


'''simple docstring'''



from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__A	=logging.get_logger(__name__)


__A	=OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

__A	=OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

__A	=OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

__A	=OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

__A	=OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

__A	=OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

__A	=OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

__A	=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__A	=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__A	=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__A	=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__A	=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__A	=_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Union[str, Any]   =					FLAX_MODEL_MAPPING

__A	=auto_class_update(FlaxAutoModel)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Union[str, Any]   =					FLAX_MODEL_FOR_PRETRAINING_MAPPING

__A	=auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Dict   =					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

__A	=auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Union[str, Any]   =					FLAX_MODEL_FOR_MASKED_LM_MAPPING

__A	=auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :int   =					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__A	=auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :List[Any]   =					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

__A	=auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :List[Any]   =					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

__A	=auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :List[str]   =					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__A	=auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Union[str, Any]   =					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

__A	=auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Union[str, Any]   =					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

__A	=auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :List[Any]   =					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__A	=auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :Dict   =					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

__A	=auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')


class 		_snake_case					(     _BaseAutoModelClass	):
					lowerCAmelCase      :int   =					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

__A	=auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)



'''simple docstring'''



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					['''input_features''', '''is_longer''']




					def __init__(      self							,							_lowerCamelCase=64							,							_lowerCamelCase=4_8000							,							_lowerCamelCase=480							,							_lowerCamelCase=10							,							_lowerCamelCase=1024							,							_lowerCamelCase=0.0							,							_lowerCamelCase=False							,							_lowerCamelCase = 0							,							_lowerCamelCase = 1_4000							,							_lowerCamelCase = None							,							_lowerCamelCase = "fusion"							,							_lowerCamelCase = "repeatpad"							,							**_lowerCamelCase							,							):
								super().__init__(
								    feature_size=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							padding_value=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							**_lowerCamelCase							,							)
								UpperCAmelCase__  :					Dict        =     top_db
								UpperCAmelCase__  :					int        =     truncation
								UpperCAmelCase__  :					Optional[Any]        =     padding
								UpperCAmelCase__  :					Union[str, Any]        =     fft_window_size
								UpperCAmelCase__  :					List[Any]        =     (fft_window_size >> 1) + 1
								UpperCAmelCase__  :					Optional[int]        =     hop_length
								UpperCAmelCase__  :					int        =     max_length_s
								UpperCAmelCase__  :					Tuple        =     max_length_s * sampling_rate
								UpperCAmelCase__  :					List[Any]        =     sampling_rate
								UpperCAmelCase__  :					Tuple        =     frequency_min
								UpperCAmelCase__  :					Dict        =     frequency_max
								UpperCAmelCase__  :					Union[str, Any]        =     mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							,							num_mel_filters=_lowerCamelCase							,							min_frequency=_lowerCamelCase							,							max_frequency=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							norm=_lowerCamelCase							,							mel_scale="""htk"""							,							)
								UpperCAmelCase__  :					Optional[int]        =     mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							,							num_mel_filters=_lowerCamelCase							,							min_frequency=_lowerCamelCase							,							max_frequency=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							norm="""slaney"""							,							mel_scale="""slaney"""							,							)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     copy.deepcopy(self.__dict__)
								UpperCAmelCase__  :					Optional[int]        =     self.__class__.__name__
								if "mel_filters" in output:
											del output["mel_filters"]
								if "mel_filters_slaney" in output:
											del output["mel_filters_slaney"]
								return output




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					str        =     spectrogram(
								    _lowerCamelCase							,							window_function(self.fft_window_size							,							"""hann""")							,							frame_length=self.fft_window_size							,							hop_length=self.hop_length							,							power=2.0							,							mel_filters=_lowerCamelCase							,							log_mel="""dB"""							,							)
								return log_mel_spectrogram.T




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     np.array_split(list(range(0							,							total_frames - chunk_frames + 1))							,							3)
								if len(ranges[1]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					List[Any]        =     [0]
								if len(ranges[2]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					Union[str, Any]        =     [0]
								# randomly choose index for each part
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[0])
								UpperCAmelCase__  :					Optional[int]        =     np.random.choice(ranges[1])
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[2])

								UpperCAmelCase__  :					str        =     mel[idx_front : idx_front + chunk_frames, :]
								UpperCAmelCase__  :					Optional[int]        =     mel[idx_middle : idx_middle + chunk_frames, :]
								UpperCAmelCase__  :					Tuple        =     mel[idx_back : idx_back + chunk_frames, :]

								UpperCAmelCase__  :					int        =     torch.tensor(mel[None, None, :])
								UpperCAmelCase__  :					Optional[int]        =     torch.nn.functional.interpolate(
								    _lowerCamelCase							,							size=[chunk_frames, 64]							,							mode="""bilinear"""							,							align_corners=_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     mel_shrink[0][0].numpy()
								UpperCAmelCase__  :					Optional[Any]        =     np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]							,							axis=0)
								return mel_fusion




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								if waveform.shape[0] > max_length:
											if truncation == "rand_trunc":
														UpperCAmelCase__  :					List[str]        =     True
														# random crop to max_length (for compatibility) -> this should be handled by self.pad
														UpperCAmelCase__  :					List[Any]        =     len(_lowerCamelCase) - max_length
														UpperCAmelCase__  :					Optional[Any]        =     np.random.randint(0							,							overflow + 1)
														UpperCAmelCase__  :					int        =     waveform[idx : idx + max_length]
														UpperCAmelCase__  :					Tuple        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters_slaney)[None, :]
											elif truncation == "fusion":
														UpperCAmelCase__  :					Any        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					int        =     max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
														UpperCAmelCase__  :					int        =     mel.shape[0]
														if chunk_frames == total_frames:
																	# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																	# In this case, we just use the whole audio.
																	UpperCAmelCase__  :					Optional[Any]        =     np.stack([mel, mel, mel, mel]							,							axis=0)
																	UpperCAmelCase__  :					List[Any]        =     False
														else:
																	UpperCAmelCase__  :					Tuple        =     self._random_mel_fusion(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
																	UpperCAmelCase__  :					str        =     True
											else:
														raise NotImplementedError(f'''data_truncating {truncation} not implemented''')

								else:
											UpperCAmelCase__  :					str        =     False
											# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
											if waveform.shape[0] < max_length:
														if padding == "repeat":
																	UpperCAmelCase__  :					int        =     int(max_length / len(_lowerCamelCase))
																	UpperCAmelCase__  :					int        =     np.stack(np.tile(_lowerCamelCase							,							n_repeat + 1))[:max_length]
														if padding == "repeatpad":
																	UpperCAmelCase__  :					str        =     int(max_length / len(_lowerCamelCase))
																	UpperCAmelCase__  :					str        =     np.stack(np.tile(_lowerCamelCase							,							_lowerCamelCase))
														UpperCAmelCase__  :					Optional[Any]        =     np.pad(_lowerCamelCase							,							(0, max_length - waveform.shape[0])							,							mode="""constant"""							,							constant_values=0)

											if truncation == "fusion":
														UpperCAmelCase__  :					List[Any]        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					Dict        =     np.stack([input_mel, input_mel, input_mel, input_mel]							,							axis=0)
											else:
														UpperCAmelCase__  :					int        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters_slaney)[None, :]

								return input_mel, longer




					def __call__(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     truncation if truncation is not None else self.truncation
								UpperCAmelCase__  :					List[Any]        =     padding if padding else self.padding

								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.""")

								UpperCAmelCase__  :					int        =     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}''')
								UpperCAmelCase__  :					List[str]        =     is_batched_numpy or (
								    isinstance(_lowerCamelCase							,							(list, tuple)) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)))
								)

								if is_batched:
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for speech in raw_speech]
								elif not is_batched and not isinstance(_lowerCamelCase							,							np.ndarray):
											UpperCAmelCase__  :					Optional[int]        =     np.asarray(_lowerCamelCase							,							dtype=np.floataa)
								elif isinstance(_lowerCamelCase							,							np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
											UpperCAmelCase__  :					str        =     raw_speech.astype(np.floataa)

								# always return batch
								if not is_batched:
											UpperCAmelCase__  :					int        =     [np.asarray(_lowerCamelCase)]

								# convert to mel spectrogram, truncate and pad if needed.
								UpperCAmelCase__  :					int        =     [
								    self._get_input_mel(_lowerCamelCase							,							max_length if max_length else self.nb_max_samples							,							_lowerCamelCase							,							_lowerCamelCase)
								    for waveform in raw_speech
								]

								UpperCAmelCase__  :					int        =     []
								UpperCAmelCase__  :					List[str]        =     []
								for mel, longer in padded_inputs:
											input_mel.append(_lowerCamelCase)
											is_longer.append(_lowerCamelCase)

								if truncation == "fusion" and sum(_lowerCamelCase) == 0:
											# if no audio is longer than 10s, then randomly select one audio to be longer
											UpperCAmelCase__  :					Optional[int]        =     np.random.randint(0							,							len(_lowerCamelCase))
											UpperCAmelCase__  :					Tuple        =     True

								if isinstance(input_mel[0]							,							_lowerCamelCase):
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for feature in input_mel]

								# is_longer is a list of bool
								UpperCAmelCase__  :					str        =     [[longer] for longer in is_longer]

								UpperCAmelCase__  :					Optional[int]        =     {"""input_features""": input_mel, """is_longer""": is_longer}
								UpperCAmelCase__  :					Optional[int]        =     BatchFeature(_lowerCamelCase)

								if return_tensors is not None:
											UpperCAmelCase__  :					str        =     input_features.convert_to_tensors(_lowerCamelCase)

								return input_features