



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


__snake_case						:str      		=     logging.get_logger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    List[Any]											=						['''input_features''', '''is_longer''']



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


           '''simple docstring'''

           super().__init__(
               feature_size=__SCREAMING_SNAKE_CASE     ,		sampling_rate=__SCREAMING_SNAKE_CASE     ,		padding_value=__SCREAMING_SNAKE_CASE     ,		return_attention_mask=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				top_db
           __a						       =				truncation
           __a						       =				padding
           __a						       =				fft_window_size
           __a						       =				(fft_window_size >> 1) + 1
           __a						       =				hop_length
           __a						       =				max_length_s
           __a						       =				max_length_s * sampling_rate
           __a						       =				sampling_rate
           __a						       =				frequency_min
           __a						       =				frequency_max
           __a						       =				mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins     ,		num_mel_filters=__SCREAMING_SNAKE_CASE     ,		min_frequency=__SCREAMING_SNAKE_CASE     ,		max_frequency=__SCREAMING_SNAKE_CASE     ,		sampling_rate=__SCREAMING_SNAKE_CASE     ,		norm=__SCREAMING_SNAKE_CASE     ,		mel_scale='''htk'''     ,		)
           __a						       =				mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins     ,		num_mel_filters=__SCREAMING_SNAKE_CASE     ,		min_frequency=__SCREAMING_SNAKE_CASE     ,		max_frequency=__SCREAMING_SNAKE_CASE     ,		sampling_rate=__SCREAMING_SNAKE_CASE     ,		norm='''slaney'''     ,		mel_scale='''slaney'''     ,		)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				copy.deepcopy(self.__dict__)
           __a						       =				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							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     np.array     ,		__SCREAMING_SNAKE_CASE							:     Optional[np.array] = None):


           '''simple docstring'''

           __a						       =				spectrogram(
               __SCREAMING_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=__SCREAMING_SNAKE_CASE     ,		log_mel='''dB'''     ,		)
           return log_mel_spectrogram.T



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

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

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

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



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     np.array     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

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

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

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

           return input_mel, longer





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


           '''simple docstring'''

           __a						       =				truncation if truncation is not None else self.truncation
           __a						       =				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						       =				isinstance(__SCREAMING_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						       =				is_batched_numpy or (
               isinstance(__SCREAMING_SNAKE_CASE     ,		(list, tuple)) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)))
           )

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

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

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

           __a						       =				[]
           __a						       =				[]
           for mel, longer in padded_inputs:
               input_mel.append(__SCREAMING_SNAKE_CASE)
               is_longer.append(__SCREAMING_SNAKE_CASE)

           if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE) == 0:
               # if no audio is longer than 10s, then randomly select one audio to be longer
               __a						       =				np.random.randint(0     ,		len(__SCREAMING_SNAKE_CASE))
               __a						       =				True

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

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

           __a						       =				{'''input_features''': input_mel, '''is_longer''': is_longer}
           __a						       =				BatchFeature(__SCREAMING_SNAKE_CASE)

           if return_tensors is not None:
               __a						       =				input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE)

           return input_features











import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_UpperCAmelCase		:  Dict	=   logging.get_logger(__name__)

_UpperCAmelCase		:  Optional[int]	=   "▁"

_UpperCAmelCase		:  int	=   {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

_UpperCAmelCase		:  List[Any]	=   {
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

_UpperCAmelCase		:  str	=   {
    "facebook/s2t-small-librispeech-asr": 1024,
}

_UpperCAmelCase		:  List[str]	=   ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

_UpperCAmelCase		:  Tuple	=   {"mustc": MUSTC_LANGS}




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  MAX_MODEL_INPUT_SIZES
       _a           =  ['''input_ids''', '''attention_mask''']

       _a           =  []






       def __init__(      self:    Dict				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    List[Any]="<s>"				,    _lowerCAmelCase:    List[str]="</s>"				,    _lowerCAmelCase:    Optional[int]="<pad>"				,    _lowerCAmelCase:    List[str]="<unk>"				,    _lowerCAmelCase:    Dict=False				,    _lowerCAmelCase:    Optional[Any]=False				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Optional[Dict[str, Any]] = None				,    **_lowerCAmelCase:    Tuple				,    ):
         lowercase			:str							=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase				,    eos_token=_lowerCAmelCase				,    unk_token=_lowerCAmelCase				,    pad_token=_lowerCAmelCase				,    do_upper_case=_lowerCAmelCase				,    do_lower_case=_lowerCAmelCase				,    tgt_lang=_lowerCAmelCase				,    lang_codes=_lowerCAmelCase				,    sp_model_kwargs=self.sp_model_kwargs				,    **_lowerCAmelCase				,    )
         lowercase			:List[str]							=  do_upper_case
         lowercase			:List[str]							=  do_lower_case

         lowercase			:str							=  load_json(_lowerCAmelCase )
         lowercase			:List[Any]							=  {v: k for k, v in self.encoder.items()}
         lowercase			:str							=  spm_file
         lowercase			:Any							=  load_spm(_lowerCAmelCase				,    self.sp_model_kwargs )

         if lang_codes is not None:
           lowercase			:List[str]							=  lang_codes
           lowercase			:Tuple							=  LANGUAGES[lang_codes]
           lowercase			:Any							=  [F"<lang:{lang}>" for lang in self.langs]
           lowercase			:Any							=  {lang: self.sp_model.PieceToId(F"<lang:{lang}>" ) for lang in self.langs}

           lowercase			:Optional[Any]							=  self.lang_tokens
           lowercase			:List[str]							=  tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang )
         else:
           lowercase			:Tuple							=  {}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         return len(self.encoder )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         return self._tgt_lang






       @tgt_lang.setter
       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any] ):
         lowercase			:Any							=  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    str ):
         lowercase			:Any							=  self.lang_code_to_id[tgt_lang]
         lowercase			:List[Any]							=  [lang_code_id]






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    str ):
         return self.sp_model.encode(_lowerCAmelCase				,    out_type=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    Tuple ):
         return self.encoder.get(_lowerCAmelCase				,    self.encoder[self.unk_token] )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    int ):
         return self.decoder.get(_lowerCAmelCase				,    self.unk_token )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str] ):
         lowercase			:int							=  []
         lowercase			:Any							=  ""
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             lowercase			:Dict							=  self.sp_model.decode(_lowerCAmelCase )
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             lowercase			:Any							=  []
           else:
             current_sub_tokens.append(_lowerCAmelCase )
         lowercase			:Dict							=  self.sp_model.decode(_lowerCAmelCase )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()






       def     SCREAMING_SNAKE_CASE			(      self:    Any				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    str=None ):
         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None				,    _lowerCAmelCase:    bool = False ):

         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_lowerCAmelCase				,    token_ids_a=_lowerCAmelCase				,    already_has_special_tokens=_lowerCAmelCase )

         lowercase			:int							=  [1] * len(self.prefix_tokens )
         lowercase			:List[str]							=  [1]
         if token_ids_a is None:
           return prefix_ones + ([0] * len(_lowerCAmelCase )) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCAmelCase )) + ([0] * len(_lowerCAmelCase )) + suffix_ones






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:int							=  self.encoder.copy()
         vocab.update(self.added_tokens_encoder )
         return vocab






       def __getstate__(      self:    Optional[Any] ):
         lowercase			:Union[str, Any]							=  self.__dict__.copy()
         lowercase			:Dict							=  None
         return state






       def __setstate__(      self:    Union[str, Any]				,    _lowerCAmelCase:    Dict ):
         lowercase			:List[Any]							=  d

         # for backward compatibility
         if not hasattr(self				,    "sp_model_kwargs" ):
           lowercase			:List[Any]							=  {}

         lowercase			:Union[str, Any]							=  load_spm(self.spm_file				,    self.sp_model_kwargs )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):
         lowercase			:List[str]							=  Path(_lowerCAmelCase )
         assert save_dir.is_dir(), F"{save_directory} should be a directory"
         lowercase			:List[Any]							=  save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
         )
         lowercase			:Any							=  save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
         )

         save_json(self.encoder				,    _lowerCAmelCase )

         if os.path.abspath(self.spm_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.spm_file ):
           copyfile(self.spm_file				,    _lowerCAmelCase )
         elif not os.path.isfile(self.spm_file ):
           with open(_lowerCAmelCase				,    "wb" ) as fi:
             lowercase			:int							=  self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase )

         return (str(_lowerCAmelCase ), str(_lowerCAmelCase ))







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:Tuple							=  sentencepiece.SentencePieceProcessor(**lowerCamelCase      )
  spm.Load(str(lowerCamelCase      )      )
  return spm



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  with open(lowerCamelCase,	"r"      ) as f:
    return json.load(lowerCamelCase      )







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  with open(lowerCamelCase,	"w"      ) as f:
    json.dump(lowerCamelCase,	lowerCamelCase,	indent=2      )



