
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


A__			:    Any         =				logging.get_logger(__name__)

A__			:    List[Any]         =				{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__			:    List[Any]         =				{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
A__			:    Optional[int]         =				{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
A__			:    Optional[Any]         =				{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

A__			:    str         =				{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
A__			:    List[Any]         =				{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
A__			:    Union[str, Any]         =				{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


A__			:    int         =				{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A__			:    str         =				{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A__			:    int         =				{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



A__			:    List[Any]         =				collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A__			:    int         =				collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__			:    int         =				r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '
@add_start_docstrings(A__			)
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __call__(  self		:					int,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Union[bool, str] = False,						lowerCamelCase		:					Union[bool, str] = False,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						lowerCamelCase		:					Optional[bool] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								if titles is None and texts is None:
												return super().__call__(
												    lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=lowerCamelCase,						return_tensors=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								elif titles is None or texts is None:
												lowercase__     				=    titles if texts is None else texts
												return super().__call__(
												    lowerCamelCase,						lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=lowerCamelCase,						return_tensors=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    titles if not isinstance(lowerCamelCase,						lowerCamelCase						) else [titles]
								lowercase__     				=    texts if not isinstance(lowerCamelCase,						lowerCamelCase						) else [texts]
								lowercase__     				=    len(lowerCamelCase						)
								lowercase__     				=    questions if not isinstance(lowerCamelCase,						lowerCamelCase						) else [questions] * n_passages
								if len(lowerCamelCase						) != len(lowerCamelCase						):
												raise ValueError(
												    F"""There should be as many titles than texts but got {len(lowerCamelCase						)} titles and {len(lowerCamelCase						)} texts."""						)
								lowercase__     				=    super().__call__(lowerCamelCase,						lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase						)['''input_ids''']
								lowercase__     				=    super().__call__(lowerCamelCase,						add_special_tokens=lowerCamelCase,						padding=lowerCamelCase,						truncation=lowerCamelCase						)['''input_ids''']
								lowercase__     				=    {
								    '''input_ids''': [
								        (encoded_question_and_title + encoded_text)[:max_length]
								        if max_length is not None and truncation
								        else encoded_question_and_title + encoded_text
								        for encoded_question_and_title, encoded_text in zip(lowerCamelCase,						lowerCamelCase						)
								    ]
								}
								if return_attention_mask is not False:
												lowercase__     				=    []
												for input_ids in encoded_inputs["input_ids"]:
																attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
												lowercase__     				=    attention_mask
								return self.pad(lowerCamelCase,						padding=lowerCamelCase,						max_length=lowerCamelCase,						return_tensors=lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					BatchEncoding,						lowerCamelCase		:					DPRReaderOutput,						lowerCamelCase		:					int = 16,						lowerCamelCase		:					int = 64,						lowerCamelCase		:					int = 4,						):

								'''simple docstring'''
								lowercase__     				=    reader_input['''input_ids''']
								lowercase__				,		lowercase__				,		lowercase__     				=    reader_output[:3]
								lowercase__     				=    len(lowerCamelCase						)
								lowercase__     				=    sorted(range(lowerCamelCase						),						reverse=lowerCamelCase,						key=relevance_logits.__getitem__						)
								lowercase__     				=    []
								for doc_id in sorted_docs:
												lowercase__     				=    list(input_ids[doc_id]						)
												# assuming question & title information is at the beginning of the sequence
												lowercase__     				=    sequence_ids.index(self.sep_token_id,						2						) + 1  # second sep id
												if sequence_ids[-1] == self.pad_token_id:
																lowercase__     				=    sequence_ids.index(self.pad_token_id						)
												else:
																lowercase__     				=    len(lowerCamelCase						)

												lowercase__     				=    self._get_best_spans(
												    start_logits=start_logits[doc_id][passage_offset:sequence_len],						end_logits=end_logits[doc_id][passage_offset:sequence_len],						max_answer_length=lowerCamelCase,						top_spans=lowerCamelCase,						)
												for start_index, end_index in best_spans:
																start_index += passage_offset
																end_index += passage_offset
																nbest_spans_predictions.append(
																    DPRSpanPrediction(
																        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],						relevance_score=relevance_logits[doc_id],						doc_id=lowerCamelCase,						start_index=lowerCamelCase,						end_index=lowerCamelCase,						text=self.decode(sequence_ids[start_index : end_index + 1]						),						)						)
												if len(lowerCamelCase						) >= num_spans:
																break
								return nbest_spans_predictions[:num_spans]




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[int],						lowerCamelCase		:					List[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						):

								'''simple docstring'''
								lowercase__     				=    []
								for start_index, start_score in enumerate(lowerCamelCase						):
												for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
																scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
								lowercase__     				=    sorted(lowerCamelCase,						key=lambda lowerCamelCase						: x[1],						reverse=lowerCamelCase						)
								lowercase__     				=    []
								for (start_index, end_index), score in scores:
												if start_index > end_index:
																raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""						)
												lowercase__     				=    end_index - start_index + 1
												if length > max_answer_length:
																raise ValueError(F"""Span is too long: {length} > {max_answer_length}"""						)
												if any(
												    start_index <= prev_start_index <= prev_end_index <= end_index
												    or prev_start_index <= start_index <= end_index <= prev_end_index
												    for (prev_start_index, prev_end_index) in chosen_span_intervals						):
																continue
												chosen_span_intervals.append((start_index, end_index)						)

												if len(lowerCamelCase						) == top_spans:
																break
								return chosen_span_intervals



@add_end_docstrings(A__			)
class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= READER_PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= READER_PRETRAINED_INIT_CONFIGURATION
				lowercase__       							= ["""input_ids""", """attention_mask"""]












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__			:    Tuple         =				logging.get_logger(__name__)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""input_features""", """is_longer"""]




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					int=64,						lowerCamelCase		:					Union[str, Any]=48_000,						lowerCamelCase		:					str=480,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					List[Any]=1_024,						lowerCamelCase		:					Optional[int]=0.0,						lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					float = 0,						lowerCamelCase		:					float = 14_000,						lowerCamelCase		:					int = None,						lowerCamelCase		:					str = "fusion",						lowerCamelCase		:					str = "repeatpad",						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    feature_size=lowerCamelCase,						sampling_rate=lowerCamelCase,						padding_value=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    top_db
								lowercase__     				=    truncation
								lowercase__     				=    padding
								lowercase__     				=    fft_window_size
								lowercase__     				=    (fft_window_size >> 1) + 1
								lowercase__     				=    hop_length
								lowercase__     				=    max_length_s
								lowercase__     				=    max_length_s * sampling_rate
								lowercase__     				=    sampling_rate
								lowercase__     				=    frequency_min
								lowercase__     				=    frequency_max
								lowercase__     				=    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''',						)
								lowercase__     				=    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        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    copy.deepcopy(self.__dict__						)
								lowercase__     				=    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        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.array,						lowerCamelCase		:					Optional[np.array] = None						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    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
												lowercase__     				=    [0]
								if len(ranges[2]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								# randomly choose index for each part
								lowercase__     				=    np.random.choice(ranges[0]						)
								lowercase__     				=    np.random.choice(ranges[1]						)
								lowercase__     				=    np.random.choice(ranges[2]						)

								lowercase__     				=    mel[idx_front : idx_front + chunk_frames, :]
								lowercase__     				=    mel[idx_middle : idx_middle + chunk_frames, :]
								lowercase__     				=    mel[idx_back : idx_back + chunk_frames, :]

								lowercase__     				=    torch.tensor(mel[None, None, :]						)
								lowercase__     				=    torch.nn.functional.interpolate(
								    lowerCamelCase,						size=[chunk_frames, 64],						mode='''bilinear''',						align_corners=lowerCamelCase						)
								lowercase__     				=    mel_shrink[0][0].numpy()
								lowercase__     				=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],						axis=0						)
								return mel_fusion




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.array,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																lowercase__     				=    True
																# random crop to max_length (for compatibility) -> this should be handled by self.pad
																lowercase__     				=    len(lowerCamelCase						) - max_length
																lowercase__     				=    np.random.randint(0,						overflow + 1						)
																lowercase__     				=    waveform[idx : idx + max_length]
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]
												elif truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																lowercase__     				=    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.
																				lowercase__     				=    np.stack([mel, mel, mel, mel],						axis=0						)
																				lowercase__     				=    False
																else:
																				lowercase__     				=    self._random_mel_fusion(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
																				lowercase__     				=    True
												else:
																raise NotImplementedError(F"""data_truncating {truncation} not implemented"""						)

								else:
												lowercase__     				=    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":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						n_repeat + 1						)						)[:max_length]
																if padding == "repeatpad":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						lowerCamelCase						)						)
																lowercase__     				=    np.pad(lowerCamelCase,						(0, max_length - waveform.shape[0]),						mode='''constant''',						constant_values=0						)

												if truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    np.stack([input_mel, input_mel, input_mel, input_mel],						axis=0						)
												else:
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]

								return input_mel, longer




				def __call__(  self		:					Union[str, Any],						lowerCamelCase		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],						lowerCamelCase		:					str = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    truncation if truncation is not None else self.truncation
								lowercase__     				=    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.'''						)

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

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

								# always return batch
								if not is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase						)]

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

								lowercase__     				=    []
								lowercase__     				=    []
								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
												lowercase__     				=    np.random.randint(0,						len(lowerCamelCase						)						)
												lowercase__     				=    True

								if isinstance(input_mel[0],						lowerCamelCase						):
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								lowercase__     				=    [[longer] for longer in is_longer]

								lowercase__     				=    {'''input_features''': input_mel, '''is_longer''': is_longer}
								lowercase__     				=    BatchFeature(lowerCamelCase						)

								if return_tensors is not None:
												lowercase__     				=    input_features.convert_to_tensors(lowerCamelCase						)

								return input_features




