
"""simple docstring"""


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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Tuple   =			['''audio_values''', '''audio_mask''']






       def __init__(  self :							Tuple       ,      UpperCAmelCase_ :							str=2_048       ,      UpperCAmelCase_ :							Tuple=1       ,      UpperCAmelCase_ :							str=[16, 16]       ,      UpperCAmelCase_ :							int=128       ,      UpperCAmelCase_ :							Union[str, Any]=44_100       ,      UpperCAmelCase_ :							Union[str, Any]=86       ,      UpperCAmelCase_ :							Union[str, Any]=2_048       ,      UpperCAmelCase_ :							str=0.0       ,      **UpperCAmelCase_ :							Optional[int]       ,      )->  Optional[Any]:







              """simple docstring"""
              super().__init__(
                  feature_size=UpperCAmelCase_       ,      sampling_rate=UpperCAmelCase_       ,      padding_value=UpperCAmelCase_       ,      **UpperCAmelCase_       ,      )

              UpperCamelCase					 =		spectrogram_length
              UpperCamelCase					 =		num_channels
              UpperCamelCase					 =		patch_size
              UpperCamelCase					 =		feature_size // self.patch_size[1]
              UpperCamelCase					 =		n_fft
              UpperCamelCase					 =		sampling_rate // hop_length_to_sampling_rate
              UpperCamelCase					 =		sampling_rate
              UpperCamelCase					 =		padding_value
              UpperCamelCase					 =		mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2       ,      num_mel_filters=UpperCAmelCase_       ,      min_frequency=0.0       ,      max_frequency=22050.0       ,      sampling_rate=UpperCAmelCase_       ,      norm="slaney"       ,      mel_scale="slaney"       ,      ).T






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							np.array			)->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		spectrogram(
                  UpperCAmelCase_       ,      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       ,      )
              UpperCamelCase					 =		log_spec[:, :-1]
              UpperCamelCase					 =		log_spec - 20.0
              UpperCamelCase					 =		np.clip(log_spec / 40.0       ,      -2.0       ,      0.0			) + 1.0
              return log_spec






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







              """simple docstring"""

              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."			)

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

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

              # Create audio attention mask
              UpperCamelCase					 =		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:
                     UpperCamelCase					 =		[
                         (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
                     ]
                     UpperCamelCase					 =		np.array(UpperCAmelCase_			).astype(np.floataa			)

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

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

              UpperCamelCase					 =		BatchFeature(data=UpperCAmelCase_       ,      tensor_type=UpperCAmelCase_			)
              return encoded_inputs







"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		inspect.getfile(accelerate.test_utils			)
              UpperCamelCase					 =		os.path.sep.join(
                  mod_file.split(os.path.sep			)[:-1] + ["scripts", "external_deps", "test_metrics.py"]			)

              from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

              UpperCamelCase					 =		test_metrics






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main       ,      num_processes=1			)






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main			)






       @require_single_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              self.test_metrics.main()






       @require_multi_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              print(f"Found {torch.cuda.device_count()} devices."			)
              UpperCamelCase					 =		["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
              with patch_environment(omp_num_threads=1			):
                     execute_subprocess_async(UpperCAmelCase_       ,      env=os.environ.copy()			)





