"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{"vocab_file": "sentencepiece.model"}

_UpperCamelCase								=		{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_UpperCamelCase								=		{
    "google/rembert": 2_56,
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				VOCAB_FILES_NAMES
			_UpperCamelCase  :						List[str]					     =				PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase  :						Any					     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(			self   ,      snake_case_   ,      snake_case_=False   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="[CLS]"   ,      snake_case_="[SEP]"   ,      snake_case_="[UNK]"   ,      snake_case_="[SEP]"   ,      snake_case_="[PAD]"   ,      snake_case_="[CLS]"   ,      snake_case_="[MASK]"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    do_lower_case=snake_case_   ,      remove_space=snake_case_   ,      keep_accents=snake_case_   ,      bos_token=snake_case_   ,      eos_token=snake_case_   ,      unk_token=snake_case_   ,      sep_token=snake_case_   ,      pad_token=snake_case_   ,      cls_token=snake_case_   ,      mask_token=snake_case_   ,      **snake_case_   ,      )

										A__       :     List[Any]								=			do_lower_case
										A__       :     Dict								=			remove_space
										A__       :     Optional[int]								=			keep_accents
										A__       :     Tuple								=			vocab_file

										A__       :     List[str]								=			spm.SentencePieceProcessor()
										self.sp_model.Load(snake_case_							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return len(self.sp_model							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			{self.convert_ids_to_tokens(snake_case_							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab





			def __getstate__(			self							):







										'''simple docstring'''


										A__       :     str								=			self.__dict__.copy()
										A__       :     Tuple								=			None
										return state





			def __setstate__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			d
										A__       :     int								=			spm.SentencePieceProcessor()
										self.sp_model.Load(self.vocab_file							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										A__       :     Tuple								=			self.sp_model.EncodeAsPieces(snake_case_							)
										return pieces





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.PieceToId(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.IdToPiece(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.sp_model.decode_pieces(snake_case_							)
										return out_string





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     List[Any]								=			[self.sep_token_id]
										A__       :     str								=			[self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = False							):







										'''simple docstring'''


										if already_has_special_tokens:
																	if token_ids_a is not None:
																								raise ValueError(
																								    """You should not supply a second sequence if the provided sequence of """
																								    """ids is already formatted with special tokens for the model."""							)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is not None:
																	return [1] + ([0] * len(snake_case_							)) + [1] + ([0] * len(snake_case_							)) + [1]
										return [1] + ([0] * len(snake_case_							)) + [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     int								=			[self.sep_token_id]
										A__       :     Any								=			[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										if not os.path.isdir(snake_case_							):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(snake_case_							)							)
																	return
										A__       :     Any								=			os.path.join(
										    snake_case_   ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
																	copyfile(self.vocab_file   ,      snake_case_							)

										return (out_vocab_file,)






"""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


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				['input_features', 'is_longer']





			def __init__(			self   ,      snake_case_=64   ,      snake_case_=48_000   ,      snake_case_=480   ,      snake_case_=10   ,      snake_case_=1_024   ,      snake_case_=0.0   ,      snake_case_=False   ,      snake_case_ = 0   ,      snake_case_ = 14_000   ,      snake_case_ = None   ,      snake_case_ = "fusion"   ,      snake_case_ = "repeatpad"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    feature_size=snake_case_   ,      sampling_rate=snake_case_   ,      padding_value=snake_case_   ,      return_attention_mask=snake_case_   ,      **snake_case_   ,      )
										A__       :     List[str]								=			top_db
										A__       :     Optional[int]								=			truncation
										A__       :     str								=			padding
										A__       :     str								=			fft_window_size
										A__       :     Any								=			(fft_window_size >> 1) + 1
										A__       :     Optional[Any]								=			hop_length
										A__       :     Any								=			max_length_s
										A__       :     int								=			max_length_s * sampling_rate
										A__       :     Any								=			sampling_rate
										A__       :     List[str]								=			frequency_min
										A__       :     int								=			frequency_max
										A__       :     Tuple								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm=snake_case_   ,      mel_scale="""htk"""   ,      )
										A__       :     List[Any]								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm="""slaney"""   ,      mel_scale="""slaney"""   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			copy.deepcopy(self.__dict__							)
										A__       :     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 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Any								=			spectrogram(
										    snake_case_   ,      window_function(self.fft_window_size   ,      """hann"""							)   ,      frame_length=self.fft_window_size   ,      hop_length=self.hop_length   ,      power=2.0   ,      mel_filters=snake_case_   ,      log_mel="""dB"""   ,      )
										return log_mel_spectrogram.T





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			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
																	A__       :     Optional[Any]								=			[0]
										if len(ranges[2]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     str								=			[0]
										# randomly choose index for each part
										A__       :     int								=			np.random.choice(ranges[0]							)
										A__       :     Dict								=			np.random.choice(ranges[1]							)
										A__       :     Dict								=			np.random.choice(ranges[2]							)

										A__       :     Any								=			mel[idx_front : idx_front + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_middle : idx_middle + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_back : idx_back + chunk_frames, :]

										A__       :     Dict								=			torch.tensor(mel[None, None, :]							)
										A__       :     Dict								=			torch.nn.functional.interpolate(
										    snake_case_   ,      size=[chunk_frames, 64]   ,      mode="""bilinear"""   ,      align_corners=snake_case_							)
										A__       :     List[Any]								=			mel_shrink[0][0].numpy()
										A__       :     Dict								=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,      axis=0							)
										return mel_fusion





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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

										else:
																	A__       :     Tuple								=			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":
																															A__       :     Union[str, Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     List[Any]								=			np.stack(np.tile(snake_case_   ,      n_repeat + 1							)							)[:max_length]
																								if padding == "repeatpad":
																															A__       :     List[Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     Tuple								=			np.stack(np.tile(snake_case_   ,      snake_case_							)							)
																								A__       :     Tuple								=			np.pad(snake_case_   ,      (0, max_length - waveform.shape[0])   ,      mode="""constant"""   ,      constant_values=0							)

																	if truncation == "fusion":
																								A__       :     str								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     List[Any]								=			np.stack([input_mel, input_mel, input_mel, input_mel]   ,      axis=0							)
																	else:
																								A__       :     List[Any]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]

										return input_mel, longer





			def __call__(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Optional[Any]								=			truncation if truncation is not None else self.truncation
										A__       :     Union[str, 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."""							)

										A__       :     List[Any]								=			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}'''							)
										A__       :     int								=			is_batched_numpy or (
										    isinstance(snake_case_   ,      (list, tuple)							) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)							))
										)

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

										# always return batch
										if not is_batched:
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_							)]

										# convert to mel spectrogram, truncate and pad if needed.
										A__       :     Optional[Any]								=			[
										    self._get_input_mel(snake_case_   ,      max_length if max_length else self.nb_max_samples   ,      snake_case_   ,      snake_case_							)
										    for waveform in raw_speech
										]

										A__       :     List[str]								=			[]
										A__       :     Optional[Any]								=			[]
										for mel, longer in padded_inputs:
																	input_mel.append(snake_case_							)
																	is_longer.append(snake_case_							)

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

										if isinstance(input_mel[0]   ,      snake_case_							):
																	A__       :     Optional[Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for feature in input_mel]

										# is_longer is a list of bool
										A__       :     Union[str, Any]								=			[[longer] for longer in is_longer]

										A__       :     str								=			{"""input_features""": input_mel, """is_longer""": is_longer}
										A__       :     Optional[int]								=			BatchFeature(snake_case_							)

										if return_tensors is not None:
																	A__       :     Dict								=			input_features.convert_to_tensors(snake_case_							)

										return input_features





