



import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  ["input_values", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 1_6000,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 16,      SCREAMING_SNAKE_CASE_ = 64,      SCREAMING_SNAKE_CASE_ = "hann_window",      SCREAMING_SNAKE_CASE_ = 1.0,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 7600,      SCREAMING_SNAKE_CASE_ = 1e-10,      SCREAMING_SNAKE_CASE_ = 2,      SCREAMING_SNAKE_CASE_ = True,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_,      sampling_rate=SCREAMING_SNAKE_CASE_,      padding_value=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     return_attention_mask

          UpperCamelCase   :  Union[str, Any]						     =     num_mel_bins
          UpperCamelCase   :  int						     =     hop_length
          UpperCamelCase   :  Any						     =     win_length
          UpperCamelCase   :  Dict						     =     win_function
          UpperCamelCase   :  Any						     =     frame_signal_scale
          UpperCamelCase   :  str						     =     fmin
          UpperCamelCase   :  int						     =     fmax
          UpperCamelCase   :  Dict						     =     mel_floor
          UpperCamelCase   :  Any						     =     reduction_factor

          UpperCamelCase   :  List[str]						     =     win_length * sampling_rate // 1000
          UpperCamelCase   :  Union[str, Any]						     =     hop_length * sampling_rate // 1000
          UpperCamelCase   :  Tuple						     =     optimal_fft_length(self.sample_size	)
          UpperCamelCase   :  int						     =     (self.n_fft // 2) + 1

          UpperCamelCase   :  Any						     =     window_function(window_length=self.sample_size,      name=self.win_function,      periodic=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     mel_filter_bank(
              num_frequency_bins=self.n_freqs,      num_mel_filters=self.num_mel_bins,      min_frequency=self.fmin,      max_frequency=self.fmax,      sampling_rate=self.sampling_rate,      norm='slaney',      mel_scale='slaney',      )

          if frame_signal_scale != 1.0:
             warnings.warn(
                 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
          if reduction_factor != 2.0:
             warnings.warn(
                 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  snake_case_	(      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 0.0	)					->		List[np.ndarray]:
          if attention_mask is not None:
             UpperCamelCase   :  Dict						     =     np.array(SCREAMING_SNAKE_CASE_,      np.intaa	)
             UpperCamelCase   :  int						     =     []

             for vector, length in zip(SCREAMING_SNAKE_CASE_,      attention_mask.sum(-1	)	):
                UpperCamelCase   :  Any						     =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
                if length < normed_slice.shape[0]:
                   UpperCamelCase   :  Optional[Any]						     =     padding_value

                normed_input_values.append(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  str						     =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

          return normed_input_values
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  int						     =     spectrogram(
              SCREAMING_SNAKE_CASE_,      window=self.window,      frame_length=self.sample_size,      hop_length=self.sample_stride,      fft_length=self.n_fft,      mel_filters=self.mel_filters,      mel_floor=self.mel_floor,      log_mel='log10',      )
          return log_mel_spec.T
       def __call__(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          if audio is None and audio_target is None:
             raise ValueError('You must provide either `audio` or `audio_target` values.'	)

          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 audio 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.'	)

          if audio is not None:
             UpperCamelCase   :  Dict						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          else:
             UpperCamelCase   :  str						     =     None

          if audio_target is not None:
             UpperCamelCase   :  str						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

             if inputs is None:
                return inputs_target
             else:
                UpperCamelCase   :  Dict						     =     inputs_target['input_values']
                UpperCamelCase   :  str						     =     inputs_target.get('attention_mask'	)
                if decoder_attention_mask is not None:
                   UpperCamelCase   :  str						     =     decoder_attention_mask

          return inputs
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          UpperCamelCase   :  Any						     =     isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and len(speech.shape	) > 1
          if is_batched_numpy and len(speech.shape	) > 2:
             raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""	)
          UpperCamelCase   :  Any						     =     is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_,      (list, tuple)	) and (isinstance(speech[0],      (np.ndarray, tuple, list)	))
          )

          if is_batched:
             UpperCamelCase   :  str						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for speech in speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	):
             UpperCamelCase   :  int						     =     np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	)
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and speech.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Any						     =     speech.astype(np.floataa	)

          # always return batch
          if not is_batched:
             UpperCamelCase   :  str						     =     [speech]

          # needed to make pad() work on spectrogram inputs
          UpperCamelCase   :  Optional[Any]						     =     self.feature_size

          # convert into correct format for padding
          if is_target:
             UpperCamelCase   :  Optional[int]						     =     [self._extract_mel_features(SCREAMING_SNAKE_CASE_	) for waveform in speech]
             UpperCamelCase   :  Union[str, Any]						     =     BatchFeature({'input_values': features}	)
             UpperCamelCase   :  List[str]						     =     self.num_mel_bins
          else:
             UpperCamelCase   :  Dict						     =     BatchFeature({'input_values': speech}	)

          UpperCamelCase   :  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_,      )

          UpperCamelCase   :  int						     =     feature_size_hack

          # convert input values to correct format
          UpperCamelCase   :  Optional[int]						     =     padded_inputs['input_values']
          if not isinstance(input_values[0],      np.ndarray	):
             UpperCamelCase   :  Union[str, Any]						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for array in input_values]
          elif (
              not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)
              and isinstance(input_values[0],      np.ndarray	)
              and input_values[0].dtype is np.dtype(np.floataa	)
          ):
             UpperCamelCase   :  Optional[int]						     =     [array.astype(np.floataa	) for array in input_values]
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Dict						     =     input_values.astype(np.floataa	)

          # convert attention_mask to correct format
          UpperCamelCase   :  Dict						     =     padded_inputs.get('attention_mask'	)
          if attention_mask is not None:
             UpperCamelCase   :  int						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.intaa	) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if not is_target and self.do_normalize:
             UpperCamelCase   :  Dict						     =     (
                 attention_mask
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase   :  Tuple						     =     self.zero_mean_unit_var_norm(
                 padded_inputs['input_values'],      attention_mask=SCREAMING_SNAKE_CASE_,      padding_value=self.padding_value	)

          if return_tensors is not None:
             UpperCamelCase   :  int						     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

          return padded_inputs






       def  snake_case_	(      self	)					->		Dict[str, Any]:
          UpperCamelCase   :  Any						     =     super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          UpperCamelCase   :  Any						     =     ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
          for name in names:
             if name in output:
                del output[name]

          return output








import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	       =	logging.get_logger(__name__)
__UpperCAmelCase	       =	{'''vocab_file''': '''spiece.model'''}

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

__UpperCAmelCase	       =	{
    '''AI-Sweden/gpt-sw3-126m''': 2_048,
    '''AI-Sweden/gpt-sw3-350m''': 2_048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_048,
    '''AI-Sweden/gpt-sw3-20b''': 2_048,
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Union[str, Any]       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : Any       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : int       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : Optional[int]       					=  ["input_ids", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		None:
          UpperCamelCase   :  Optional[Any]						     =     {} if sp_model_kwargs is None else sp_model_kwargs

          UpperCamelCase   :  Dict						     =     kwargs.get('name_or_path'	)
          if name_or_path is None:
             logger.warning(
                 'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                 ' you are testing the model, this can safely be ignored'	)
             UpperCamelCase   :  Tuple						     =     'None'

          # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
          UpperCamelCase   :  str						     =     '<|endoftext|>' if eos_token is None else eos_token
          UpperCamelCase   :  Tuple						     =     '<unk>' if unk_token is None else unk_token
          if "gpt-sw3-7b" in name_or_path:
             UpperCamelCase   :  str						     =     unk_token if pad_token is None else pad_token
             UpperCamelCase   :  List[str]						     =     eos_token if bos_token is None else bos_token
          else:
             UpperCamelCase   :  List[Any]						     =     '<pad>' if pad_token is None else pad_token
             UpperCamelCase   :  Dict						     =     '<s>' if bos_token is None else bos_token

          super().__init__(
              do_lower_case=SCREAMING_SNAKE_CASE_,      remove_space=SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      sp_model_kwargs=self.sp_model_kwargs,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  List[str]						     =     do_lower_case
          UpperCamelCase   :  List[str]						     =     remove_space
          UpperCamelCase   :  List[Any]						     =     keep_accents
          UpperCamelCase   :  List[str]						     =     vocab_file

          UpperCamelCase   :  Union[str, Any]						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(SCREAMING_SNAKE_CASE_	)

          # Used for whitespace normalization in input texts
          # fmt : off
          UpperCamelCase   :  Dict						     =     {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
          # fmt : on

          # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
          UpperCamelCase   :  List[Any]						     =     re.compile(
              F"""[{"".join(map(SCREAMING_SNAKE_CASE_,      list(range(0,      9	)	) + list(range(11,      32	)	) + list(range(127,      160	)	) + [160, 173, 8203]	)	)}]"""	)
       def __getstate__(      self	)					->		Tuple:
          UpperCamelCase   :  List[Any]						     =     self.__dict__.copy()
          UpperCamelCase   :  Optional[int]						     =     None
          return state
       def __setstate__(      self,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  Any						     =     d

          # for backward compatibility
          if not hasattr(self,      'sp_model_kwargs'	):
             UpperCamelCase   :  Optional[int]						     =     {}

          UpperCamelCase   :  int						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(self.vocab_file	)
       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def  snake_case_	(      self	)					->		int:
          return len(self.sp_model	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Dict						     =     self.non_printing_characters_re.sub('',      SCREAMING_SNAKE_CASE_	)

          # Normalize whitespaces
          UpperCamelCase   :  Any						     =     ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

          # NFC Unicode normalization
          UpperCamelCase   :  Dict						     =     unicodedata.normalize('NFC',      SCREAMING_SNAKE_CASE_	)
          return text
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  Any						     =     self.preprocess_text(SCREAMING_SNAKE_CASE_	)
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,      out_type=SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		int:
          return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
       @staticmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		str:
          return out_string
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Optional[Any]						     =     []
          UpperCamelCase   :  List[Any]						     =     ''
          UpperCamelCase   :  str						     =     False
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                if not prev_is_special:
                   out_string += " "

                out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                UpperCamelCase   :  Dict						     =     True
                UpperCamelCase   :  Optional[Any]						     =     []
             else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
                UpperCamelCase   :  str						     =     False
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)

          return out_string
       def  snake_case_	(      self	)					->		Dict[str, int]:
          UpperCamelCase   :  Tuple						     =     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
             return
          UpperCamelCase   :  List[str]						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

          if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file,      SCREAMING_SNAKE_CASE_	)
          elif not os.path.isfile(self.vocab_file	):
             with open(SCREAMING_SNAKE_CASE_,      'wb'	) as fi:
                UpperCamelCase   :  Any						     =     self.sp_model.serialized_model_proto()
                fi.write(SCREAMING_SNAKE_CASE_	)

          return (out_vocab_file,)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False	)					->		Union[List[int], List[List[int]], "torch.Tensor"]:

          if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  List[str]						     =     self.preprocess_text(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Optional[Any]						     =     self.sp_model.encode(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  Union[str, Any]						     =     [self.preprocess_text(SCREAMING_SNAKE_CASE_	) for t in text]
             UpperCamelCase   :  Any						     =     self.sp_model.encode(SCREAMING_SNAKE_CASE_	)

          if return_tensors is True or return_tensors == "pt":
             UpperCamelCase   :  List[Any]						     =     torch.tensor(SCREAMING_SNAKE_CASE_	)

          return token_ids
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return self.sp_model.decode(SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[int]:
          UpperCamelCase   :  List[Any]						     =     [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
          UpperCamelCase   :  Optional[Any]						     =     (
              F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(SCREAMING_SNAKE_CASE_	) + F"""{self.bos_token}Bot:"""
          )
          return self.encode(text=SCREAMING_SNAKE_CASE_	)



