperCamelCase__			:   Optional[Any]       				=  1 + alpha / big_a
     UpperCamelCase__			:   int       				=  -2 * _cos
     UpperCamelCase__			:   Optional[int]       				=  1 - alpha / big_a

     UpperCamelCase__			:   Union[str, Any]       				=  IIRFilter(2			)
     filt.set_coefficients([aa, aa, aa]					,    [ba, ba, ba]			)
     return filt







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)					,    ) ->	IIRFilter:
     UpperCamelCase__			:   List[Any]       				=  tau * frequency / samplerate
     UpperCamelCase__			:   Optional[Any]       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   Union[str, Any]       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   Dict       				=  _sin / (2 * q_factor)
     UpperCamelCase__			:   str       				=  10 ** (gain_db / 40)
     UpperCamelCase__			:   Tuple       				=  (big_a + 1) - (big_a - 1) * _cos
     UpperCamelCase__			:   Dict       				=  (big_a + 1) + (big_a - 1) * _cos
     UpperCamelCase__			:   int       				=  (big_a - 1) - (big_a + 1) * _cos
     UpperCamelCase__			:   Union[str, Any]       				=  (big_a - 1) + (big_a + 1) * _cos
     UpperCamelCase__			:   Dict       				=  2 * sqrt(__lowerCAmelCase			) * alpha

     UpperCamelCase__			:   Optional[int]       				=  big_a * (pmc + aaa)
     UpperCamelCase__			:   Optional[int]       				=  2 * big_a * mpc
     UpperCamelCase__			:   Any       				=  big_a * (pmc - aaa)
     UpperCamelCase__			:   List[Any]       				=  ppmc + aaa
     UpperCamelCase__			:   Optional[int]       				=  -2 * pmpc
     UpperCamelCase__			:   Tuple       				=  ppmc - aaa

     UpperCamelCase__			:   Optional[int]       				=  IIRFilter(2			)
     filt.set_coefficients([aa, aa, aa]					,    [ba, ba, ba]			)
     return filt







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = 1 / sqrt(2			)					,    ) ->	IIRFilter:
     UpperCamelCase__			:   List[Any]       				=  tau * frequency / samplerate
     UpperCamelCase__			:   Optional[Any]       				=  sin(__lowerCAmelCase			)
     UpperCamelCase__			:   List[Any]       				=  cos(__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[int]       				=  _sin / (2 * q_factor)
     UpperCamelCase__			:   Dict       				=  10 ** (gain_db / 40)
     UpperCamelCase__			:   Optional[int]       				=  (big_a + 1) - (big_a - 1) * _cos
     UpperCamelCase__			:   int       				=  (big_a + 1) + (big_a - 1) * _cos
     UpperCamelCase__			:   List[Any]       				=  (big_a - 1) - (big_a + 1) * _cos
     UpperCamelCase__			:   Dict       				=  (big_a - 1) + (big_a + 1) * _cos
     UpperCamelCase__			:   Dict       				=  2 * sqrt(__lowerCAmelCase			) * alpha

     UpperCamelCase__			:   Any       				=  big_a * (ppmc + aaa)
     UpperCamelCase__			:   str       				=  -2 * big_a * pmpc
     UpperCamelCase__			:   Optional[int]       				=  big_a * (ppmc - aaa)
     UpperCamelCase__			:   int       				=  pmc + aaa
     UpperCamelCase__			:   Optional[int]       				=  2 * mpc
     UpperCamelCase__			:   Tuple       				=  pmc - aaa

     UpperCamelCase__			:   List[Any]       				=  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 			__a  (    datasets.BeamBasedBuilder       ):







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return datasets.DatasetInfo(
          features=datasets.Features({"content": datasets.Value("string"						)}						)							,   supervised_keys=SCREAMING_SNAKE_CASE							,   )







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      import apache_beam as beam

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







class 			__a  (    datasets.BeamBasedBuilder       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      import apache_beam as beam

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







def 		SCREAMING_SNAKE_CASE							(					) ->	List[str]:
     return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"]			)]







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





class 			__a  (    A__       ):







 @require_beam
 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  len(get_test_dummy_examples()						)
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           UpperCamelCase__			:   List[Any]       				=  DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE							,   beam_runner="DirectRunner"						)
           builder.download_and_prepare()
           self.assertTrue(
               os.path.exists(
                   os.path.join(SCREAMING_SNAKE_CASE							,   builder.name							,   "default"							,   "0.0.0"							,   F'{builder.name}-train.arrow'						)						)						)
           self.assertDictEqual(builder.info.features							,   datasets.Features({"content": datasets.Value("string"						)}						)						)
           UpperCamelCase__			:   Optional[Any]       				=  builder.as_dataset()
           self.assertEqual(dset["train"].num_rows							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(dset["train"].info.splits["train"].num_examples							,   SCREAMING_SNAKE_CASE						)
           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(SCREAMING_SNAKE_CASE							,   builder.name							,   "default"							,   "0.0.0"							,   "dataset_info.json"						)						)						)
           del dset







 @require_beam
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      import apache_beam as beam

      UpperCamelCase__			:   Any       				=  beam.io.parquetio.WriteToParquet

      UpperCamelCase__			:   Tuple       				=  len(get_test_dummy_examples()						)
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           UpperCamelCase__			:   str       				=  DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE							,   beam_runner="DirectRunner"						)
           with patch("apache_beam.io.parquetio.WriteToParquet"						) as write_parquet_mock:
                UpperCamelCase__			:   Optional[Any]       				=  partial(SCREAMING_SNAKE_CASE							,   num_shards=2						)
                builder.download_and_prepare()
           self.assertTrue(
               os.path.exists(
                   os.path.join(
                       SCREAMING_SNAKE_CASE							,   builder.name							,   "default"							,   "0.0.0"							,   F'{builder.name}-train-00000-of-00002.arrow'						)						)						)
           self.assertTrue(
               os.path.exists(
                   os.path.join(
                       SCREAMING_SNAKE_CASE							,   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"						)}						)						)
           UpperCamelCase__			:   List[Any]       				=  builder.as_dataset()
           self.assertEqual(dset["train"].num_rows							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(dset["train"].info.splits["train"].num_examples							,   SCREAMING_SNAKE_CASE						)
           # 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(SCREAMING_SNAKE_CASE							,   builder.name							,   "default"							,   "0.0.0"							,   "dataset_info.json"						)						)						)
           del dset







 @require_beam
 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           UpperCamelCase__			:   int       				=  DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE						)
           self.assertRaises(datasets.builder.MissingBeamOptions							,   builder.download_and_prepare						)







 @require_beam
 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  len(get_test_nested_examples()						)
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           UpperCamelCase__			:   Any       				=  NestedBeamDataset(cache_dir=SCREAMING_SNAKE_CASE							,   beam_runner="DirectRunner"						)
           builder.download_and_prepare()
           self.assertTrue(
               os.path.exists(
                   os.path.join(SCREAMING_SNAKE_CASE							,   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"						)}						)}						)						)
           UpperCamelCase__			:   List[Any]       				=  builder.as_dataset()
           self.assertEqual(dset["train"].num_rows							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(dset["train"].info.splits["train"].num_examples							,   SCREAMING_SNAKE_CASE						)
           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(SCREAMING_SNAKE_CASE							,   builder.name							,   "default"							,   "0.0.0"							,   "dataset_info.json"						)						)						)
           del dset