


"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)





class    lowerCamelCase__						(		_a    ):
						a :     Optional[Any]											=		["""input_features"""]
						def __init__(		self	:	Optional[Any]      ,       A_	:	List[Any]=8_0      ,       A_	:	Any=1_6_0_0_0      ,       A_	:	Any=1_6_0      ,       A_	:	int=3_0      ,       A_	:	Union[str, Any]=4_0_0      ,       A_	:	Tuple=0.0      ,       A_	:	Any=False      ,       **A_	:	List[str]      ,       ):






							'''simple docstring'''


							super().__init__(
							    feature_size=A_      ,       sampling_rate=A_      ,       padding_value=A_      ,       return_attention_mask=A_      ,       **A_      ,       )
							__lowercase									=	n_fft
							__lowercase									=	hop_length
							__lowercase									=	chunk_length
							__lowercase									=	chunk_length * sampling_rate
							__lowercase									=	self.n_samples // hop_length
							__lowercase									=	sampling_rate
							__lowercase									=	mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2      ,       num_mel_filters=A_      ,       min_frequency=0.0      ,       max_frequency=8_0_0_0.0      ,       sampling_rate=A_      ,       norm="""slaney"""      ,       mel_scale="""slaney"""      ,       )
						def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	np.array							):






							'''simple docstring'''


							__lowercase									=	spectrogram(
							    A_      ,       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"""      ,       )
							__lowercase									=	log_spec[:, :-1]
							__lowercase									=	np.maximum(A_      ,       log_spec.max() - 8.0							)
							__lowercase									=	(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 SCREAMING_SNAKE_CASE_	(		A_	:	List[np.ndarray]      ,       A_	:	List[np.ndarray]      ,       A_	:	float = 0.0							):






							'''simple docstring'''


							if attention_mask is not None:
								__lowercase									=	np.array(A_      ,       np.intaa							)
								__lowercase									=	[]

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

									normed_input_values.append(A_							)
							else:
								__lowercase									=	[(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

							return normed_input_values
						def __call__(		self	:	Any      ,       A_	:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,       A_	:	bool = True      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[str] = "max_length"      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[bool] = None      ,       **A_	:	Dict      ,       ):






							'''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."""							)

							__lowercase									=	isinstance(A_      ,       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}'''							)
							__lowercase									=	is_batched_numpy or (
							    isinstance(A_      ,       (list, tuple)							) and (isinstance(raw_speech[0]      ,       (np.ndarray, tuple, list)							))
							)

							if is_batched:
								__lowercase									=	[np.asarray([speech]      ,       dtype=np.floataa							).T for speech in raw_speech]
							elif not is_batched and not isinstance(A_      ,       np.ndarray							):
								__lowercase									=	np.asarray(A_      ,       dtype=np.floataa							)
							elif isinstance(A_      ,       np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
								__lowercase									=	raw_speech.astype(np.floataa							)

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

							__lowercase									=	BatchFeature({"""input_features""": raw_speech}							)

							# convert into correct format for padding

							__lowercase									=	self.pad(
							    A_      ,       padding=A_      ,       max_length=max_length if max_length else self.n_samples      ,       truncation=A_      ,       pad_to_multiple_of=A_      ,       return_attention_mask=return_attention_mask or do_normalize      ,       )

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

							# make sure list is in array format
							__lowercase									=	padded_inputs.get("""input_features"""							).transpose(2      ,       0      ,       1							)

							__lowercase									=	[self._np_extract_fbank_features(A_							) for waveform in input_features[0]]

							if isinstance(input_features[0]      ,       A_							):
								__lowercase									=	[np.asarray(A_      ,       dtype=np.floataa							) for feature in input_features]
							else:
								__lowercase									=	input_features

							if return_attention_mask:
								# rescale from sample (48000) to feature (3000)
								__lowercase									=	padded_inputs["""attention_mask"""][:, :: self.hop_length]

							if return_tensors is not None:
								__lowercase									=	padded_inputs.convert_to_tensors(A_							)

							return padded_inputs






						def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






							'''simple docstring'''


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




"""simple docstring"""

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def lowerCAmelCase_      (				UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 assert isinstance(UpperCamelCase__				,   UpperCamelCase__ )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""				,   [False, True] )
def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__				,   keep_in_memory=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize(
    """features"""				,   [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,   )
def lowerCAmelCase_      (				UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	features.copy() if features else default_expected_features
 __lowercase									=	(
     Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
 )
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   features=UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize("""split"""				,   [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Tuple ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__				,   split=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )
 assert dataset.split == split if split else "train"


@pytest.mark.parametrize("""path_type"""				,   [str, list] )
def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 if issubclass(UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	text_path
 elif issubclass(UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	[text_path]
 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_dataset(UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any=("train",) ):






 """simple docstring"""







 assert isinstance(UpperCamelCase__				,   UpperCamelCase__ )
 for split in splits:
  __lowercase									=	dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""				,   [False, True] )
def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __lowercase									=	TextDatasetReader({"""train""": text_path}				,   cache_dir=UpperCamelCase__				,   keep_in_memory=UpperCamelCase__ ).read()
 _check_text_datasetdict(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize(
    """features"""				,   [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,   )
def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







 __lowercase									=	tmp_path / """cache"""
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	features.copy() if features else default_expected_features
 __lowercase									=	(
     Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
 )
 __lowercase									=	TextDatasetReader({"""train""": text_path}				,   features=UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_datasetdict(UpperCamelCase__				,   UpperCamelCase__ )


@pytest.mark.parametrize("""split"""				,   [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: int				,   UpperCamelCase__	: int ):






 """simple docstring"""







 if split:
  __lowercase									=	{split: text_path}
 else:
  __lowercase									=	"""train"""
  __lowercase									=	{"""train""": text_path, """test""": text_path}
 __lowercase									=	tmp_path / """cache"""
 __lowercase									=	{"""text""": """string"""}
 __lowercase									=	TextDatasetReader(UpperCamelCase__				,   cache_dir=UpperCamelCase__ ).read()
 _check_text_datasetdict(UpperCamelCase__				,   UpperCamelCase__				,   splits=list(path.keys() ) )
 assert all(dataset[split].split == split for split in path.keys() )
