



"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__snake_case				:  List[str] =       logging.get_logger(__name__)



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['input_features', 'attention_mask']

	def __init__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[Any]=80			,     _SCREAMING_SNAKE_CASE:						str=1_6000			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Dict=10			,     _SCREAMING_SNAKE_CASE:						Dict=25			,     _SCREAMING_SNAKE_CASE:						str="hamming_window"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=3_2768.0			,     _SCREAMING_SNAKE_CASE:						Any=0.97			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=1.0			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						List[str]=True			,     _SCREAMING_SNAKE_CASE:						List[str]=False			,     **_SCREAMING_SNAKE_CASE:						int			,     )							->			Optional[int]:

				"""simple docstring"""

				super().__init__(feature_size=_SCREAMING_SNAKE_CASE			,     sampling_rate=_SCREAMING_SNAKE_CASE			,     padding_value=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			feature_size
				__lowerCAmelCase :    Dict				=			sampling_rate
				__lowerCAmelCase :    Dict				=			padding_value
				__lowerCAmelCase :    Union[str, Any]				=			hop_length
				__lowerCAmelCase :    List[Any]				=			win_length
				__lowerCAmelCase :    List[str]				=			frame_signal_scale
				__lowerCAmelCase :    List[str]				=			preemphasis_coeff
				__lowerCAmelCase :    List[str]				=			mel_floor
				__lowerCAmelCase :    Tuple				=			normalize_means
				__lowerCAmelCase :    str				=			normalize_vars
				__lowerCAmelCase :    Optional[Any]				=			win_function
				__lowerCAmelCase :    Optional[int]				=			return_attention_mask

				__lowerCAmelCase :    Optional[int]				=			win_length * sampling_rate // 1000
				__lowerCAmelCase :    List[Any]				=			hop_length * sampling_rate // 1000

				__lowerCAmelCase :    Any				=			optimal_fft_length(self.sample_size)
				__lowerCAmelCase :    Tuple				=			(self.n_fft // 2) + 1

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						np.array)							->			np.ndarray:

				"""simple docstring"""

				if self.win_function == "hamming_window":
							__lowerCAmelCase :    Optional[int]				=			window_function(window_length=self.sample_size			,     name=self.win_function			,     periodic=_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase :    List[str]				=			window_function(window_length=self.sample_size			,     name=self.win_function)

				__lowerCAmelCase :    Union[str, Any]				=			mel_filter_bank(
				    num_frequency_bins=self.n_freqs			,     num_mel_filters=self.feature_size			,     min_frequency=0.0			,     max_frequency=self.sampling_rate / 2.0			,     sampling_rate=self.sampling_rate			,     )

				__lowerCAmelCase :    int				=			spectrogram(
				    one_waveform * self.frame_signal_scale			,     window=_SCREAMING_SNAKE_CASE			,     frame_length=self.sample_size			,     hop_length=self.sample_stride			,     fft_length=self.n_fft			,     center=_SCREAMING_SNAKE_CASE			,     preemphasis=self.preemphasis_coeff			,     mel_filters=_SCREAMING_SNAKE_CASE			,     mel_floor=self.mel_floor			,     log_mel="log"			,     )
				return msfc_features.T

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Any)							->			List[str]:

				"""simple docstring"""

				if self.normalize_means:
							__lowerCAmelCase :    Optional[Any]				=			x[:input_length].mean(axis=0)
							__lowerCAmelCase :    Any				=			np.subtract(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				if self.normalize_vars:
							__lowerCAmelCase :    Optional[int]				=			x[:input_length].std(axis=0)
							__lowerCAmelCase :    List[Any]				=			np.divide(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				if input_length < x.shape[0]:
							__lowerCAmelCase :    Tuple				=			padding_value

				# make sure array is in float32
				__lowerCAmelCase :    Dict				=			x.astype(np.floataa)

				return x

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[np.ndarray]			,     _SCREAMING_SNAKE_CASE:						Optional[np.ndarray] = None)							->			List[np.ndarray]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [self._normalize_one(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     self.padding_value) for x, n in zip(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)]







	def __call__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,     _SCREAMING_SNAKE_CASE:						Union[bool, str, PaddingStrategy] = False			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						bool = False			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, TensorType]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     **_SCREAMING_SNAKE_CASE:						Dict			,     )							->			BatchFeature:

				"""simple docstring"""

				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
										    F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
										    F""" {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.")

				__lowerCAmelCase :    Tuple				=			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}""")
				__lowerCAmelCase :    Tuple				=			is_batched_numpy or (
				    isinstance(_SCREAMING_SNAKE_CASE			,     (list, tuple)) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)))
				)

				if is_batched:
							__lowerCAmelCase :    Any				=			[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):
							__lowerCAmelCase :    str				=			np.asarray(_SCREAMING_SNAKE_CASE			,     dtype=np.floataa)
				elif isinstance(_SCREAMING_SNAKE_CASE			,     np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
							__lowerCAmelCase :    List[Any]				=			raw_speech.astype(np.floataa)

				# always return batch
				if not is_batched:
							__lowerCAmelCase :    List[str]				=			[raw_speech]

				# extract fbank features
				__lowerCAmelCase :    List[str]				=			[self._extract_mfsc_features(_SCREAMING_SNAKE_CASE) for one_waveform in raw_speech]

				# convert into correct format for padding
				__lowerCAmelCase :    Any				=			BatchFeature({"input_features": features})

				__lowerCAmelCase :    Tuple				=			self.pad(
				    _SCREAMING_SNAKE_CASE			,     padding=_SCREAMING_SNAKE_CASE			,     max_length=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE			,     return_attention_mask=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )
				# make sure list is in array format
				__lowerCAmelCase :    int				=			padded_inputs.get("input_features")
				if isinstance(input_features[0]			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[int]				=			[np.asarray(_SCREAMING_SNAKE_CASE			,     dtype=np.floataa) for feature in input_features]

				__lowerCAmelCase :    int				=			padded_inputs.get("attention_mask")
				if attention_mask is not None:
							__lowerCAmelCase :    Union[str, Any]				=			[np.asarray(_SCREAMING_SNAKE_CASE			,     dtype=np.intaa) for array in attention_mask]

				if self.normalize_means or self.normalize_vars:
							__lowerCAmelCase :    Optional[int]				=			(
							    np.array(_SCREAMING_SNAKE_CASE			,     dtype=np.intaa)
							    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE			,     max_length=_SCREAMING_SNAKE_CASE) is not PaddingStrategy.DO_NOT_PAD
							    and padding
							    else None
							)
							__lowerCAmelCase :    Any				=			self.normalize(
							    padded_inputs["input_features"]			,     attention_mask=_SCREAMING_SNAKE_CASE)

				if return_tensors is not None:
							__lowerCAmelCase :    int				=			padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE)

				return padded_inputs







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


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

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [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  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [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]