




'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      assert isinstance(__UpperCamelCase      ,__UpperCamelCase    )
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'      ,[False, True]    )
def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      Tuple    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE   :      Tuple    					=						ParquetDatasetReader(__UpperCamelCase      ,cache_dir=__UpperCamelCase      ,keep_in_memory=__UpperCamelCase    ).read()
      _check_parquet_dataset(__UpperCamelCase      ,__UpperCamelCase    )




@pytest.mark.parametrize(
    'features'      ,[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,)
def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE   :      str    					=						(
          Features({feature: Value(__UpperCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
      )
      SCREAMING_SNAKE_CASE   :      int    					=						ParquetDatasetReader(__UpperCamelCase      ,features=__UpperCamelCase      ,cache_dir=__UpperCamelCase    ).read()
      _check_parquet_dataset(__UpperCamelCase      ,__UpperCamelCase    )




@pytest.mark.parametrize('split'      ,[None, NamedSplit('train'    ), 'train', 'test']    )
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      List[str]    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE   :      str    					=						ParquetDatasetReader(__UpperCamelCase      ,cache_dir=__UpperCamelCase      ,split=__UpperCamelCase    ).read()
      _check_parquet_dataset(__UpperCamelCase      ,__UpperCamelCase    )
      assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'      ,[str, list]    )
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if issubclass(__UpperCamelCase      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      int    					=						parquet_path
      elif issubclass(__UpperCamelCase      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Dict    					=						[parquet_path]
      SCREAMING_SNAKE_CASE   :      Tuple    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      Dict    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE   :      List[str]    					=						ParquetDatasetReader(__UpperCamelCase      ,cache_dir=__UpperCamelCase    ).read()
      _check_parquet_dataset(__UpperCamelCase      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					str=("train",)    ):

      """simple docstring"""






      assert isinstance(__UpperCamelCase      ,__UpperCamelCase    )
      for split in splits:
            SCREAMING_SNAKE_CASE   :      Dict    					=						dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 3
            assert dataset.column_names == ["col_1", "col_2", "col_3"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'      ,[False, True]    )
def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ParquetDatasetReader(
                {'train': parquet_path}      ,cache_dir=__UpperCamelCase      ,keep_in_memory=__UpperCamelCase    ).read()
      _check_parquet_datasetdict(__UpperCamelCase      ,__UpperCamelCase    )




@pytest.mark.parametrize(
    'features'      ,[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,)
def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      int    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE   :      int    					=						features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						(
          Features({feature: Value(__UpperCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
      )
      SCREAMING_SNAKE_CASE   :      List[str]    					=						ParquetDatasetReader({'train': parquet_path}      ,features=__UpperCamelCase      ,cache_dir=__UpperCamelCase    ).read()
      _check_parquet_datasetdict(__UpperCamelCase      ,__UpperCamelCase    )




@pytest.mark.parametrize('split'      ,[None, NamedSplit('train'    ), 'train', 'test']    )
def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      if split:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{split: parquet_path}
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						'train'
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						{'train': parquet_path, 'test': parquet_path}
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						tmp_path / 'cache'
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE   :      Tuple    					=						ParquetDatasetReader(__UpperCamelCase      ,cache_dir=__UpperCamelCase    ).read()
      _check_parquet_datasetdict(__UpperCamelCase      ,__UpperCamelCase      ,splits=list(path.keys()    )    )
      assert all(dataset[split].split == split for split in path.keys()    )




def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						ParquetDatasetWriter(__UpperCamelCase      ,tmp_path / 'foo.parquet'    )
      assert writer.write() > 0
      SCREAMING_SNAKE_CASE   :      Any    					=						pq.ParquetFile(tmp_path / 'foo.parquet'    )
      SCREAMING_SNAKE_CASE   :      List[str]    					=						pf.read()
      assert dataset.data.table == output_table




def   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						str(shared_datadir / 'test_image_rgb.jpg'    )
      SCREAMING_SNAKE_CASE   :      Any    					=						{'image': [image_path]}
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Features({'image': Image()}    )
      SCREAMING_SNAKE_CASE   :      Any    					=						Dataset.from_dict(__UpperCamelCase      ,features=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						ParquetDatasetWriter(__UpperCamelCase      ,tmp_path / 'foo.parquet'    )
      assert writer.write() > 0

      SCREAMING_SNAKE_CASE   :      int    					=						Dataset.from_parquet(str(tmp_path / 'foo.parquet'    )    )
      assert dataset.features == reloaded_dataset.features

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						ParquetDatasetReader(str(tmp_path / 'foo.parquet'    )      ,streaming=__UpperCamelCase    ).read()
      assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    'feature, expected'      ,[
        (Features({'foo': Value('int32'    )}    ), None),
        (Features({'image': Image(), 'foo': Value('int32'    )}    ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()    )}    ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]      ,)
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      assert get_writer_batch_size(__UpperCamelCase    ) == expected








'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						['''audio_values''', '''audio_mask''']







       def __init__(      self,							A=2_048,							A=1,							A=[16, 16],							A=128,							A=44_100,							A=86,							A=2_048,							A=0.0,							**A,							):







             '''simple docstring'''







             super().__init__(
                 feature_size=A,							sampling_rate=A,							padding_value=A,							**A,							)

             SCREAMING_SNAKE_CASE   :      str    					=						spectrogram_length
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feature_size // self.patch_size[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						n_fft
             SCREAMING_SNAKE_CASE   :      Tuple    					=						sampling_rate // hop_length_to_sampling_rate
             SCREAMING_SNAKE_CASE   :      str    					=						sampling_rate
             SCREAMING_SNAKE_CASE   :      int    					=						padding_value
             SCREAMING_SNAKE_CASE   :      Any    					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2,							num_mel_filters=A,							min_frequency=0.0,							max_frequency=2_20_50.0,							sampling_rate=A,							norm='slaney',							mel_scale='slaney',							).T







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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.T,							log_mel='dB',							db_range=80.0,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						log_spec[:, :-1]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						log_spec - 20.0
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.clip(log_spec / 40.0,							-2.0,							0.0  ) + 1.0
             return log_spec







       def __call__(      self,							A,							A = None,							A = True,							A = None,							A = False,							A = False,							**A,							):







             '''simple docstring'''







             if sampling_rate is not None:
                   if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             'This feature extractor is set to support sampling rate'
                             F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                             F" 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.'  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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}"  )
             SCREAMING_SNAKE_CASE   :      int    					=						is_batched_numpy or (
                 isinstance(A,							(list, tuple)  ) and (isinstance(raw_speech[0],							(np.ndarray, tuple, list)  ))
             )
             if is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([speech],							dtype=np.floataa  ).T for speech in raw_speech]
             elif not is_batched and not isinstance(A,							np.ndarray  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						np.asarray(A,							dtype=np.floataa  )
             elif isinstance(A,							np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						raw_speech.astype(np.floataa  )
             # always return batch
             if not is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([raw_speech]  ).T]

             # Convert audio signals to log mel spectrograms, truncate by time axis
             SCREAMING_SNAKE_CASE   :      int    					=						[
                 self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
             ]
             if isinstance(audio_features[0],							A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray(A,							dtype=np.floataa  ) for feature in audio_features]

             # Create audio attention mask
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max(
                 [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                       (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                       + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                       for feature in audio_features
                   ]
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(A  ).astype(np.floataa  )

             # convert into correct format for padding
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.ones([len(A  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						padded_audio_features * self.padding_value
             for i in range(len(A  )  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						audio_features[i]
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feature

             # return as BatchFeature
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      Any    					=						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'audio_values': padded_audio_features}

             SCREAMING_SNAKE_CASE   :      str    					=						BatchFeature(data=A,							tensor_type=A  )
             return encoded_inputs


