





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase_    =				logging.get_logger(__name__)



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     int       			=      ['''input_features''']






			def __init__(self					,							__magic_name__=80					,							__magic_name__=1_6000					,							__magic_name__=160					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=0.0					,							__magic_name__=False					,							**__magic_name__					,							)     ->   Optional[int]:


							'''simple docstring'''



							super().__init__(
							    feature_size=__magic_name__					,							sampling_rate=__magic_name__					,							padding_value=__magic_name__					,							return_attention_mask=__magic_name__					,							**__magic_name__					,							)
							snake_case_						:    Union[str, Any]				=  n_fft
							snake_case_						:    str				=  hop_length
							snake_case_						:    List[str]				=  chunk_length
							snake_case_						:    List[Any]				=  chunk_length * sampling_rate
							snake_case_						:    Optional[int]				=  self.n_samples // hop_length
							snake_case_						:    Optional[Any]				=  sampling_rate
							snake_case_						:    List[Any]				=  mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2					,							num_mel_filters=__magic_name__					,							min_frequency=0.0					,							max_frequency=8_000.0					,							sampling_rate=__magic_name__					,							norm='''slaney'''					,							mel_scale='''slaney'''					,							)






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   np.ndarray:


							'''simple docstring'''



							snake_case_						:    Dict				=  spectrogram(
							    __magic_name__					,							window_function(self.n_fft					,							'''hann'''    )					,							frame_length=self.n_fft					,							hop_length=self.hop_length					,							power=2.0					,							mel_filters=self.mel_filters					,							log_mel='''log10'''					,							)
							snake_case_						:    Any				=  log_spec[:, :-1]
							snake_case_						:    Optional[Any]				=  np.maximum(__magic_name__					,							log_spec.max() - 8.0    )
							snake_case_						:    Dict				=  (log_spec + 4.0) / 4.0
							return log_spec






			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__					,							__magic_name__ = 0.0    )     ->   List[np.ndarray]:


							'''simple docstring'''



							if attention_mask is not None:
											snake_case_						:    Union[str, Any]				=  np.array(__magic_name__					,							np.intaa    )
											snake_case_						:    Tuple				=  []

											for vector, length in zip(__magic_name__					,							attention_mask.sum(-1    )    ):
															snake_case_						:    str				=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
															if length < normed_slice.shape[0]:
																			snake_case_						:    List[str]				=  padding_value

															normed_input_values.append(__magic_name__    )
							else:
											snake_case_						:    Union[str, Any]				=  [(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

							return normed_input_values






			def __call__(self					,							__magic_name__					,							__magic_name__ = True					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = "max_length"					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   BatchFeature:


							'''simple docstring'''



							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
															    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
															    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''    )
							else:
											logger.warning(
											    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
											    '''Failing to do so can result in silent errors that might be hard to debug.'''    )

							snake_case_						:    List[str]				=  isinstance(__magic_name__					,							np.ndarray    ) and len(raw_speech.shape    ) > 1
							if is_batched_numpy and len(raw_speech.shape    ) > 2:
											raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''    )
							snake_case_						:    Union[str, Any]				=  is_batched_numpy or (
							    isinstance(__magic_name__					,							(list, tuple)    ) and (isinstance(raw_speech[0]					,							(np.ndarray, tuple, list)    ))
							)

							if is_batched:
											snake_case_						:    Tuple				=  [np.asarray([speech]					,							dtype=np.floataa    ).T for speech in raw_speech]
							elif not is_batched and not isinstance(__magic_name__					,							np.ndarray    ):
											snake_case_						:    int				=  np.asarray(__magic_name__					,							dtype=np.floataa    )
							elif isinstance(__magic_name__					,							np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
											snake_case_						:    Optional[int]				=  raw_speech.astype(np.floataa    )

							# always return batch
							if not is_batched:
											snake_case_						:    Optional[Any]				=  [np.asarray([raw_speech]    ).T]

							snake_case_						:    int				=  BatchFeature({'''input_features''': raw_speech}    )

							# convert into correct format for padding

							snake_case_						:    str				=  self.pad(
							    __magic_name__					,							padding=__magic_name__					,							max_length=max_length if max_length else self.n_samples					,							truncation=__magic_name__					,							pad_to_multiple_of=__magic_name__					,							return_attention_mask=return_attention_mask or do_normalize					,							)

							# zero-mean and unit-variance normalization
							if do_normalize:
											snake_case_						:    int				=  self.zero_mean_unit_var_norm(
											    padded_inputs['''input_features''']					,							attention_mask=padded_inputs['''attention_mask''']					,							padding_value=self.padding_value					,							)
											snake_case_						:    List[Any]				=  np.stack(padded_inputs['''input_features''']					,							axis=0    )

							# make sure list is in array format
							snake_case_						:    Union[str, Any]				=  padded_inputs.get('''input_features'''    ).transpose(2					,							0					,							1    )

							snake_case_						:    Optional[int]				=  [self._np_extract_fbank_features(__magic_name__    ) for waveform in input_features[0]]

							if isinstance(input_features[0]					,							__magic_name__    ):
											snake_case_						:    int				=  [np.asarray(__magic_name__					,							dtype=np.floataa    ) for feature in input_features]
							else:
											snake_case_						:    str				=  input_features

							if return_attention_mask:
											# rescale from sample (48000) to feature (3000)
											snake_case_						:    Optional[Any]				=  padded_inputs['''attention_mask'''][:, :: self.hop_length]

							if return_tensors is not None:
											snake_case_						:    Optional[int]				=  padded_inputs.convert_to_tensors(__magic_name__    )

							return padded_inputs






			def 			lowerCamelCase  (self    )     ->   Dict[str, Any]:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  copy.deepcopy(self.__dict__    )
							snake_case_						:    Tuple				=  self.__class__.__name__
							if "mel_filters" in output:
											del output["mel_filters"]
							return output







import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

lowerCAmelCase_    =				(
    '''4S 3H 2C 7S 5H''',
    '''9D 8H 2C 6S 7H''',
    '''2D 6D 9D TH 7D''',
    '''TC 8C 2S JH 6C''',
    '''JH 8S TH AH QH''',
    '''TS KS 5S 9S AC''',
    '''KD 6S 9D TH AD''',
    '''KS 8D 4D 9S 4S''',  # pair
    '''8C 4S KH JS 4D''',  # pair
    '''QH 8H KD JH 8S''',  # pair
    '''KC 4H KS 2H 8D''',  # pair
    '''KD 4S KC 3H 8S''',  # pair
    '''AH 8S AS KC JH''',  # pair
    '''3H 4C 4H 3S 2H''',  # 2 pairs
    '''5S 5D 2C KH KH''',  # 2 pairs
    '''3C KH 5D 5S KH''',  # 2 pairs
    '''AS 3C KH AD KH''',  # 2 pairs
    '''7C 7S 3S 7H 5S''',  # 3 of a kind
    '''7C 7S KH 2H 7H''',  # 3 of a kind
    '''AC KH QH AH AS''',  # 3 of a kind
    '''2H 4D 3C AS 5S''',  # straight (low ace)
    '''3C 5C 4C 2C 6H''',  # straight
    '''6S 8S 7S 5H 9H''',  # straight
    '''JS QS 9H TS KH''',  # straight
    '''QC KH TS JS AH''',  # straight (high ace)
    '''8C 9C 5C 3C TC''',  # flush
    '''3S 8S 9S 5S KS''',  # flush
    '''4C 5C 9C 8C KC''',  # flush
    '''JH 8H AH KH QH''',  # flush
    '''3D 2H 3H 2C 2D''',  # full house
    '''2H 2C 3S 3H 3D''',  # full house
    '''KH KC 3S 3H 3D''',  # full house
    '''JC 6H JS JD JH''',  # 4 of a kind
    '''JC 7H JS JD JH''',  # 4 of a kind
    '''JC KH JS JD JH''',  # 4 of a kind
    '''2S AS 4S 5S 3S''',  # straight flush (low ace)
    '''2D 6D 3D 4D 5D''',  # straight flush
    '''5C 6C 3C 7C 4C''',  # straight flush
    '''JH 9H TH KH QH''',  # straight flush
    '''JH AH TH KH QH''',  # royal flush (high ace straight flush)
)

lowerCAmelCase_    =				(
    ('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
    ('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
    ('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
    ('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
    ('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
    ('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
    ('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
    ('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
    ('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
    ('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
    ('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
    ('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
    ('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
    ('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
    ('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
    ('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
    ('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
    ('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
    ('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
    ('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
    ('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
    ('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
    ('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
    ('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
    ('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
    ('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
    ('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)

lowerCAmelCase_    =				(
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', True),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', False),
    ('''AS 3S 4S 8S 2S''', True),
)

lowerCAmelCase_    =				(
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', False),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', True),
)

lowerCAmelCase_    =				(
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
    ('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

lowerCAmelCase_    =				(
    ('''JH AH TH KH QH''', 0),
    ('''JH 9H TH KH QH''', 0),
    ('''JC KH JS JD JH''', 7),
    ('''KH KC 3S 3H 3D''', 6),
    ('''8C 9C 5C 3C TC''', 0),
    ('''JS QS 9H TS KH''', 0),
    ('''7C 7S KH 2H 7H''', 3),
    ('''3C KH 5D 5S KH''', 2),
    ('''QH 8H KD JH 8S''', 1),
    ('''2D 6D 9D TH 7D''', 0),
)

lowerCAmelCase_    =				(
    ('''JH AH TH KH QH''', 2_3),
    ('''JH 9H TH KH QH''', 2_2),
    ('''JC KH JS JD JH''', 2_1),
    ('''KH KC 3S 3H 3D''', 2_0),
    ('''8C 9C 5C 3C TC''', 1_9),
    ('''JS QS 9H TS KH''', 1_8),
    ('''7C 7S KH 2H 7H''', 1_7),
    ('''3C KH 5D 5S KH''', 1_6),
    ('''QH 8H KD JH 8S''', 1_5),
    ('''2D 6D 9D TH 7D''', 1_4),
)






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


				snake_case_			,							snake_case_						:    Any				=  randrange(len(_UpperCamelCase ) ), randrange(len(_UpperCamelCase ) )
				snake_case_						:    Any				=  ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
				snake_case_			,							snake_case_						:    Tuple				=  SORTED_HANDS[play], SORTED_HANDS[oppo]
				return hand, other, expected






def      lowerCamelCase_						(				_UpperCamelCase = 100 )		->						str:






				"""simple docstring"""


				return (generate_random_hand() for _ in range(_UpperCamelCase ))






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._is_flush() == expected






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._is_straight() == expected






@pytest.mark.parametrize('''hand, expected, card_values''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    str				=  PokerHand(_UpperCamelCase )
				assert player._is_five_high_straight() == expected
				assert player._card_values == card_values






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._is_same_kind() == expected






@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._hand_type == expected






@pytest.mark.parametrize('''hand, other, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Any:






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected






@pytest.mark.parametrize('''hand, other, expected''' ,				generate_random_hands() )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[Any]:






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected






def      lowerCamelCase_						(				)		->						str:






				"""simple docstring"""


				snake_case_						:    Dict				=  [PokerHand(_UpperCamelCase ) for hand in SORTED_HANDS]
				snake_case_						:    str				=  poker_hands.copy()
				shuffle(_UpperCamelCase )
				snake_case_						:    List[str]				=  chain(sorted(_UpperCamelCase ) )
				for index, hand in enumerate(_UpperCamelCase ):
								assert hand == poker_hands[index]






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  [PokerHand('''2D AC 3H 4H 5S''' ), PokerHand('''2S 3H 4H 5S 6C''' )]
				pokerhands.sort(reverse=_UpperCamelCase )
				assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"






def      lowerCamelCase_						(				)		->						str:






				"""simple docstring"""


				snake_case_						:    Dict				=  PokerHand('''2C 4S AS 3D 5C''' )
				snake_case_						:    str				=  True
				snake_case_						:    Tuple				=  [5, 4, 3, 2, 14]
				for _ in range(10 ):
								assert pokerhand._is_five_high_straight() == expected
								assert pokerhand._card_values == expected_card_values






def      lowerCamelCase_						(				)		->						List[str]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  0
				snake_case_						:    Union[str, Any]				=  os.path.abspath(os.path.dirname(_UpperCamelCase ) )
				snake_case_						:    Dict				=  os.path.join(_UpperCamelCase ,				'''poker_hands.txt''' )
				with open(_UpperCamelCase ) as file_hand:
								for line in file_hand:
												snake_case_						:    Dict				=  line[:14].strip()
												snake_case_						:    List[str]				=  line[15:].strip()
												snake_case_			,							snake_case_						:    str				=  PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
												snake_case_						:    int				=  player.compare_with(_UpperCamelCase )
												if output == "Win":
																answer += 1
				assert answer == 376
