'''simple docstring'''




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

__lowercase          =  random.Random()






def 				snake_case__							(			_A:					Union[str, Any]      ,   _A:					Optional[Any]=1.0      ,   _A:					Dict=None      ,   _A:					Optional[Any]=None )    ->	Tuple:

		'''simple docstring'''


		if rng is None:
				lowerCAmelCase   					=  global_rng

		lowerCAmelCase   					=  []
		for batch_idx in range(shape[0] ):
				values.append([] )
				for _ in range(shape[1] ):
						values[-1].append(rng.random() * scale )

		return values






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=2000       ,    __lowerCAmelCase=2048       ,    __lowerCAmelCase=128       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=512       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=44100       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  min_seq_length
					lowerCAmelCase   					=  max_seq_length
					lowerCAmelCase   					=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCAmelCase   					=  spectrogram_length
					lowerCAmelCase   					=  feature_size
					lowerCAmelCase   					=  num_audio_channels
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  sampling_rate



			def 							a_       (   self):




					"""simple docstring"""


					return {
					    "spectrogram_length": self.spectrogram_length,
					    "feature_size": self.feature_size,
					    "num_audio_channels": self.num_audio_channels,
					    "hop_length": self.hop_length,
					    "chunk_length": self.chunk_length,
					    "sampling_rate": self.sampling_rate,
					}







			def 							a_       (   self       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=False):




					"""simple docstring"""


					def _flatten(__lowerCAmelCase):
							return list(itertools.chain(*__lowerCAmelCase))

					if equal_length:
							lowerCAmelCase   					=  [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
					else:
							# make sure that inputs increase in size
							lowerCAmelCase   					=  [
							    floats_list((x, self.feature_size))
							    for x in range(self.min_seq_length       ,    self.max_seq_length       ,    self.seq_length_diff)
							]
					if numpify:
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase) for x in speech_inputs]
					return speech_inputs







@require_torch
@require_torchaudio
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       TvltFeatureExtractor



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  TvltFeatureExtractionTester(self)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_dict)
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """spectrogram_length"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """feature_size"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """num_audio_channels"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """hop_length"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """chunk_length"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """sampling_rate"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_dict)

					with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase   					=  feat_extract_first.save_pretrained(__lowerCAmelCase)[0]
							check_json_file_has_correct_format(__lowerCAmelCase)
							lowerCAmelCase   					=  self.feature_extraction_class.from_pretrained(__lowerCAmelCase)

					lowerCAmelCase   					=  feat_extract_first.to_dict()
					lowerCAmelCase   					=  feat_extract_second.to_dict()
					lowerCAmelCase   					=  dict_first.pop("""mel_filters""")
					lowerCAmelCase   					=  dict_second.pop("""mel_filters""")
					self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase))
					self.assertEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_dict)

					with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase   					=  os.path.join(__lowerCAmelCase       ,    """feat_extract.json""")
							feat_extract_first.to_json_file(__lowerCAmelCase)
							lowerCAmelCase   					=  self.feature_extraction_class.from_json_file(__lowerCAmelCase)

					lowerCAmelCase   					=  feat_extract_first.to_dict()
					lowerCAmelCase   					=  feat_extract_second.to_dict()
					lowerCAmelCase   					=  dict_first.pop("""mel_filters""")
					lowerCAmelCase   					=  dict_second.pop("""mel_filters""")
					self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase))
					self.assertEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_dict)

					# create three inputs of length 800, 1000, and 1200
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in range(800       ,    1400       ,    200)]
					lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase) for speech_input in speech_inputs]

					# Test not batched input
					lowerCAmelCase   					=  feature_extractor(np_speech_inputs[0]       ,    return_tensors="""np"""       ,    sampling_rate=44100).audio_values

					self.assertTrue(encoded_audios.ndim == 4)
					self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
					self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
					self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

					# Test batched
					lowerCAmelCase   					=  feature_extractor(__lowerCAmelCase       ,    return_tensors="""np"""       ,    sampling_rate=44100).audio_values

					self.assertTrue(encoded_audios.ndim == 4)
					self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
					self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
					self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

					# Test audio masking
					lowerCAmelCase   					=  feature_extractor(
					    __lowerCAmelCase       ,    return_tensors="""np"""       ,    sampling_rate=44100       ,    mask_audio=__lowerCAmelCase).audio_values

					self.assertTrue(encoded_audios.ndim == 4)
					self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
					self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
					self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

					# Test 2-D numpy arrays are batched.
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in (800, 800, 800)]
					lowerCAmelCase   					=  np.asarray(__lowerCAmelCase)
					lowerCAmelCase   					=  feature_extractor(__lowerCAmelCase       ,    return_tensors="""np"""       ,    sampling_rate=44100).audio_values
					self.assertTrue(encoded_audios.ndim == 4)
					self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
					self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
					self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  load_dataset("""hf-internal-testing/librispeech_asr_dummy"""       ,    """clean"""       ,    split="""validation""")
					# automatic decoding with librispeech
					lowerCAmelCase   					=  ds.sort("""id""").select(range(__lowerCAmelCase))[:num_samples]["""audio"""]

					return [x["array"] for x in speech_samples]







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self._load_datasamples(1)
					lowerCAmelCase   					=  TvltFeatureExtractor()
					lowerCAmelCase   					=  feature_extractor(__lowerCAmelCase       ,    return_tensors="""pt""").audio_values

					self.assertEquals(audio_values.shape       ,    (1, 1, 192, 128))

					lowerCAmelCase   					=  torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]])
					self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,    __lowerCAmelCase       ,    atol=1E-4))





'''simple docstring'''




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


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  n_fft
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  chunk_length * sampling_rate
					lowerCAmelCase   					=  self.n_samples // hop_length
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2       ,    num_mel_filters=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    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"""       ,    )
					lowerCAmelCase   					=  log_spec[:, :-1]
					lowerCAmelCase   					=  np.maximum(__lowerCAmelCase       ,    log_spec.max() - 8.0)
					lowerCAmelCase   					=  (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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase   					=  np.array(__lowerCAmelCase       ,    np.intaa)
							lowerCAmelCase   					=  []

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

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

					return normed_input_values



			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = "max_length"       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					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.""")

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

					if is_batched:
							lowerCAmelCase   					=  [np.asarray([speech]       ,    dtype=np.floataa).T for speech in raw_speech]
					elif not is_batched and not isinstance(__lowerCAmelCase       ,    np.ndarray):
							lowerCAmelCase   					=  np.asarray(__lowerCAmelCase       ,    dtype=np.floataa)
					elif isinstance(__lowerCAmelCase       ,    np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
							lowerCAmelCase   					=  raw_speech.astype(np.floataa)

					# always return batch
					if not is_batched:
							lowerCAmelCase   					=  [np.asarray([raw_speech]).T]

					lowerCAmelCase   					=  BatchFeature({"""input_features""": raw_speech})

					# convert into correct format for padding

					lowerCAmelCase   					=  self.pad(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    return_attention_mask=return_attention_mask or do_normalize       ,    )

					# zero-mean and unit-variance normalization
					if do_normalize:
							lowerCAmelCase   					=  self.zero_mean_unit_var_norm(
							    padded_inputs["""input_features"""]       ,    attention_mask=padded_inputs["""attention_mask"""]       ,    padding_value=self.padding_value       ,    )
							lowerCAmelCase   					=  np.stack(padded_inputs["""input_features"""]       ,    axis=0)

					# make sure list is in array format
					lowerCAmelCase   					=  padded_inputs.get("""input_features""").transpose(2       ,    0       ,    1)

					lowerCAmelCase   					=  [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    dtype=np.floataa) for feature in input_features]
					else:
							lowerCAmelCase   					=  input_features

					if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							lowerCAmelCase   					=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

					if return_tensors is not None:
							lowerCAmelCase   					=  padded_inputs.convert_to_tensors(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


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




