



"""simple docstring"""

from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  Optional[int]       =			tau * frequency / samplerate
    snake_case_				:  Any       =			sin(_a					)
    snake_case_				:  List[str]       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)

    snake_case_				:  str       =			(1 - _cos) / 2
    snake_case_				:  Dict       =			1 - _cos

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

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




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  int       =			tau * frequency / samplerate
    snake_case_				:  int       =			sin(_a					)
    snake_case_				:  str       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)

    snake_case_				:  str       =			(1 + _cos) / 2
    snake_case_				:  Optional[int]       =			-1 - _cos

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

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




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  str       =			tau * frequency / samplerate
    snake_case_				:  Any       =			sin(_a					)
    snake_case_				:  Union[str, Any]       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)

    snake_case_				:  List[str]       =			_sin / 2
    snake_case_				:  List[str]       =			0
    snake_case_				:  List[str]       =			-ba

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

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




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  Optional[int]       =			tau * frequency / samplerate
    snake_case_				:  int       =			sin(_a					)
    snake_case_				:  List[Any]       =			cos(_a					)
    snake_case_				:  Optional[Any]       =			_sin / (2 * q_factor)

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

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




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1 / sqrt(2					)	,    ):
    snake_case_				:  Tuple       =			tau * frequency / samplerate
    snake_case_				:  Optional[int]       =			sin(_a					)
    snake_case_				:  Union[str, Any]       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)
    snake_case_				:  Dict       =			10 ** (gain_db / 40)

    snake_case_				:  List[Any]       =			1 + alpha * big_a
    snake_case_				:  Union[str, Any]       =			-2 * _cos
    snake_case_				:  Optional[Any]       =			1 - alpha * big_a
    snake_case_				:  Tuple       =			1 + alpha / big_a
    snake_case_				:  List[Any]       =			-2 * _cos
    snake_case_				:  Dict       =			1 - alpha / big_a

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




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1 / sqrt(2					)	,    ):
    snake_case_				:  Dict       =			tau * frequency / samplerate
    snake_case_				:  Optional[int]       =			sin(_a					)
    snake_case_				:  Optional[Any]       =			cos(_a					)
    snake_case_				:  str       =			_sin / (2 * q_factor)
    snake_case_				:  str       =			10 ** (gain_db / 40)
    snake_case_				:  List[str]       =			(big_a + 1) - (big_a - 1) * _cos
    snake_case_				:  Dict       =			(big_a + 1) + (big_a - 1) * _cos
    snake_case_				:  Union[str, Any]       =			(big_a - 1) - (big_a + 1) * _cos
    snake_case_				:  List[str]       =			(big_a - 1) + (big_a + 1) * _cos
    snake_case_				:  int       =			2 * sqrt(_a					) * alpha

    snake_case_				:  Dict       =			big_a * (pmc + aaa)
    snake_case_				:  int       =			2 * big_a * mpc
    snake_case_				:  int       =			big_a * (pmc - aaa)
    snake_case_				:  Union[str, Any]       =			ppmc + aaa
    snake_case_				:  Optional[int]       =			-2 * pmpc
    snake_case_				:  Optional[Any]       =			ppmc - aaa

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






def __lowercase    (							_a	,    _a	,    _a	,    _a = 1 / sqrt(2					)	,    ):
    snake_case_				:  int       =			tau * frequency / samplerate
    snake_case_				:  Any       =			sin(_a					)
    snake_case_				:  Union[str, Any]       =			cos(_a					)
    snake_case_				:  int       =			_sin / (2 * q_factor)
    snake_case_				:  Union[str, Any]       =			10 ** (gain_db / 40)
    snake_case_				:  int       =			(big_a + 1) - (big_a - 1) * _cos
    snake_case_				:  Optional[int]       =			(big_a + 1) + (big_a - 1) * _cos
    snake_case_				:  Dict       =			(big_a - 1) - (big_a + 1) * _cos
    snake_case_				:  Optional[int]       =			(big_a - 1) + (big_a + 1) * _cos
    snake_case_				:  List[Any]       =			2 * sqrt(_a					) * alpha

    snake_case_				:  str       =			big_a * (ppmc + aaa)
    snake_case_				:  Optional[int]       =			-2 * big_a * pmpc
    snake_case_				:  str       =			big_a * (ppmc - aaa)
    snake_case_				:  List[str]       =			pmc + aaa
    snake_case_				:  List[str]       =			2 * mpc
    snake_case_				:  str       =			pmc - aaa

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










"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowercase__					:						Tuple												=			get_tests_dir('''fixtures''')
lowercase__					:						List[str]												=			get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
lowercase__					:						Dict												=			get_tests_dir('''fixtures/dummy-config.json''')




class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[Any]       =			0

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int       ):
     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_				:  List[Any]       =			WavaVecaConfig()

         # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
         snake_case_				:  Optional[Any]       =			AutoFeatureExtractor.from_pretrained(lowercase_       ).to_dict()

         config_dict.pop('''feature_extractor_type'''       )
         snake_case_				:  Optional[Any]       =			WavaVecaFeatureExtractor(**lowercase_       )

         # save in new folder
         model_config.save_pretrained(lowercase_       )
         config.save_pretrained(lowercase_       )

         snake_case_				:  List[Any]       =			AutoFeatureExtractor.from_pretrained(lowercase_       )

         # make sure private variable is not incorrectly saved
         snake_case_				:  Dict       =			json.loads(config.to_json_string()       )
         self.assertTrue('''_processor_class''' not in dict_as_saved       )

     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     with self.assertRaisesRegex(
         lowercase_						,  '''bert-base is not a local folder and is not a valid model identifier'''       ):
         snake_case_				:  List[str]       =			AutoFeatureExtractor.from_pretrained('''bert-base'''       )

 def 		_snake_case      (  self	: Any       ):
     with self.assertRaisesRegex(
         lowercase_						,  r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''       ):
         snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained(lowercase_						,  revision='''aaaaaa'''       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     with self.assertRaisesRegex(
         lowercase_						,  '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''						,  ):
         snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''       )

 def 		_snake_case      (  self	: Optional[int]       ):
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(lowercase_       ):
         snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''       )
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(lowercase_       ):
         snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )

     snake_case_				:  List[str]       =			AutoFeatureExtractor.from_pretrained(
         '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )
     self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(lowercase_       )
         snake_case_				:  Optional[int]       =			AutoFeatureExtractor.from_pretrained(lowercase_						,  trust_remote_code=lowercase_       )
     self.assertEqual(reloaded_feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     try:
         AutoConfig.register('''custom'''						,  lowercase_       )
         AutoFeatureExtractor.register(lowercase_						,  lowercase_       )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(lowercase_       ):
             AutoFeatureExtractor.register(lowercase_						,  lowercase_       )

         # Now that the config is registered, it can be used as any other config with the auto-API
         snake_case_				:  List[Any]       =			CustomFeatureExtractor.from_pretrained(lowercase_       )
         with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(lowercase_       )
             snake_case_				:  Any       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
             self.assertIsInstance(lowercase_						,  lowercase_       )

     finally:
         if "custom" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
             del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






 def 		_snake_case      (  self	: Union[str, Any]       ):




     class  _UpperCAmelCase (    lowerCAmelCase__):
      _lowerCAmelCase     :    str	      =	True

     try:
         AutoConfig.register('''custom'''						,  lowercase_       )
         AutoFeatureExtractor.register(lowercase_						,  lowercase_       )
         # If remote code is not set, the default is to use local
         snake_case_				:  List[Any]       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''       )
         self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )
         self.assertTrue(feature_extractor.is_local       )

         # If remote code is disabled, we load the local one.
         snake_case_				:  Union[str, Any]       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )
         self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )
         self.assertTrue(feature_extractor.is_local       )

         # If remote is enabled, we load from the Hub
         snake_case_				:  Tuple       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )
         self.assertEqual(feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )
         self.assertTrue(not hasattr(lowercase_						,  '''is_local'''       )       )

     finally:
         if "custom" in CONFIG_MAPPING._extra_content:
             del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
             del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





