'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''input_features''', '''attention_mask''']







     def __init__(		self     ,			_lowerCamelCase=8_0     ,			_lowerCamelCase=1_6_0_0_0     ,			_lowerCamelCase=8_0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(feature_size=_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			padding_value=_lowerCamelCase     ,			**_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  num_mel_bins
         __UpperCamelCase	:	str		    =  do_ceptral_normalize
         __UpperCamelCase	:	Optional[Any]		    =  normalize_means
         __UpperCamelCase	:	Union[str, Any]		    =  normalize_vars
         __UpperCamelCase	:	Union[str, Any]		    =  True







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			):
         __UpperCamelCase	:	Dict		    =  waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
         __UpperCamelCase	:	List[Any]		    =  torch.from_numpy(_lowerCamelCase    ).unsqueeze(0    )
         __UpperCamelCase	:	str		    =  ta_kaldi.fbank(_lowerCamelCase     ,			num_mel_bins=self.num_mel_bins     ,			sample_frequency=self.sampling_rate    )
         return features.numpy()







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = True     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 0.0     ,			):
         # make sure we normalize float32 arrays
         if normalize_means:
             __UpperCamelCase	:	Union[str, Any]		    =  x[:input_length].mean(axis=0    )
             __UpperCamelCase	:	int		    =  np.subtract(_lowerCamelCase     ,			_lowerCamelCase    )
         if normalize_vars:
             __UpperCamelCase	:	List[str]		    =  x[:input_length].std(axis=0    )
             __UpperCamelCase	:	Optional[Any]		    =  np.divide(_lowerCamelCase     ,			_lowerCamelCase    )

         if input_length < x.shape[0]:
             __UpperCamelCase	:	Optional[int]		    =  padding_value

         # make sure array is in float32
         __UpperCamelCase	:	List[str]		    =  x.astype(np.floataa    )

         return x







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [
             self.utterance_cmvn(_lowerCamelCase     ,			_lowerCamelCase     ,			self.normalize_means     ,			self.normalize_vars     ,			self.padding_value    )
             for x, n in zip(_lowerCamelCase     ,			_lowerCamelCase    )
         ]







     def __call__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):

         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.'    )

         __UpperCamelCase	:	Any		    =  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	:	Union[str, Any]		    =  is_batched_numpy or (
             isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(raw_speech[0]     ,			(np.ndarray, tuple, list)    ))
         )

         if is_batched:
             __UpperCamelCase	:	Tuple		    =  [np.asarray(_lowerCamelCase     ,			dtype=np.floataa    ) for speech in raw_speech]
         elif not is_batched and not isinstance(_lowerCamelCase     ,			np.ndarray    ):
             __UpperCamelCase	:	List[str]		    =  np.asarray(_lowerCamelCase     ,			dtype=np.floataa    )
         elif isinstance(_lowerCamelCase     ,			np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
             __UpperCamelCase	:	Optional[int]		    =  raw_speech.astype(np.floataa    )

         # always return batch
         if not is_batched:
             __UpperCamelCase	:	Any		    =  [raw_speech]

         # extract fbank features
         __UpperCamelCase	:	Any		    =  [self._extract_fbank_features(_lowerCamelCase    ) for waveform in raw_speech]

         # convert into correct format for padding
         __UpperCamelCase	:	Optional[Any]		    =  BatchFeature({'input_features': features}    )

         __UpperCamelCase	:	int		    =  self.pad(
             _lowerCamelCase     ,			padding=_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			truncation=_lowerCamelCase     ,			pad_to_multiple_of=_lowerCamelCase     ,			return_attention_mask=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         # make sure list is in array format
         __UpperCamelCase	:	Union[str, Any]		    =  padded_inputs.get('input_features'    )
         if isinstance(input_features[0]     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	List[Any]		    =  [np.asarray(_lowerCamelCase     ,			dtype=np.floataa    ) for feature in input_features]

         __UpperCamelCase	:	str		    =  padded_inputs.get('attention_mask'    )
         if attention_mask is not None:
             __UpperCamelCase	:	int		    =  [np.asarray(_lowerCamelCase     ,			dtype=np.intaa    ) for array in attention_mask]

         # Utterance-level cepstral mean and variance normalization
         if self.do_ceptral_normalize:
             __UpperCamelCase	:	Dict		    =  (
                 np.array(_lowerCamelCase     ,			dtype=np.intaa    )
                 if self._get_padding_strategies(_lowerCamelCase     ,			max_length=_lowerCamelCase    ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             __UpperCamelCase	:	List[Any]		    =  self.normalize(
                 padded_inputs['input_features']     ,			attention_mask=_lowerCamelCase    )

         if return_tensors is not None:
             __UpperCamelCase	:	List[str]		    =  padded_inputs.convert_to_tensors(_lowerCamelCase    )

         return padded_inputs




'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


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

a=  {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







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

         __UpperCamelCase	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  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:
                 __UpperCamelCase	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

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







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[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 + sep + token_ids_a + sep    ) * [0]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         for is_user, text in conversation.iter_texts():
             if is_user:
                 # We need to space prefix as it's being done within blenderbot
                 inputs.append(' ' + text    )
             else:
                 # Generated responses should contain them already.
                 inputs.append(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  input_ids[-self.model_max_length :]
             logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
         return input_ids



