"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)





class 				_A		( lowerCAmelCase			):
      snake_case__    :     List[Any]													=			['input_values', 'padding_mask']

      def __init__(		self	, __lowerCAmelCase = 1	, __lowerCAmelCase = 2_4000	, __lowerCAmelCase = 0.0	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(feature_size=__lowerCAmelCase	, sampling_rate=__lowerCAmelCase	, padding_value=__lowerCAmelCase	, **__lowerCAmelCase		)
            lowercase														=			chunk_length_s
            lowercase														=			overlap

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.chunk_length_s is None:
                  return None
            else:
                  return int(self.chunk_length_s * self.sampling_rate		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.chunk_length_s is None or self.overlap is None:
                  return None
            else:
                  return max(1	, int((1.0 - self.overlap) * self.chunk_length		)		)







      def __call__(		self	, __lowerCAmelCase	, __lowerCAmelCase = None	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, ):







            """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} was trained using a sampling rate of'
                            f' {self.sampling_rate}. Please make sure that the provided audio 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."""		)

            if padding and truncation:
                  raise ValueError("""Both padding and truncation were set. Make sure you only set one."""		)
            elif padding is None:
                  # by default let's pad the inputs
                  lowercase														=			True

            lowercase														=			bool(
                isinstance(__lowerCAmelCase	, (list, tuple)		) and (isinstance(raw_audio[0]	, (np.ndarray, tuple, list)		))		)

            if is_batched:
                  lowercase														=			[np.asarray(__lowerCAmelCase	, dtype=np.floataa		).T for audio in raw_audio]
            elif not is_batched and not isinstance(__lowerCAmelCase	, np.ndarray		):
                  lowercase														=			np.asarray(__lowerCAmelCase	, dtype=np.floataa		)
            elif isinstance(__lowerCAmelCase	, np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
                  lowercase														=			raw_audio.astype(np.floataa		)

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

            # verify inputs are valid
            for idx, example in enumerate(__lowerCAmelCase		):
                  if example.ndim > 2:
                        raise ValueError(f'Expected input shape (channels, length) but got shape {example.shape}'		)
                  if self.feature_size == 1 and example.ndim != 1:
                        raise ValueError(f'Expected mono audio but example has {example.shape[-1]} channels'		)
                  if self.feature_size == 2 and example.shape[-1] != 2:
                        raise ValueError(f'Expected stereo audio but example has {example.shape[-1]} channels'		)

            lowercase														=			None
            lowercase														=			BatchFeature({"""input_values""": raw_audio}		)
            if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                  if truncation:
                        lowercase														=			min(array.shape[0] for array in raw_audio		)
                        lowercase														=			int(np.floor(max_length / self.chunk_stride		)		)
                        lowercase														=			(nb_step - 1) * self.chunk_stride + self.chunk_length
                  elif padding:
                        lowercase														=			max(array.shape[0] for array in raw_audio		)
                        lowercase														=			int(np.ceil(max_length / self.chunk_stride		)		)
                        lowercase														=			(nb_step - 1) * self.chunk_stride + self.chunk_length
                        lowercase														=			"""max_length"""
                  else:
                        lowercase														=			input_values

        # normal padding on batch
            if padded_inputs is None:
                  lowercase														=			self.pad(
                      __lowerCAmelCase	, max_length=__lowerCAmelCase	, truncation=__lowerCAmelCase	, padding=__lowerCAmelCase	, return_attention_mask=__lowerCAmelCase	, )
                  if padding:
                        lowercase														=			padded_inputs.pop("""attention_mask"""		)

            lowercase														=			[]
            for example in padded_inputs.pop("""input_values"""		):
                  if self.feature_size == 1:
                        lowercase														=			example[..., None]
                  input_values.append(example.T		)

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

            return padded_inputs




"""simple docstring"""






import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Tuple	)				->					Union[str, Any]:
      '''simple docstring'''


      assert isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	)
      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


@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""      ,	[False, True]	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :List[str]	)				->					int:
      '''simple docstring'''


      lowercase														=			tmp_path / """cache"""
      lowercase														=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            lowercase														=			SqlDatasetReader(
                """dataset"""      ,	"""sqlite:///""" + sqlite_path      ,	cache_dir=lowerCAmelCase__      ,	keep_in_memory=lowerCAmelCase__	).read()
      _check_sql_dataset(lowerCAmelCase__      ,	lowerCAmelCase__	)


@require_sqlalchemy
@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     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			tmp_path / """cache"""
      lowercase														=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      lowercase														=			features.copy() if features else default_expected_features
      lowercase														=			(
          Features({feature: Value(lowerCAmelCase__	) for feature, dtype in features.items()}	) if features is not None else None
      )
      lowercase														=			SqlDatasetReader("""dataset"""      ,	"""sqlite:///""" + sqlite_path      ,	features=lowerCAmelCase__      ,	cache_dir=lowerCAmelCase__	).read()
      _check_sql_dataset(lowerCAmelCase__      ,	lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Optional[int]:
      '''simple docstring'''


      with contextlib.closing(sqlitea.connect(lowerCAmelCase__	)	) as con:
            lowercase														=			con.cursor()
            cur.execute("""SELECT * FROM dataset"""	)
            for row in cur:
                  yield row


@require_sqlalchemy
def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			tmp_path / """cache"""
      lowercase														=			os.path.join(lowerCAmelCase__      ,	"""tmp.sql"""	)
      lowercase														=			SqlDatasetReader("""dataset"""      ,	"""sqlite:///""" + sqlite_path      ,	cache_dir=lowerCAmelCase__	).read()
      SqlDatasetWriter(lowerCAmelCase__      ,	"""dataset"""      ,	"""sqlite:///""" + output_sqlite_path      ,	num_proc=1	).write()

      lowercase														=			iter_sql_file(lowerCAmelCase__	)
      lowercase														=			iter_sql_file(lowerCAmelCase__	)

      for rowa, rowa in zip(lowerCAmelCase__      ,	lowerCAmelCase__	):
            assert rowa == rowa


@require_sqlalchemy
def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Optional[int]	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			tmp_path / """cache"""
      lowercase														=			os.path.join(lowerCAmelCase__      ,	"""tmp.sql"""	)
      lowercase														=			SqlDatasetReader("""dataset"""      ,	"""sqlite:///""" + sqlite_path      ,	cache_dir=lowerCAmelCase__	).read()
      SqlDatasetWriter(lowerCAmelCase__      ,	"""dataset"""      ,	"""sqlite:///""" + output_sqlite_path      ,	num_proc=2	).write()

      lowercase														=			iter_sql_file(lowerCAmelCase__	)
      lowercase														=			iter_sql_file(lowerCAmelCase__	)

      for rowa, rowa in zip(lowerCAmelCase__      ,	lowerCAmelCase__	):
            assert rowa == rowa






@require_sqlalchemy
def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			tmp_path / """cache"""
      lowercase														=			os.path.join(lowerCAmelCase__      ,	"""tmp.sql"""	)
      lowercase														=			SqlDatasetReader("""dataset"""      ,	"""sqlite:///""" + sqlite_path      ,	cache_dir=lowerCAmelCase__	).read()
      with pytest.raises(lowerCAmelCase__	):
            SqlDatasetWriter(lowerCAmelCase__      ,	"""dataset"""      ,	"""sqlite:///""" + output_sqlite_path      ,	num_proc=0	).write()



