"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)


class   lowercase      (       A_       ):
							lowercase__  :    int									=							["input_features", "attention_mask"]




							def __init__(  self				:   int		,							_UpperCamelCase				:   Optional[Any]=80		,							_UpperCamelCase				:   str=16_000		,							_UpperCamelCase				:   Optional[int]=80		,							_UpperCamelCase				:   Optional[Any]=0.0		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   Optional[int]=True		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			int:



												'''simple docstring'''

												super().__init__(feature_size=snake_case__		,							sampling_rate=snake_case__		,							padding_value=snake_case__		,							**snake_case__ )
												SCREAMING_SNAKE_CASE           =    num_mel_bins
												SCREAMING_SNAKE_CASE           =    do_ceptral_normalize
												SCREAMING_SNAKE_CASE           =    normalize_means
												SCREAMING_SNAKE_CASE           =    normalize_vars
												SCREAMING_SNAKE_CASE           =    True




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   np.ndarray		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
												SCREAMING_SNAKE_CASE           =    torch.from_numpy(snake_case__ ).unsqueeze(0 )
												SCREAMING_SNAKE_CASE           =    ta_kaldi.fbank(snake_case__		,							num_mel_bins=self.num_mel_bins		,							sample_frequency=self.sampling_rate )
												return features.numpy()




							@staticmethod
							def 		__snake_case(  _UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[bool] = True		,							_UpperCamelCase				:   Optional[bool] = True		,							_UpperCamelCase				:   float = 0.0		,							)   ->			np.ndarray:



												'''simple docstring'''

												if normalize_means:
																	SCREAMING_SNAKE_CASE           =    x[:input_length].mean(axis=0 )
																	SCREAMING_SNAKE_CASE           =    np.subtract(snake_case__		,							snake_case__ )
												if normalize_vars:
																	SCREAMING_SNAKE_CASE           =    x[:input_length].std(axis=0 )
																	SCREAMING_SNAKE_CASE           =    np.divide(snake_case__		,							snake_case__ )

												if input_length < x.shape[0]:
																	SCREAMING_SNAKE_CASE           =    padding_value

												# make sure array is in float32
												SCREAMING_SNAKE_CASE           =    x.astype(np.floataa )

												return x




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   List[np.ndarray]		,							_UpperCamelCase				:   Optional[np.ndarray] = None )   ->			List[np.ndarray]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [
												    self.utterance_cmvn(snake_case__		,							snake_case__		,							self.normalize_means		,							self.normalize_vars		,							self.padding_value )
												    for x, n in zip(snake_case__		,							snake_case__ )
												]





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



												'''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} was trained using a sampling rate of"
																						    F" {self.sampling_rate}. Please make sure that the provided `raw_speech` 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." )

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

												if is_batched:
																	SCREAMING_SNAKE_CASE           =    [np.asarray(snake_case__		,							dtype=np.floataa ) for speech in raw_speech]
												elif not is_batched and not isinstance(snake_case__		,							np.ndarray ):
																	SCREAMING_SNAKE_CASE           =    np.asarray(snake_case__		,							dtype=np.floataa )
												elif isinstance(snake_case__		,							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           =    [raw_speech]

												# extract fbank features
												SCREAMING_SNAKE_CASE           =    [self._extract_fbank_features(snake_case__ ) for waveform in raw_speech]

												# convert into correct format for padding
												SCREAMING_SNAKE_CASE           =    BatchFeature({"input_features": features} )

												SCREAMING_SNAKE_CASE           =    self.pad(
												    snake_case__		,							padding=snake_case__		,							max_length=snake_case__		,							truncation=snake_case__		,							pad_to_multiple_of=snake_case__		,							return_attention_mask=snake_case__		,							**snake_case__		,							)

												# make sure list is in array format
												SCREAMING_SNAKE_CASE           =    padded_inputs.get("input_features" )
												if isinstance(input_features[0]		,							snake_case__ ):
																	SCREAMING_SNAKE_CASE           =    [np.asarray(snake_case__		,							dtype=np.floataa ) for feature in input_features]

												SCREAMING_SNAKE_CASE           =    padded_inputs.get("attention_mask" )
												if attention_mask is not None:
																	SCREAMING_SNAKE_CASE           =    [np.asarray(snake_case__		,							dtype=np.intaa ) for array in attention_mask]

												# Utterance-level cepstral mean and variance normalization
												if self.do_ceptral_normalize:
																	SCREAMING_SNAKE_CASE           =    (
																	    np.array(snake_case__		,							dtype=np.intaa )
																	    if self._get_padding_strategies(snake_case__		,							max_length=snake_case__ ) is not PaddingStrategy.DO_NOT_PAD
																	    else None
																	)
																	SCREAMING_SNAKE_CASE           =    self.normalize(
																	    padded_inputs["input_features"]		,							attention_mask=snake_case__ )

												if return_tensors is not None:
																	SCREAMING_SNAKE_CASE           =    padded_inputs.convert_to_tensors(snake_case__ )

												return padded_inputs





import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
							import jax.numpy as jnp

							from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )["last_hidden_state"]
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# compare the actual values for a slice of last dim
												self.assertTrue(jnp.allclose(output[:, :, -1]		,							_UpperCamelCase		,							atol=1e-3 ) )




