






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:     Dict   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''input_features''', '''is_longer''']


  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			int=64       ,				lowerCamelCase		:			int=4_8000       ,				lowerCamelCase		:			List[str]=480       ,				lowerCamelCase		:			int=10       ,				lowerCamelCase		:			Optional[int]=1024       ,				lowerCamelCase		:			List[Any]=0.0       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			float = 0       ,				lowerCamelCase		:			float = 1_4000       ,				lowerCamelCase		:			int = None       ,				lowerCamelCase		:			str = "fusion"       ,				lowerCamelCase		:			str = "repeatpad"       ,				**lowerCamelCase		:			Union[str, Any]       ,				)						->	Tuple:


         """simple docstring"""
         super().__init__(
             feature_size=lowerCamelCase       ,				sampling_rate=lowerCamelCase       ,				padding_value=lowerCamelCase       ,				return_attention_mask=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    top_db
         _UpperCAmelCase							=    truncation
         _UpperCAmelCase							=    padding
         _UpperCAmelCase							=    fft_window_size
         _UpperCAmelCase							=    (fft_window_size >> 1) + 1
         _UpperCAmelCase							=    hop_length
         _UpperCAmelCase							=    max_length_s
         _UpperCAmelCase							=    max_length_s * sampling_rate
         _UpperCAmelCase							=    sampling_rate
         _UpperCAmelCase							=    frequency_min
         _UpperCAmelCase							=    frequency_max
         _UpperCAmelCase							=    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							=    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  lowerCamelCase       (      self		:			List[Any]							)						->	Dict[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    copy.deepcopy(self.__dict__							)
         _UpperCAmelCase							=    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		:			Tuple       ,				lowerCamelCase		:			np.array       ,				lowerCamelCase		:			Optional[np.array] = None							)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]							)						->	Any:


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

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

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


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			np.array       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]							)						->	np.array:


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

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

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

         return input_mel, longer





  def __call__(      self		:			int       ,				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		:			int       ,				)						->	BatchFeature:


         """simple docstring"""
         _UpperCAmelCase							=    truncation if truncation is not None else self.truncation
         _UpperCAmelCase							=    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							=    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							=    is_batched_numpy or (
             isinstance(lowerCamelCase       ,				(list, tuple)							) and (isinstance(raw_speech[0]       ,				(np.ndarray, tuple, list)							))
         )

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

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

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

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

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

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

         _UpperCAmelCase							=    {"""input_features""": input_mel, """is_longer""": is_longer}
         _UpperCAmelCase							=    BatchFeature(lowerCamelCase							)

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

         return input_features





'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase_		:     Union[str, Any]          =		logging.get_logger(__name__)


UpperCamelCase_		:     Optional[Any]          =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase_		:     Dict          =		{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase_		:     Tuple          =		{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_				:       int       							=		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_				:       Any       							=		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_				:       str       							=		BartTokenizer






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE="replace"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="<unk>"   ,_SCREAMING_SNAKE_CASE="<pad>"   ,_SCREAMING_SNAKE_CASE="<mask>"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,tokenizer_file=_SCREAMING_SNAKE_CASE   ,errors=_SCREAMING_SNAKE_CASE   ,bos_token=_SCREAMING_SNAKE_CASE   ,eos_token=_SCREAMING_SNAKE_CASE   ,sep_token=_SCREAMING_SNAKE_CASE   ,cls_token=_SCREAMING_SNAKE_CASE   ,unk_token=_SCREAMING_SNAKE_CASE   ,pad_token=_SCREAMING_SNAKE_CASE   ,mask_token=_SCREAMING_SNAKE_CASE   ,add_prefix_space=_SCREAMING_SNAKE_CASE   ,trim_offsets=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
          if pre_tok_state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
               _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,pre_tok_state.pop("type"       )       )
               _snake_case				= add_prefix_space
               _snake_case				= pre_tok_class(**_SCREAMING_SNAKE_CASE       )

          _snake_case				= add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _snake_case				= "post_processor"
          _snake_case				= getattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          if tokenizer_component_instance:
               _snake_case				= json.loads(tokenizer_component_instance.__getstate__()       )

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    _snake_case				= tuple(state["sep"]       )
               if "cls" in state:
                    _snake_case				= tuple(state["cls"]       )

               _snake_case				= False

               if state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
                    _snake_case				= add_prefix_space
                    _snake_case				= True

               if state.get("trim_offsets"   ,_SCREAMING_SNAKE_CASE       ) != trim_offsets:
                    _snake_case				= trim_offsets
                    _snake_case				= True

               if changes_to_apply:
                    _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,state.pop("type"       )       )
                    _snake_case				= component_class(**_SCREAMING_SNAKE_CASE       )
                    setattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     @property
     def 							_lowercase      (					self       )				->     str:
          if self._mask_token is None:
               if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."       )
               return None
          return str(self._mask_token       )






     @mask_token.setter
     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= AddedToken(_SCREAMING_SNAKE_CASE   ,lstrip=_SCREAMING_SNAKE_CASE   ,rstrip=_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) else value
          _snake_case				= value






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[str]:
          _snake_case				= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,name=_SCREAMING_SNAKE_CASE       )
          return tuple(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Tuple:
          _snake_case				= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     List[int]:
          _snake_case				= [self.sep_token_id]
          _snake_case				= [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 + sep + token_ids_a + sep       ) * [0]


