


'''simple docstring'''






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,
)


a_   :      Tuple				    =   "\\n    Text data.\n    Second line of data."

a_   :      List[Any]				    =   "file"






@pytest.fixture(scope='session'						)
def 		_A			(lowerCAmelCase__					:Optional[int]						)				->					Optional[int]:




     '''simple docstring'''



     _a      =							tmp_path_factory.mktemp('data'						) / (FILE_PATH + '.zstd')
     _a      =							bytes(lowerCAmelCase__			,  'utf-8'						)
     with zstd.open(lowerCAmelCase__			,  'wb'						) as f:
          f.write(lowerCAmelCase__						)
     return path






@pytest.fixture
def 		_A			(lowerCAmelCase__					:List[Any]						)				->					Any:




     '''simple docstring'''



     with open(os.path.join(tmpfs.local_root_dir			,  lowerCAmelCase__						)			,  'w'						) as f:
          f.write(lowerCAmelCase__						)
     return FILE_PATH






@pytest.mark.parametrize('compression_format'			,  ['gzip', 'xz', 'zstd']						)
def 		_A			(lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:str			,  lowerCAmelCase__					:Tuple						)				->					Dict:




     '''simple docstring'''



     _a      =							{'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
     _a      =							input_paths[compression_format]
     _a      =							tmp_path / 'cache'
     _a      =							DownloadConfig(cache_dir=lowerCAmelCase__			,  extract_compressed_file=lowerCAmelCase__						)
     _a      =							cached_path(lowerCAmelCase__			,  download_config=lowerCAmelCase__						)
     with open(lowerCAmelCase__						) as f:
          _a      =							f.read()
     with open(lowerCAmelCase__						) as f:
          _a      =							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 		_A			(lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:Tuple						)				->					Optional[Any]:




     '''simple docstring'''



     _a      =							'custom_cache'
     _a      =							'custom_extracted_dir'
     _a      =							tmp_path / 'custom_extracted_path'
     if default_extracted:
          _a      =							('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
     else:
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'			,  lowerCAmelCase__						)
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'			,  str(lowerCAmelCase__						)						)
          _a      =							custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

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






def 		_A			(lowerCAmelCase__					:int						)				->					Optional[Any]:




     '''simple docstring'''



     _a      =							str(Path(lowerCAmelCase__						).resolve()						)
     assert cached_path(lowerCAmelCase__						) == text_file
     # relative path
     _a      =							str(Path(lowerCAmelCase__						).resolve().relative_to(Path(os.getcwd()						)						)						)
     assert cached_path(lowerCAmelCase__						) == text_file






def 		_A			(lowerCAmelCase__					:List[Any]						)				->					Dict:




     '''simple docstring'''



     _a      =							str(tmp_path.resolve() / '__missing_file__.txt'						)
     with pytest.raises(lowerCAmelCase__						):
          cached_path(lowerCAmelCase__						)
     # relative path
     _a      =							'./__missing_file__.txt'
     with pytest.raises(lowerCAmelCase__						):
          cached_path(lowerCAmelCase__						)






def 		_A			(lowerCAmelCase__					:List[Any]						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							get_from_cache(f'tmp://{tmpfs_file}'						)
     with open(lowerCAmelCase__						) as f:
          _a      =							f.read()
     assert output_file_content == FILE_CONTENT






@patch('datasets.config.HF_DATASETS_OFFLINE'			,  lowerCAmelCase__						)
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     with pytest.raises(lowerCAmelCase__						):
          cached_path('https://huggingface.co'						)






@patch('datasets.config.HF_DATASETS_OFFLINE'			,  lowerCAmelCase__						)
def 		_A			(lowerCAmelCase__					:List[str]						)				->					int:




     '''simple docstring'''



     _a      =							tmp_path_factory.mktemp('data'						) / 'file.html'
     with pytest.raises(lowerCAmelCase__						):
          http_get('https://huggingface.co'			,  temp_file=lowerCAmelCase__						)
     with pytest.raises(lowerCAmelCase__						):
          http_head('https://huggingface.co'						)






@patch('datasets.config.HF_DATASETS_OFFLINE'			,  lowerCAmelCase__						)
def 		_A			(lowerCAmelCase__					:Tuple						)				->					List[Any]:




     '''simple docstring'''



     _a      =							tmp_path_factory.mktemp('data'						) / 'file.html'
     with pytest.raises(lowerCAmelCase__						):
          ftp_get('ftp://huggingface.co'			,  temp_file=lowerCAmelCase__						)
     with pytest.raises(lowerCAmelCase__						):
          ftp_head('ftp://huggingface.co'						)






@patch('datasets.config.HF_DATASETS_OFFLINE'			,  lowerCAmelCase__						)
def 		_A			(lowerCAmelCase__					:Any						)				->					int:




     '''simple docstring'''



     _a      =							tmp_path_factory.mktemp('data'						) / 'file.html'
     with pytest.raises(lowerCAmelCase__						):
          fsspec_get('s3://huggingface.co'			,  temp_file=lowerCAmelCase__						)
     with pytest.raises(lowerCAmelCase__						):
          fsspec_head('s3://huggingface.co'						)





'''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


a_   :      Tuple				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""input_features"""]
 def __init__(	self ,			__magic_name__=80 ,			__magic_name__=1_60_00 ,			__magic_name__=1_60 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=0.0 ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(
          feature_size=__magic_name__ ,			sampling_rate=__magic_name__ ,			padding_value=__magic_name__ ,			return_attention_mask=__magic_name__ ,			**__magic_name__ ,			)
      _a      =							n_fft
      _a      =							hop_length
      _a      =							chunk_length
      _a      =							chunk_length * sampling_rate
      _a      =							self.n_samples // hop_length
      _a      =							sampling_rate
      _a      =							mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=__magic_name__ ,			min_frequency=0.0 ,			max_frequency=8_0_0_0.0 ,			sampling_rate=__magic_name__ ,			norm='slaney' ,			mel_scale='slaney' ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      _a      =							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' ,			)
      _a      =							log_spec[:, :-1]
      _a      =							np.maximum(__magic_name__ ,			log_spec.max() - 8.0  )
      _a      =							(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 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__ = 0.0  )   ->							List[np.ndarray]:
      if attention_mask is not None:
           _a      =							np.array(__magic_name__ ,			np.intaa  )
           _a      =							[]

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

                normed_input_values.append(__magic_name__  )
      else:
           _a      =							[(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:

      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.'  )

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

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

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

      _a      =							BatchFeature({'input_features': raw_speech}  )

      # convert into correct format for padding

      _a      =							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:
           _a      =							self.zero_mean_unit_var_norm(
               padded_inputs['input_features'] ,			attention_mask=padded_inputs['attention_mask'] ,			padding_value=self.padding_value ,			)
           _a      =							np.stack(padded_inputs['input_features'] ,			axis=0  )

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

      _a      =							[self._np_extract_fbank_features(__magic_name__  ) for waveform in input_features[0]]

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

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

      if return_tensors is not None:
           _a      =							padded_inputs.convert_to_tensors(__magic_name__  )

      return padded_inputs




 def 							__UpperCAmelCase						(	self  )   ->							Dict[str, Any]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.__class__.__name__
      if "mel_filters" in output:
           del output["mel_filters"]
      return output

