






'''simple docstring'''







import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      assert isinstance(snake_case__				,      snake_case__				)
      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     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            __A		 =   CsvDatasetReader(snake_case__				,      cache_dir=snake_case__				,      keep_in_memory=snake_case__				).read()
      _check_csv_dataset(snake_case__				,      snake_case__				)


@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				(  snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      __A		 =   features.copy() if features else default_expected_features
      __A		 =   (
          Features({feature: Value(snake_case__				) for feature, dtype in features.items()}				) if features is not None else None
      )
      __A		 =   CsvDatasetReader(snake_case__				,      features=snake_case__				,      cache_dir=snake_case__				).read()
      _check_csv_dataset(snake_case__				,      snake_case__				)


@pytest.mark.parametrize("split"				,      [None, NamedSplit("train"				), "train", "test"]				)
def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      __A		 =   CsvDatasetReader(snake_case__				,      cache_dir=snake_case__				,      split=snake_case__				).read()
      _check_csv_dataset(snake_case__				,      snake_case__				)
      assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"				,      [str, list]				)
def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      if issubclass(snake_case__				,      snake_case__				):
            __A		 =   csv_path
      elif issubclass(snake_case__				,      snake_case__				):
            __A		 =   [csv_path]
      __A		 =   tmp_path / "cache"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      __A		 =   CsvDatasetReader(snake_case__				,      cache_dir=snake_case__				).read()
      _check_csv_dataset(snake_case__				,      snake_case__				)


def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__=("train",)				):
      assert isinstance(snake_case__				,      snake_case__				)
      for split in splits:
            __A		 =   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     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            __A		 =   CsvDatasetReader({"train": csv_path}				,      cache_dir=snake_case__				,      keep_in_memory=snake_case__				).read()
      _check_csv_datasetdict(snake_case__				,      snake_case__				)


@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				(  snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      __A		 =   features.copy() if features else default_expected_features
      __A		 =   (
          Features({feature: Value(snake_case__				) for feature, dtype in features.items()}				) if features is not None else None
      )
      __A		 =   CsvDatasetReader({"train": csv_path}				,      features=snake_case__				,      cache_dir=snake_case__				).read()
      _check_csv_datasetdict(snake_case__				,      snake_case__				)


@pytest.mark.parametrize("split"				,      [None, NamedSplit("train"				), "train", "test"]				)
def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      if split:
            __A		 =   {split: csv_path}
      else:
            __A		 =   {"train": csv_path, "test": csv_path}
      __A		 =   tmp_path / "cache"
      __A		 =   {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      __A		 =   CsvDatasetReader(snake_case__				,      cache_dir=snake_case__				).read()
      _check_csv_datasetdict(snake_case__				,      snake_case__				,      splits=list(path.keys()				)				)
      assert all(dataset[split].split == split for split in path.keys()				)


def     _UpperCamelCase				(  snake_case__				):
      with open(snake_case__				,      encoding="utf-8"				) as csvfile:
            yield from csv.reader(snake_case__				)


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      __A		 =   os.path.join(snake_case__				,      "tmp.csv"				)
      __A		 =   CsvDatasetReader({"train": csv_path}				,      cache_dir=snake_case__				).read()
      CsvDatasetWriter(dataset["train"]				,      snake_case__				,      num_proc=1				).write()

      __A		 =   iter_csv_file(snake_case__				)
      __A		 =   iter_csv_file(snake_case__				)

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


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      __A		 =   os.path.join(snake_case__				,      "tmp.csv"				)
      __A		 =   CsvDatasetReader({"train": csv_path}				,      cache_dir=snake_case__				).read()
      CsvDatasetWriter(dataset["train"]				,      snake_case__				,      num_proc=2				).write()

      __A		 =   iter_csv_file(snake_case__				)
      __A		 =   iter_csv_file(snake_case__				)

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





def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      __A		 =   tmp_path / "cache"
      __A		 =   os.path.join(snake_case__				,      "tmp.csv"				)
      __A		 =   CsvDatasetReader({"train": csv_path}				,      cache_dir=snake_case__				).read()
      with pytest.raises(snake_case__				):
            CsvDatasetWriter(dataset["train"]				,      snake_case__				,      num_proc=0				)










'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


__A   :							int     	=						logging.get_logger(__name__)






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	['input_values', 'attention_mask']






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=1					,					lowercase__     :						Optional[int]=16000					,					lowercase__     :						Any=128					,					lowercase__     :						Optional[Any]=1024					,					lowercase__     :						int=0.0					,					lowercase__     :						List[str]=True					,					lowercase__     :						Dict=-4.2_67_73_93					,					lowercase__     :						Tuple=4.5_68_99_74					,					lowercase__     :						Optional[int]=False					,					**lowercase__     :						Optional[int]					,					)	->							Tuple:
           super().__init__(feature_size=lowercase__					,					sampling_rate=lowercase__					,					padding_value=lowercase__					,					**lowercase__       )
           __A		 =   num_mel_bins
           __A		 =   max_length
           __A		 =   do_normalize
           __A		 =   mean
           __A		 =   std
           __A		 =   return_attention_mask






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						np.ndarray					,					lowercase__     :						int					,					)	->							np.ndarray:
           __A		 =   torch.from_numpy(lowercase__       ).unsqueeze(0       )
           __A		 =   ta_kaldi.fbank(
               lowercase__					,					htk_compat=lowercase__					,					sample_frequency=self.sampling_rate					,					use_energy=lowercase__					,					window_type="hanning"					,					num_mel_bins=self.num_mel_bins					,					dither=0.0					,					frame_shift=10					,					)

           __A		 =   fbank.shape[0]
           __A		 =   max_length - n_frames

           # pad or truncate, depending on difference
           if difference > 0:
                 __A		 =   torch.nn.ZeroPadad((0, 0, 0, difference)       )
                 __A		 =   pad_module(lowercase__       )
           elif difference < 0:
                 __A		 =   fbank[0:max_length, :]

           __A		 =   fbank.numpy()

           return fbank






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						np.ndarray       )	->							np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)






     def __call__(self     :						str					,					lowercase__     :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					lowercase__     :						Optional[int] = None					,					lowercase__     :						Optional[Union[str, TensorType]] = None					,					**lowercase__     :						List[Any]					,					)	->							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."       )

           __A		 =   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}"       )
           __A		 =   is_batched_numpy or (
               isinstance(lowercase__					,					(list, tuple)       ) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)       ))
           )

           if is_batched:
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for speech in raw_speech]
           elif not is_batched and not isinstance(lowercase__					,					np.ndarray       ):
                 __A		 =   np.asarray(lowercase__					,					dtype=np.floataa       )
           elif isinstance(lowercase__					,					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		 =   [raw_speech]

           # extract fbank features and pad/truncate to max_length
           __A		 =   [self._extract_fbank_features(lowercase__					,					max_length=self.max_length       ) for waveform in raw_speech]

           # convert into BatchFeature
           __A		 =   BatchFeature({"input_values": features}       )

           # make sure list is in array format
           __A		 =   padded_inputs.get("input_values"       )
           if isinstance(input_values[0]					,					lowercase__       ):
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for feature in input_values]

           # normalization
           if self.do_normalize:
                 __A		 =   [self.normalize(lowercase__       ) for feature in input_values]

           if return_tensors is not None:
                 __A		 =   padded_inputs.convert_to_tensors(lowercase__       )

           return padded_inputs


