





import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def    _A	(   lowerCamelCase ):
   a__						:    int   		=	tmp_path / "file.csv"
   a__						:    List[str]   		=	textwrap.dedent(
       "\\n        header1,header2\n        1,2\n        10,20\n        " )
   with open(lowerCamelCase				,			"w" ) as f:
      f.write(lowerCamelCase )
   return str(lowerCamelCase )






@pytest.fixture
def    _A	(   lowerCamelCase ):
   a__						:    Union[str, Any]   		=	tmp_path / "malformed_file.csv"
   a__						:    Dict   		=	textwrap.dedent(
       "\\n        header1,header2\n        1,2\n        10,20,\n        " )
   with open(lowerCamelCase				,			"w" ) as f:
      f.write(lowerCamelCase )
   return str(lowerCamelCase )






@pytest.fixture
def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    str   		=	tmp_path / "csv_with_image.csv"
   a__						:    str   		=	textwrap.dedent(
       F"""\
        image
        {image_file}
        """ )
   with open(lowerCamelCase				,			"w" ) as f:
      f.write(lowerCamelCase )
   return str(lowerCamelCase )






@pytest.fixture
def    _A	(   lowerCamelCase ):
   a__						:    List[Any]   		=	tmp_path / "csv_with_label.csv"
   a__						:    Union[str, Any]   		=	textwrap.dedent(
       "\\n        label\n        good\n        bad\n        good\n        " )
   with open(lowerCamelCase				,			"w" ) as f:
      f.write(lowerCamelCase )
   return str(lowerCamelCase )






@pytest.fixture
def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	tmp_path / "csv_with_int_list.csv"
   a__						:    Optional[Any]   		=	textwrap.dedent(
       "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        " )
   with open(lowerCamelCase				,			"w" ) as f:
      f.write(lowerCamelCase )
   return str(lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    Optional[Any]   		=	Csv()
   a__						:    Any   		=	csv._generate_tables([[csv_file, malformed_csv_file]] )
   with pytest.raises(lowerCamelCase				,			match="Error tokenizing data" ):
      for _ in generator:
         pass
   assert any(
       record.levelname == "ERROR"
       and "Failed to read file" in record.message
       and os.path.basename(lowerCamelCase ) in record.message
       for record in caplog.records )






@require_pil
def    _A	(   lowerCamelCase ):
   with open(lowerCamelCase				,			encoding="utf-8" ) as f:
      a__						:    int   		=	f.read().splitlines()[1]
   a__						:    List[str]   		=	Csv(encoding="utf-8"				,			features=Features({"image": Image()} ) )
   a__						:    str   		=	csv._generate_tables([[csv_file_with_image]] )
   a__						:    Optional[Any]   		=	pa.concat_tables([table for _, table in generator] )
   assert pa_table.schema.field("image" ).type == Image()()
   a__						:    Dict   		=	pa_table.to_pydict()["image"]
   assert generated_content == [{"path": image_file, "bytes": None}]






def    _A	(   lowerCamelCase ):
   with open(lowerCamelCase				,			encoding="utf-8" ) as f:
      a__						:    Optional[Any]   		=	f.read().splitlines()[1:]
   a__						:    Tuple   		=	Csv(encoding="utf-8"				,			features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
   a__						:    Dict   		=	csv._generate_tables([[csv_file_with_label]] )
   a__						:    Optional[int]   		=	pa.concat_tables([table for _, table in generator] )
   assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
   a__						:    Tuple   		=	pa_table.to_pydict()["label"]
   assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(lowerCamelCase ) for label in labels]






def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	Csv(encoding="utf-8"				,			sep=","				,			converters={"int_list": lambda lowerCamelCase : [int(lowerCamelCase ) for i in x.split()]} )
   a__						:    Tuple   		=	csv._generate_tables([[csv_file_with_int_list]] )
   a__						:    Optional[int]   		=	pa.concat_tables([table for _, table in generator] )
   assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
   a__						:    Tuple   		=	pa_table.to_pydict()["int_list"]
   assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]








import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__							:      Optional[int]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					["""input_values""", """attention_mask"""]





   def __init__(			self       ,      snake_case = 1       ,      snake_case = 16_000       ,      snake_case = 0.0       ,      snake_case = False       ,      snake_case = 80       ,      snake_case = 16       ,      snake_case = 64       ,      snake_case = "hann_window"       ,      snake_case = 1.0       ,      snake_case = 80       ,      snake_case = 7_600       ,      snake_case = 1E-10       ,      snake_case = 2       ,      snake_case = True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      super().__init__(feature_size=snake_case       ,      sampling_rate=snake_case       ,      padding_value=snake_case       ,      **snake_case   )
      a__						:    Any   		=	do_normalize
      a__						:    List[str]   		=	return_attention_mask

      a__						:    List[Any]   		=	num_mel_bins
      a__						:    List[str]   		=	hop_length
      a__						:    int   		=	win_length
      a__						:    List[Any]   		=	win_function
      a__						:    List[str]   		=	frame_signal_scale
      a__						:    List[Any]   		=	fmin
      a__						:    Optional[Any]   		=	fmax
      a__						:    Union[str, Any]   		=	mel_floor
      a__						:    Union[str, Any]   		=	reduction_factor

      a__						:    List[str]   		=	win_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	hop_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	optimal_fft_length(self.sample_size   )
      a__						:    Dict   		=	(self.n_fft // 2) + 1

      a__						:    str   		=	window_function(window_length=self.sample_size       ,      name=self.win_function       ,      periodic=snake_case   )

      a__						:    Tuple   		=	mel_filter_bank(
          num_frequency_bins=self.n_freqs       ,      num_mel_filters=self.num_mel_bins       ,      min_frequency=self.fmin       ,      max_frequency=self.fmax       ,      sampling_rate=self.sampling_rate       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )

      if frame_signal_scale != 1.0:
         warnings.warn(
             "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )
      if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )





   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 						_snake_case			(			snake_case       ,      snake_case       ,      snake_case = 0.0   )					->	List[np.ndarray]:
      """simple docstring"""





      if attention_mask is not None:
         a__						:    Tuple   		=	np.array(snake_case       ,      np.intaa   )
         a__						:    List[str]   		=	[]

         for vector, length in zip(snake_case       ,      attention_mask.sum(-1   )   ):
            a__						:    List[Any]   		=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
            if length < normed_slice.shape[0]:
               a__						:    List[str]   		=	padding_value

            normed_input_values.append(snake_case   )
      else:
         a__						:    Any   		=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

      return normed_input_values





   def 						_snake_case			(			self       ,      snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    str   		=	spectrogram(
          snake_case       ,      window=self.window       ,      frame_length=self.sample_size       ,      hop_length=self.sample_stride       ,      fft_length=self.n_fft       ,      mel_filters=self.mel_filters       ,      mel_floor=self.mel_floor       ,      log_mel="log10"       ,      )
      return log_mel_spec.T





   def __call__(			self       ,      snake_case = None       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      if audio is None and audio_target is None:
         raise ValueError("You must provide either `audio` or `audio_target` values."   )

      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 audio is not None:
         a__						:    Dict   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )
      else:
         a__						:    Optional[int]   		=	None

      if audio_target is not None:
         a__						:    List[Any]   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )

         if inputs is None:
            return inputs_target
         else:
            a__						:    Tuple   		=	inputs_target["input_values"]
            a__						:    Tuple   		=	inputs_target.get("attention_mask"   )
            if decoder_attention_mask is not None:
               a__						:    Tuple   		=	decoder_attention_mask

      return inputs





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      a__						:    Optional[int]   		=	isinstance(snake_case       ,      np.ndarray   ) and len(speech.shape   ) > 1
      if is_batched_numpy and len(speech.shape   ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""   )
      a__						:    List[Any]   		=	is_batched_numpy or (
          isinstance(snake_case       ,      (list, tuple)   ) and (isinstance(speech[0]       ,      (np.ndarray, tuple, list)   ))
      )

      if is_batched:
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for speech in speech]
      elif not is_batched and not isinstance(snake_case       ,      np.ndarray   ):
         a__						:    Any   		=	np.asarray(snake_case       ,      dtype=np.floataa   )
      elif isinstance(snake_case       ,      np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
         a__						:    List[Any]   		=	speech.astype(np.floataa   )

      # always return batch
      if not is_batched:
         a__						:    Union[str, Any]   		=	[speech]

      # needed to make pad() work on spectrogram inputs
      a__						:    Optional[Any]   		=	self.feature_size

      # convert into correct format for padding
      if is_target:
         a__						:    List[str]   		=	[self._extract_mel_features(snake_case   ) for waveform in speech]
         a__						:    Optional[Any]   		=	BatchFeature({"input_values": features}   )
         a__						:    str   		=	self.num_mel_bins
      else:
         a__						:    int   		=	BatchFeature({"input_values": speech}   )

      a__						:    int   		=	self.pad(
          snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      truncation=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_attention_mask=snake_case       ,      **snake_case       ,      )

      a__						:    Any   		=	feature_size_hack

      # convert input values to correct format
      a__						:    Tuple   		=	padded_inputs["input_values"]
      if not isinstance(input_values[0]       ,      np.ndarray   ):
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for array in input_values]
      elif (
          not isinstance(snake_case       ,      np.ndarray   )
          and isinstance(input_values[0]       ,      np.ndarray   )
          and input_values[0].dtype is np.dtype(np.floataa   )
      ):
         a__						:    Union[str, Any]   		=	[array.astype(np.floataa   ) for array in input_values]
      elif isinstance(snake_case       ,      np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
         a__						:    Optional[int]   		=	input_values.astype(np.floataa   )

      # convert attention_mask to correct format
      a__						:    Optional[int]   		=	padded_inputs.get("attention_mask"   )
      if attention_mask is not None:
         a__						:    Tuple   		=	[np.asarray(snake_case       ,      dtype=np.intaa   ) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if not is_target and self.do_normalize:
         a__						:    Any   		=	(
             attention_mask
             if self._get_padding_strategies(snake_case       ,      max_length=snake_case   ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a__						:    Optional[Any]   		=	self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]       ,      attention_mask=snake_case       ,      padding_value=self.padding_value   )

      if return_tensors is not None:
         a__						:    int   		=	padded_inputs.convert_to_tensors(snake_case   )

      return padded_inputs





   def 						_snake_case			(			self   )					->	Dict[str, Any]:
      """simple docstring"""





      a__						:    int   		=	super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      a__						:    str   		=	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
      for name in names:
         if name in output:
            del output[name]

      return output

