





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np

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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

class 		lowercase					(					snake_case_					):
      _SCREAMING_SNAKE_CASE					=      ['input_features', 'attention_mask']





      def __init__(       self      ,						lowercase=80      ,						lowercase=16_000      ,						lowercase=0.0      ,						lowercase=10      ,						lowercase=25      ,						lowercase="hamming_window"      ,						lowercase=32_768.0      ,						lowercase=0.97      ,						lowercase=1.0      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            super().__init__(feature_size=lowercase      ,						sampling_rate=lowercase      ,						padding_value=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       feature_size
            lowerCAmelCase  						=       sampling_rate
            lowerCAmelCase  						=       padding_value
            lowerCAmelCase  						=       hop_length
            lowerCAmelCase  						=       win_length
            lowerCAmelCase  						=       frame_signal_scale
            lowerCAmelCase  						=       preemphasis_coeff
            lowerCAmelCase  						=       mel_floor
            lowerCAmelCase  						=       normalize_means
            lowerCAmelCase  						=       normalize_vars
            lowerCAmelCase  						=       win_function
            lowerCAmelCase  						=       return_attention_mask

            lowerCAmelCase  						=       win_length * sampling_rate // 1_000
            lowerCAmelCase  						=       hop_length * sampling_rate // 1_000

            lowerCAmelCase  						=       optimal_fft_length(self.sample_size )
            lowerCAmelCase  						=       (self.n_fft // 2) + 1





      def     _snake_case			(       self      ,						lowercase )		->     np.ndarray:
            if self.win_function == "hamming_window":
                  lowerCAmelCase  						=       window_function(window_length=self.sample_size      ,						name=self.win_function      ,						periodic=lowercase )
            else:
                  lowerCAmelCase  						=       window_function(window_length=self.sample_size      ,						name=self.win_function )

            lowerCAmelCase  						=       mel_filter_bank(
                num_frequency_bins=self.n_freqs      ,						num_mel_filters=self.feature_size      ,						min_frequency=0.0      ,						max_frequency=self.sampling_rate / 2.0      ,						sampling_rate=self.sampling_rate      ,						)

            lowerCAmelCase  						=       spectrogram(
                one_waveform * self.frame_signal_scale      ,						window=lowercase      ,						frame_length=self.sample_size      ,						hop_length=self.sample_stride      ,						fft_length=self.n_fft      ,						center=lowercase      ,						preemphasis=self.preemphasis_coeff      ,						mel_filters=lowercase      ,						mel_floor=self.mel_floor      ,						log_mel="""log"""      ,						)
            return msfc_features.T





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[str]:
            # make sure we normalize float32 arrays
            if self.normalize_means:
                  lowerCAmelCase  						=       x[:input_length].mean(axis=0 )
                  lowerCAmelCase  						=       np.subtract(lowercase      ,						lowercase )
            if self.normalize_vars:
                  lowerCAmelCase  						=       x[:input_length].std(axis=0 )
                  lowerCAmelCase  						=       np.divide(lowercase      ,						lowercase )

            if input_length < x.shape[0]:
                  lowerCAmelCase  						=       padding_value

            # make sure array is in float32
            lowerCAmelCase  						=       x.astype(np.floataa )

            return x





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[np.ndarray]:
            lowerCAmelCase  						=       attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
            return [self._normalize_one(lowercase      ,						lowercase      ,						self.padding_value ) for x, n in zip(lowercase      ,						lowercase )]





      def __call__(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = None      ,						lowercase = False      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     BatchFeature:

            if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                        raise ValueError(
                            f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                            f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                            f' {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.""" )

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

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

            # always return batch
            if not is_batched:
                  lowerCAmelCase  						=       [raw_speech]

            # extract fbank features
            lowerCAmelCase  						=       [self._extract_mfsc_features(lowercase ) for one_waveform in raw_speech]

            # convert into correct format for padding
            lowerCAmelCase  						=       BatchFeature({"""input_features""": features} )

            lowerCAmelCase  						=       self.pad(
                lowercase      ,						padding=lowercase      ,						max_length=lowercase      ,						truncation=lowercase      ,						pad_to_multiple_of=lowercase      ,						return_attention_mask=lowercase      ,						**lowercase      ,						)
            # make sure list is in array format
            lowerCAmelCase  						=       padded_inputs.get("""input_features""" )
            if isinstance(input_features[0]      ,						lowercase ):
                  lowerCAmelCase  						=       [np.asarray(lowercase      ,						dtype=np.floataa ) for feature in input_features]

            lowerCAmelCase  						=       padded_inputs.get("""attention_mask""" )
            if attention_mask is not None:
                  lowerCAmelCase  						=       [np.asarray(lowercase      ,						dtype=np.intaa ) for array in attention_mask]

            if self.normalize_means or self.normalize_vars:
                  lowerCAmelCase  						=       (
                      np.array(lowercase      ,						dtype=np.intaa )
                      if self._get_padding_strategies(lowercase      ,						max_length=lowercase ) is not PaddingStrategy.DO_NOT_PAD
                      and padding
                      else None
                  )
                  lowerCAmelCase  						=       self.normalize(
                      padded_inputs["""input_features"""]      ,						attention_mask=lowercase )

            if return_tensors is not None:
                  lowerCAmelCase  						=       padded_inputs.convert_to_tensors(lowercase )

            return padded_inputs














import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowercase  							=					"\\n    Text data.\n    Second line of data."

lowercase  							=					"file"





@pytest.fixture(scope='session')
def 						__UpperCAmelCase			(     a_):
							snake_case_				=							tmp_path_factory.mktemp('data') / (FILE_PATH + '.zstd')
							snake_case_				=							bytes(a_							,     'utf-8')
							with zstd.open(a_							,     'wb') as f:
														f.write(a_)
							return path





@pytest.fixture
def 						__UpperCAmelCase			(     a_):
							with open(os.path.join(tmpfs.local_root_dir							,     a_)							,     'w') as f:
														f.write(a_)
							return FILE_PATH





@pytest.mark.parametrize('compression_format'							,     ['gzip', 'xz', 'zstd'])
def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_							,     a_							,     a_):
							snake_case_				=							{'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
							snake_case_				=							input_paths[compression_format]
							snake_case_				=							tmp_path / 'cache'
							snake_case_				=							DownloadConfig(cache_dir=a_							,     extract_compressed_file=a_)
							snake_case_				=							cached_path(a_							,     download_config=a_)
							with open(a_) as f:
														snake_case_				=							f.read()
							with open(a_) as f:
														snake_case_				=							f.read()
							assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('default_extracted'							,     [True, False])
@pytest.mark.parametrize('default_cache_dir'							,     [True, False])
def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_							,     a_):
							snake_case_				=							'custom_cache'
							snake_case_				=							'custom_extracted_dir'
							snake_case_				=							tmp_path / 'custom_extracted_path'
							if default_extracted:
														snake_case_				=							('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
							else:
														monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'							,     a_)
														monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'							,     str(a_))
														snake_case_				=							custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

							snake_case_				=							xz_file
							snake_case_				=							(
							    DownloadConfig(extract_compressed_file=a_)
							    if default_cache_dir
							    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir							,     extract_compressed_file=a_)
							)
							snake_case_				=							cached_path(a_							,     download_config=a_)
							assert Path(a_).parent.parts[-2:] == expected





def 						__UpperCAmelCase			(     a_):
							# absolute path
							snake_case_				=							str(Path(a_).resolve())
							assert cached_path(a_) == text_file
							# relative path
							snake_case_				=							str(Path(a_).resolve().relative_to(Path(os.getcwd())))
							assert cached_path(a_) == text_file





def 						__UpperCAmelCase			(     a_):
							# absolute path
							snake_case_				=							str(tmp_path.resolve() / '__missing_file__.txt')
							with pytest.raises(a_):
														cached_path(a_)
							# relative path
							snake_case_				=							'./__missing_file__.txt'
							with pytest.raises(a_):
														cached_path(a_)





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							get_from_cache(f'''tmp://{tmpfs_file}''')
							with open(a_) as f:
														snake_case_				=							f.read()
							assert output_file_content == FILE_CONTENT





@patch('datasets.config.HF_DATASETS_OFFLINE'							,     a_)
def 						__UpperCAmelCase			(     ):
							with pytest.raises(a_):
														cached_path('https://huggingface.co')





@patch('datasets.config.HF_DATASETS_OFFLINE'							,     a_)
def 						__UpperCAmelCase			(     a_):
							snake_case_				=							tmp_path_factory.mktemp('data') / 'file.html'
							with pytest.raises(a_):
														http_get('https://huggingface.co'							,     temp_file=a_)
							with pytest.raises(a_):
														http_head('https://huggingface.co')





@patch('datasets.config.HF_DATASETS_OFFLINE'							,     a_)
def 						__UpperCAmelCase			(     a_):
							snake_case_				=							tmp_path_factory.mktemp('data') / 'file.html'
							with pytest.raises(a_):
														ftp_get('ftp://huggingface.co'							,     temp_file=a_)
							with pytest.raises(a_):
														ftp_head('ftp://huggingface.co')





@patch('datasets.config.HF_DATASETS_OFFLINE'							,     a_)
def 						__UpperCAmelCase			(     a_):
							snake_case_				=							tmp_path_factory.mktemp('data') / 'file.html'
							with pytest.raises(a_):
														fsspec_get('s3://huggingface.co'							,     temp_file=a_)
							with pytest.raises(a_):
														fsspec_head('s3://huggingface.co')





