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


SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""input_features"""]

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=80     ,							__SCREAMING_SNAKE_CASE :      List[str]=1_60_00     ,							__SCREAMING_SNAKE_CASE :      Tuple=1_60     ,							__SCREAMING_SNAKE_CASE :      Tuple=30     ,							__SCREAMING_SNAKE_CASE :      List[Any]=4_00     ,							__SCREAMING_SNAKE_CASE :      Tuple=0.0     ,							__SCREAMING_SNAKE_CASE :      Any=False     ,							**__SCREAMING_SNAKE_CASE :      int     ,							):
   super().__init__(
       feature_size=__SCREAMING_SNAKE_CASE     ,							sampling_rate=__SCREAMING_SNAKE_CASE     ,							padding_value=__SCREAMING_SNAKE_CASE     ,							return_attention_mask=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)
   __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=__SCREAMING_SNAKE_CASE     ,							min_frequency=0.0     ,							max_frequency=80_00.0     ,							sampling_rate=__SCREAMING_SNAKE_CASE     ,							norm="slaney"     ,							mel_scale="slaney"     ,							)

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      np.array       ):
   __a       =  spectrogram(
       __SCREAMING_SNAKE_CASE     ,							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(__SCREAMING_SNAKE_CASE     ,							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   (		__SCREAMING_SNAKE_CASE :      List[np.ndarray]     ,							__SCREAMING_SNAKE_CASE :      List[np.ndarray]     ,							__SCREAMING_SNAKE_CASE :      float = 0.0       ):
   if attention_mask is not None:
    __a       =  np.array(__SCREAMING_SNAKE_CASE     ,							np.intaa       )
    __a       =  []

    for vector, length in zip(__SCREAMING_SNAKE_CASE     ,							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(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  [(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

   return normed_input_values

  def __call__(		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = "max_length"     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							**__SCREAMING_SNAKE_CASE :      List[Any]     ,							):

   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(__SCREAMING_SNAKE_CASE     ,							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(__SCREAMING_SNAKE_CASE     ,							(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(__SCREAMING_SNAKE_CASE     ,							np.ndarray       ):
    __a       =  np.asarray(__SCREAMING_SNAKE_CASE     ,							dtype=np.floataa       )
   elif isinstance(__SCREAMING_SNAKE_CASE     ,							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(
       __SCREAMING_SNAKE_CASE     ,							padding=__SCREAMING_SNAKE_CASE     ,							max_length=max_length if max_length else self.n_samples     ,							truncation=__SCREAMING_SNAKE_CASE     ,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,							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(__SCREAMING_SNAKE_CASE       ) for waveform in input_features[0]]

   if isinstance(input_features[0]     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  [np.asarray(__SCREAMING_SNAKE_CASE     ,							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(__SCREAMING_SNAKE_CASE       )

   return padded_inputs

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  copy.deepcopy(self.__dict__       )
   __a       =  self.__class__.__name__
   if "mel_filters" in output:
    del output["mel_filters"]
   return output



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("dataset_size"							,				[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize("input_in_memory_max_size"							,				["default", 0, 100 * 2**20, 900 * 2**20]					)
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config							,				"IN_MEMORY_MAX_SIZE"							,				_A					)
 __a       =  datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  __a       =  dataset_size < in_memory_max_size
 else:
  __a       =  False
 __a       =  is_small_dataset(_A					)
 assert result == expected


