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


A     :  Any					     =       logging.get_logger(__name__)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						['''audio_values''', '''audio_mask''']







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Union[str, Any]=2_048  ,     __magic_name__ :       Any=1  ,     __magic_name__ :       int=[16, 16]  ,     __magic_name__ :       Any=128  ,     __magic_name__ :       Union[str, Any]=44_100  ,     __magic_name__ :       Optional[int]=86  ,     __magic_name__ :       Dict=2_048  ,     __magic_name__ :       Optional[int]=0.0  ,     **__magic_name__ :       str  ,     )  ->	str:
        super().__init__(
            feature_size=__magic_name__  ,     sampling_rate=__magic_name__  ,     padding_value=__magic_name__  ,     **__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  spectrogram_length
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  patch_size
        SCREAMING_SNAKE_CASE_          =  feature_size // self.patch_size[1]
        SCREAMING_SNAKE_CASE_          =  n_fft
        SCREAMING_SNAKE_CASE_          =  sampling_rate // hop_length_to_sampling_rate
        SCREAMING_SNAKE_CASE_          =  sampling_rate
        SCREAMING_SNAKE_CASE_          =  padding_value
        SCREAMING_SNAKE_CASE_          =  mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2  ,     num_mel_filters=__magic_name__  ,     min_frequency=0.0  ,     max_frequency=2_2050.0  ,     sampling_rate=__magic_name__  ,     norm="slaney"  ,     mel_scale="slaney"  ,     ).T







  def    __A     (							self :       Dict  ,     __magic_name__ :       np.array   )  ->	np.ndarray:
        SCREAMING_SNAKE_CASE_          =  spectrogram(
            __magic_name__  ,     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_          =  log_spec[:, :-1]
        SCREAMING_SNAKE_CASE_          =  log_spec - 20.0
        SCREAMING_SNAKE_CASE_          =  np.clip(log_spec / 40.0  ,     -2.0  ,     0.0   ) + 1.0
        return log_spec







  def __call__(							self :       Optional[Any]  ,     __magic_name__ :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,     __magic_name__ :       Optional[Union[str, TensorType]] = None  ,     __magic_name__ :       Optional[bool] = True  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       bool = False  ,     __magic_name__ :       bool = False  ,     **__magic_name__ :       int  ,     )  ->	BatchFeature:

        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_          =  isinstance(__magic_name__  ,     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_          =  is_batched_numpy or (
            isinstance(__magic_name__  ,     (list, tuple)   ) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)   ))
        )
        if is_batched:
              SCREAMING_SNAKE_CASE_          =  [np.asarray([speech]  ,     dtype=np.floataa   ).T for speech in raw_speech]
        elif not is_batched and not isinstance(__magic_name__  ,     np.ndarray   ):
              SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__  ,     dtype=np.floataa   )
        elif isinstance(__magic_name__  ,     np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
              SCREAMING_SNAKE_CASE_          =  raw_speech.astype(np.floataa   )
        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE_          =  [np.asarray([raw_speech]   ).T]

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

        # Create audio attention mask
        SCREAMING_SNAKE_CASE_          =  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_          =  [
                  (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_          =  np.array(__magic_name__   ).astype(np.floataa   )

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

        # return as BatchFeature
        if return_attention_mask:
              SCREAMING_SNAKE_CASE_          =  {"audio_values": padded_audio_features, "audio_mask": audio_mask}
        else:
              SCREAMING_SNAKE_CASE_          =  {"audio_values": padded_audio_features}

        SCREAMING_SNAKE_CASE_          =  BatchFeature(data=__magic_name__  ,     tensor_type=__magic_name__   )
        return encoded_inputs



import logging
import os
import threading
import time


try:
      import warnings
except ImportError:
      A     :  Optional[int]					     =       None

try:
      import msvcrt
except ImportError:
      A     :  List[str]					     =       None

try:
      import fcntl
except ImportError:
      A     :  Union[str, Any]					     =       None


# Backward compatibility
# ------------------------------------------------
try:
      TimeoutError
except NameError:
      A     :  int					     =       OSError


# Data
# ------------------------------------------------
A     :  Any					     =       [
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

A     :  Tuple					     =       "3.0.12"


A     :  str					     =       None


def      a__ (		):
      global _logger
      SCREAMING_SNAKE_CASE_          =  _logger or logging.getLogger(__name__				)
      return _logger


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  lock_file
        return None







  def __str__(							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  F'''The file lock \'{self.lock_file}\' could not be acquired.'''
        return temp



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  lock
        return None







  def __enter__(							self :       int   )  ->	int:
        return self.lock







  def __exit__(							self :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	Optional[int]:
        self.lock.release()
        return None



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]=-1  ,     __magic_name__ :       Optional[int]=None   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  max_filename_length if max_filename_length is not None else 255
        # Hash the filename if it's too long
        SCREAMING_SNAKE_CASE_          =  self.hash_filename_if_too_long(__magic_name__  ,     __magic_name__   )
        # The path to the lock file.
        SCREAMING_SNAKE_CASE_          =  lock_file

        # The file descriptor for the *_lock_file* as it is returned by the
        # os.open() function.
        # This file lock is only NOT None, if the object currently holds the
        # lock.
        SCREAMING_SNAKE_CASE_          =  None

        # The default timeout value.
        SCREAMING_SNAKE_CASE_          =  timeout

        # We use this lock primarily for the lock counter.
        SCREAMING_SNAKE_CASE_          =  threading.Lock()

        # The lock counter is used for implementing the nested locking
        # mechanism. Whenever the lock is acquired, the counter is increased and
        # the lock is only released, when this value is 0 again.
        SCREAMING_SNAKE_CASE_          =  0
        return None







  @property
  def    __A     (							self :       int   )  ->	Optional[Any]:
        return self._lock_file







  @property
  def    __A     (							self :       List[str]   )  ->	Union[str, Any]:
        return self._timeout







  @timeout.setter
  def    __A     (							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  float(__magic_name__   )
        return None







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        raise NotImplementedError()







  def    __A     (							self :       Optional[Any]   )  ->	int:
        raise NotImplementedError()







  @property
  def    __A     (							self :       Dict   )  ->	List[str]:
        return self._lock_file_fd is not None







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Optional[int]=0.05   )  ->	str:
        # Use the default timeout, if no timeout is provided.
        if timeout is None:
              SCREAMING_SNAKE_CASE_          =  self.timeout

        # Increment the number right at the beginning.
        # We can still undo it, if something fails.
        with self._thread_lock:
              self._lock_counter += 1

        SCREAMING_SNAKE_CASE_          =  id(self   )
        SCREAMING_SNAKE_CASE_          =  self._lock_file
        SCREAMING_SNAKE_CASE_          =  time.time()
        try:
              while True:
                    with self._thread_lock:
                          if not self.is_locked:
                                logger().debug(F'''Attempting to acquire lock {lock_id} on {lock_filename}'''   )
                                self._acquire()

                    if self.is_locked:
                          logger().debug(F'''Lock {lock_id} acquired on {lock_filename}'''   )
                          break
                    elif timeout >= 0 and time.time() - start_time > timeout:
                          logger().debug(F'''Timeout on acquiring lock {lock_id} on {lock_filename}'''   )
                          raise Timeout(self._lock_file   )
                    else:
                          logger().debug(
                              F'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'''   )
                          time.sleep(__magic_name__   )
        except:  # noqa
              # Something did go wrong, so decrement the counter.
              with self._thread_lock:
                    SCREAMING_SNAKE_CASE_          =  max(0  ,     self._lock_counter - 1   )

              raise
        return _Acquire_ReturnProxy(lock=self   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Tuple=False   )  ->	Optional[int]:
        with self._thread_lock:
              if self.is_locked:
                    self._lock_counter -= 1

                    if self._lock_counter == 0 or force:
                          SCREAMING_SNAKE_CASE_          =  id(self   )
                          SCREAMING_SNAKE_CASE_          =  self._lock_file

                          logger().debug(F'''Attempting to release lock {lock_id} on {lock_filename}'''   )
                          self._release()
                          SCREAMING_SNAKE_CASE_          =  0
                          logger().debug(F'''Lock {lock_id} released on {lock_filename}'''   )

        return None







  def __enter__(							self :       Optional[Any]   )  ->	Tuple:
        self.acquire()
        return self







  def __exit__(							self :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       Union[str, Any]   )  ->	Union[str, Any]:
        self.release()
        return None







  def __del__(							self :       Optional[int]   )  ->	Optional[Any]:
        self.release(force=__magic_name__   )
        return None







  def    __A     (							self :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.path.basename(__magic_name__   )
        if len(__magic_name__   ) > max_length and max_length > 0:
              SCREAMING_SNAKE_CASE_          =  os.path.dirname(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  str(hash(__magic_name__   )   )
              SCREAMING_SNAKE_CASE_          =  filename[: max_length - len(__magic_name__   ) - 8] + "..." + hashed_filename + ".lock"
              return os.path.join(__magic_name__  ,     __magic_name__   )
        else:
              return path



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Optional[int]=None   )  ->	Dict:
        from .file_utils import relative_to_absolute_path

        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  "\\\\?\\" + relative_to_absolute_path(self.lock_file   )







  def    __A     (							self :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC

        try:
              SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )
        except OSError:
              pass
        else:
              try:
                    msvcrt.locking(__magic_name__  ,     msvcrt.LK_NBLCK  ,     1   )
              except OSError:
                    os.close(__magic_name__   )
              else:
                    SCREAMING_SNAKE_CASE_          =  fd
        return None







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        msvcrt.locking(__magic_name__  ,     msvcrt.LK_UNLCK  ,     1   )
        os.close(__magic_name__   )

        try:
              os.remove(self._lock_file   )
        # Probably another instance of the application
        # that acquired the file lock.
        except OSError:
              pass
        return None



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Dict  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Dict=None   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  os.statvfs(os.path.dirname(__magic_name__   )   ).f_namemax
        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )







  def    __A     (							self :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC
        SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )

        try:
              fcntl.flock(__magic_name__  ,     fcntl.LOCK_EX | fcntl.LOCK_NB   )
        except OSError:
              os.close(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  fd
        return None







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        # Do not remove the lockfile:
        #
        #   https://github.com/benediktschmitt/py-filelock/issues/31
        #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        fcntl.flock(__magic_name__  ,     fcntl.LOCK_UN   )
        os.close(__magic_name__   )
        return None



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
              SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )
        except OSError:
              pass
        else:
              SCREAMING_SNAKE_CASE_          =  fd
        return None







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        os.close(self._lock_file_fd   )
        SCREAMING_SNAKE_CASE_          =  None

        try:
              os.remove(self._lock_file   )
        # The file is already deleted and that's what we want.
        except OSError:
              pass
        return None



A     :  List[str]					     =       None

if msvcrt:
      A     :  Tuple					     =       WindowsFileLock
elif fcntl:
      A     :  Union[str, Any]					     =       UnixFileLock
else:
      A     :  List[Any]					     =       SoftFileLock

      if warnings is not None:
            warnings.warn("only soft file lock is available")


