




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.generation import (
       FlaxForcedBOSTokenLogitsProcessor,
       FlaxForcedEOSTokenLogitsProcessor,
       FlaxLogitsProcessorList,
       FlaxMinLengthLogitsProcessor,
       FlaxTemperatureLogitsWarper,
       FlaxTopKLogitsWarper,
       FlaxTopPLogitsWarper,
   )





@require_flax
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     jnp.ones((batch_size, length)		) / length
       return scores



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     20

       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(batch_size=2							,							length=_lowerCAmelCase		)

       # tweak scores to not be uniform anymore
       SCREAMING_SNAKE_CASE_      		=     scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
       SCREAMING_SNAKE_CASE_      		=     scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

       # compute softmax
       SCREAMING_SNAKE_CASE_      		=     jax.nn.softmax(_lowerCAmelCase							,							axis=-1		)

       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=0.5		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=1.3		)

       SCREAMING_SNAKE_CASE_      		=     jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase							,							scores.copy()							,							cur_len=_lowerCAmelCase		)							,							axis=-1		)
       SCREAMING_SNAKE_CASE_      		=     jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase							,							scores.copy()							,							cur_len=_lowerCAmelCase		)							,							axis=-1		)

       # uniform distribution stays uniform
       self.assertTrue(jnp.allclose(probs[0, :]							,							warped_prob_sharp[0, :]							,							atol=1E-3		)		)
       self.assertTrue(jnp.allclose(probs[0, :]							,							warped_prob_smooth[0, :]							,							atol=1E-3		)		)

       # sharp peaks get higher, valleys get lower
       self.assertLess(probs[1, :].max()							,							warped_prob_sharp[1, :].max()		)
       self.assertGreater(probs[1, :].min()							,							warped_prob_sharp[1, :].min()		)

       # smooth peaks get lower, valleys get higher
       self.assertGreater(probs[1, :].max()							,							warped_prob_smooth[1, :].max()		)
       self.assertLess(probs[1, :].min()							,							warped_prob_smooth[1, :].min()		)



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     2

       # create ramp distribution
       SCREAMING_SNAKE_CASE_      		=     np.broadcast_to(np.arange(_lowerCAmelCase		)[None, :]							,							(batch_size, vocab_size)		).copy()
       SCREAMING_SNAKE_CASE_      		=     ramp_logits[1:, : vocab_size // 2] + vocab_size

       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(3		)

       SCREAMING_SNAKE_CASE_      		=     top_k_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # check that correct tokens are filtered
       self.assertListEqual(jnp.isinf(scores[0]		).tolist()							,							7 * [True] + 3 * [False]		)
       self.assertListEqual(jnp.isinf(scores[1]		).tolist()							,							2 * [True] + 3 * [False] + 5 * [True]		)

       # check special case
       SCREAMING_SNAKE_CASE_      		=     5
       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(top_k=1							,							filter_value=0.0							,							min_tokens_to_keep=3		)

       SCREAMING_SNAKE_CASE_      		=     np.broadcast_to(np.arange(_lowerCAmelCase		)[None, :]							,							(batch_size, length)		).copy()
       SCREAMING_SNAKE_CASE_      		=     top_k_warp_safety_check(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
       self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()							,							[2, 2]		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     2

       # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
       SCREAMING_SNAKE_CASE_      		=     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]		)		)

       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.8		)
       SCREAMING_SNAKE_CASE_      		=     np.exp(top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)		)

       # dist should be filtered to keep min num values so that sum is >= top_p
       # exp (-inf) => 0
       SCREAMING_SNAKE_CASE_      		=     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]		)
       self.assertTrue(np.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-3		)		)

       # check edge cases with negative and extreme logits
       SCREAMING_SNAKE_CASE_      		=     np.broadcast_to(np.arange(_lowerCAmelCase		)[None, :]							,							(batch_size, vocab_size)		).copy() - (
           vocab_size // 2
       )

       # make ramp_logits more extreme
       SCREAMING_SNAKE_CASE_      		=     ramp_logits[1] * 100.0

       # make sure at least 2 tokens are kept
       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.9							,							min_tokens_to_keep=2							,							filter_value=0.0		)
       SCREAMING_SNAKE_CASE_      		=     top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
       self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()							,							[3, 2]		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     20
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     0

       SCREAMING_SNAKE_CASE_      		=     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCAmelCase		)

       # check that min length is applied at length 5
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, 20)							,							vocab_size=20		)
       SCREAMING_SNAKE_CASE_      		=     5
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     min_dist_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()							,							4 * [-float('inf'		)]		)

       # check that min length is not applied anymore at length 15
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     15
       SCREAMING_SNAKE_CASE_      		=     min_dist_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertFalse(jnp.isinf(_lowerCAmelCase		).any()		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     20
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     0

       SCREAMING_SNAKE_CASE_      		=     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase		)

       # check that all scores are -inf except the bos_token_id score
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, 1)							,							vocab_size=20		)
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
       self.assertListEqual(scores[:, bos_token_id].tolist()							,							4 * [0]		)  # score for bos_token_id shold be zero

       # check that bos_token_id is not forced if current length is greater than 1
       SCREAMING_SNAKE_CASE_      		=     3
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertFalse(jnp.isinf(_lowerCAmelCase		).any()		)



 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     20
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     0
       SCREAMING_SNAKE_CASE_      		=     5

       SCREAMING_SNAKE_CASE_      		=     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase		)

       # check that all scores are -inf except the eos_token_id when max_length is reached
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, 4)							,							vocab_size=20		)
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
       self.assertListEqual(scores[:, eos_token_id].tolist()							,							4 * [0]		)  # score for eos_token_id should be zero

       # check that eos_token_id is not forced if max_length is not reached
       SCREAMING_SNAKE_CASE_      		=     3
       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     logits_processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       self.assertFalse(jnp.isinf(_lowerCAmelCase		).any()		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     15
       SCREAMING_SNAKE_CASE_      		=     2
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     15

       # dummy input_ids and scores
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, sequence_length)							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     input_ids.copy()

       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     scores.copy()

       # instantiate all dist processors
       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=0.5		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(3		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.8		)

       # instantiate all logits processors
       SCREAMING_SNAKE_CASE_      		=     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     10

       # no processor list
       SCREAMING_SNAKE_CASE_      		=     temp_dist_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     top_k_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     min_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     bos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     eos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # with processor list
       SCREAMING_SNAKE_CASE_      		=     FlaxLogitsProcessorList(
           [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
       SCREAMING_SNAKE_CASE_      		=     processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)

       # scores should be equal
       self.assertTrue(jnp.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-3		)		)

       # input_ids should never be changed
       self.assertListEqual(input_ids.tolist()							,							input_ids_comp.tolist()		)




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     10
       SCREAMING_SNAKE_CASE_      		=     15
       SCREAMING_SNAKE_CASE_      		=     2
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     15

       # dummy input_ids and scores
       SCREAMING_SNAKE_CASE_      		=     ids_tensor((batch_size, sequence_length)							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     input_ids.copy()

       SCREAMING_SNAKE_CASE_      		=     self._get_uniform_logits(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     scores.copy()

       # instantiate all dist processors
       SCREAMING_SNAKE_CASE_      		=     FlaxTemperatureLogitsWarper(temperature=0.5		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopKLogitsWarper(3		)
       SCREAMING_SNAKE_CASE_      		=     FlaxTopPLogitsWarper(0.8		)

       # instantiate all logits processors
       SCREAMING_SNAKE_CASE_      		=     FlaxMinLengthLogitsProcessor(min_length=10							,							eos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase							,							eos_token_id=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     10

       # no processor list
       def run_no_processor_list(_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
             SCREAMING_SNAKE_CASE_      		=     temp_dist_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     top_k_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     top_p_warp(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     min_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     bos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     eos_dist_proc(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             return scores

       # with processor list
       def run_processor_list(_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]		):
             SCREAMING_SNAKE_CASE_      		=     FlaxLogitsProcessorList(
                 [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
             SCREAMING_SNAKE_CASE_      		=     processor(_lowerCAmelCase							,							_lowerCAmelCase							,							cur_len=_lowerCAmelCase		)
             return scores

       SCREAMING_SNAKE_CASE_      		=     jax.jit(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     jax.jit(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     jitted_run_no_processor_list(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     jitted_run_processor_list(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)

       # scores should be equal
       self.assertTrue(jnp.allclose(_lowerCAmelCase							,							_lowerCAmelCase							,							atol=1E-3		)		)

       # input_ids should never be changed
       self.assertListEqual(input_ids.tolist()							,							input_ids_comp.tolist()		)





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


lowercase__							:	Tuple											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''input_values''', '''attention_mask''']






						def __init__(		self				:	List[Any]			,      __lowercase				:	int = 1			,      __lowercase				:	int = 1_60_00			,      __lowercase				:	float = 0.0			,      __lowercase				:	bool = False			,      __lowercase				:	int = 80			,      __lowercase				:	int = 16			,      __lowercase				:	int = 64			,      __lowercase				:	str = "hann_window"			,      __lowercase				:	float = 1.0			,      __lowercase				:	float = 80			,      __lowercase				:	float = 76_00			,      __lowercase				:	float = 1E-10			,      __lowercase				:	int = 2			,      __lowercase				:	bool = True			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							super().__init__(feature_size=__lowercase			,      sampling_rate=__lowercase			,      padding_value=__lowercase			,      **__lowercase		)
							snake_case_						       =	do_normalize
							snake_case_						       =	return_attention_mask

							snake_case_						       =	num_mel_bins
							snake_case_						       =	hop_length
							snake_case_						       =	win_length
							snake_case_						       =	win_function
							snake_case_						       =	frame_signal_scale
							snake_case_						       =	fmin
							snake_case_						       =	fmax
							snake_case_						       =	mel_floor
							snake_case_						       =	reduction_factor

							snake_case_						       =	win_length * sampling_rate // 10_00
							snake_case_						       =	hop_length * sampling_rate // 10_00
							snake_case_						       =	optimal_fft_length(self.sample_size		)
							snake_case_						       =	(self.n_fft // 2) + 1

							snake_case_						       =	window_function(window_length=self.sample_size			,      name=self.win_function			,      periodic=__lowercase		)

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






						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def 			snake_case__		(		__lowercase				:	List[np.ndarray]			,      __lowercase				:	List[np.ndarray]			,      __lowercase				:	float = 0.0		):






							"""simple docstring"""






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

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

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

							return normed_input_values






						def 			snake_case__		(		self				:	str			,      __lowercase				:	np.ndarray			,      ):






							"""simple docstring"""






							snake_case_						       =	spectrogram(
							    __lowercase			,      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				:	Optional[int]			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	Optional[int] = None			,      **__lowercase				:	List[str]			,      ):






							"""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:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )
							else:
								snake_case_						       =	None

							if audio_target is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )

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

							return inputs






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,      __lowercase				:	bool = False			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	isinstance(__lowercase			,      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}"		)
							snake_case_						       =	is_batched_numpy or (
							    isinstance(__lowercase			,      (list, tuple)		) and (isinstance(speech[0]			,      (np.ndarray, tuple, list)		))
							)

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

							# always return batch
							if not is_batched:
								snake_case_						       =	[speech]

							# needed to make pad() work on spectrogram inputs
							snake_case_						       =	self.feature_size

							# convert into correct format for padding
							if is_target:
								snake_case_						       =	[self._extract_mel_features(__lowercase		) for waveform in speech]
								snake_case_						       =	BatchFeature({"input_values": features}		)
								snake_case_						       =	self.num_mel_bins
							else:
								snake_case_						       =	BatchFeature({"input_values": speech}		)

							snake_case_						       =	self.pad(
							    __lowercase			,      padding=__lowercase			,      max_length=__lowercase			,      truncation=__lowercase			,      pad_to_multiple_of=__lowercase			,      return_attention_mask=__lowercase			,      **__lowercase			,      )

							snake_case_						       =	feature_size_hack

							# convert input values to correct format
							snake_case_						       =	padded_inputs["input_values"]
							if not isinstance(input_values[0]			,      np.ndarray		):
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for array in input_values]
							elif (
							    not isinstance(__lowercase			,      np.ndarray		)
							    and isinstance(input_values[0]			,      np.ndarray		)
							    and input_values[0].dtype is np.dtype(np.floataa		)
							):
								snake_case_						       =	[array.astype(np.floataa		) for array in input_values]
							elif isinstance(__lowercase			,      np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
								snake_case_						       =	input_values.astype(np.floataa		)

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

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

							if return_tensors is not None:
								snake_case_						       =	padded_inputs.convert_to_tensors(__lowercase		)

							return padded_inputs






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	super().to_dict()

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

							return output
