

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {'tokenizer_file': 'tokenizer.json'}

lowercase_             =     {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						VOCAB_FILES_NAMES
  _a		    =						PRETRAINED_VOCAB_FILES_MAP
  _a		    =						["""input_ids""", """attention_mask"""]
  _a		    =						None




  def __init__(       self							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     lowerCAmelCase="<unk>"							,     lowerCAmelCase="<s>"							,     lowerCAmelCase="</s>"							,     lowerCAmelCase="<pad>"							,     lowerCAmelCase=False							,     lowerCAmelCase=False							,     **lowerCAmelCase							,     )						->				Dict:
        '''simple docstring'''






        super().__init__(
            lowerCAmelCase							,     lowerCAmelCase							,     tokenizer_file=lowerCAmelCase							,     unk_token=lowerCAmelCase							,     bos_token=lowerCAmelCase							,     eos_token=lowerCAmelCase							,     pad_token=lowerCAmelCase							,     add_prefix_space=lowerCAmelCase							,     clean_up_tokenization_spaces=lowerCAmelCase							,     **lowerCAmelCase							,     )
        _lowercase			 =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
        if pre_tok_state.get('add_prefix_space'							,     lowerCAmelCase      ) != add_prefix_space:
              _lowercase			 =getattr(lowerCAmelCase							,     pre_tok_state.pop('type'      )      )
              _lowercase			 =add_prefix_space
              _lowercase			 =pre_tok_class(**lowerCAmelCase      )

        _lowercase			 =add_prefix_space




  def 						A__  (       self							,     *lowerCAmelCase							,     **lowerCAmelCase      )						->				BatchEncoding:
        '''simple docstring'''






        _lowercase			 =kwargs.get('is_split_into_words'							,     lowerCAmelCase      )
        if not (self.add_prefix_space or not is_split_into_words):
              raise Exception(
                  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 						A__  (       self							,     *lowerCAmelCase							,     **lowerCAmelCase      )						->				BatchEncoding:
        '''simple docstring'''






        _lowercase			 =kwargs.get('is_split_into_words'							,     lowerCAmelCase      )

        if not (self.add_prefix_space or not is_split_into_words):
              raise Exception(
                  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 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None      )						->				Tuple[str]:
        '''simple docstring'''






        _lowercase			 =self._tokenizer.model.save(lowerCAmelCase							,     name=lowerCAmelCase      )
        return tuple(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				List[int]:
        '''simple docstring'''






        _lowercase			 =[]
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowerCAmelCase							,     add_special_tokens=lowerCAmelCase      ) + [self.eos_token_id]      )

        if len(lowerCAmelCase      ) > self.model_max_length:
              _lowercase			 =input_ids[-self.model_max_length :]
        return input_ids





import math

import tensorflow as tf
from packaging import version
def   a   (    A__			:     Tuple  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )   ,     x.dtype  )  ))

      return x * cdf
def   a   (    A__			:     Any  )					->   int:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(math.pi   ,     x.dtype  )
      _lowercase			 =tf.cast(0.044715   ,     x.dtype  )
      _lowercase			 =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(A__   ,     3  ))  ))

      return x * cdf
def   a   (    A__			:     Optional[int]  )					->   int:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )

      return x * tf.tanh(tf.math.softplus(A__  )  )
def   a   (    A__			:     Tuple  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(0.044715   ,     x.dtype  )
      _lowercase			 =tf.cast(0.7978845608   ,     x.dtype  )

      return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))
def   a   (    A__			:     int  )					->   Any:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(1.702   ,     x.dtype  )
      return x * tf.math.sigmoid(coeff * x  )
def   a   (    A__			:     int  )					->   Dict:





      """simple docstring"""



      return tf.clip_by_value(_gelu(A__  )   ,     -10   ,     10  )
def   a   (    A__			:     Optional[int]   ,     A__			:     int=-1  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase , _lowercase			 =tf.split(A__   ,     2   ,     axis=A__  )
      return a * tf.math.sigmoid(A__  )


if version.parse(tf.version.VERSION) >= version.parse('2.4'):
  def   a   (    A__			:     Any  )					->   Dict:





        """simple docstring"""



        return tf.keras.activations.gelu(A__   ,     approximate=A__  )

  lowercase_             =     tf.keras.activations.gelu







  lowercase_             =     approximate_gelu_wrap
else:
  lowercase_             =     _gelu
  lowercase_             =     _gelu_new


lowercase_             =     {
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def   a   (    A__			:     Optional[int]  )					->   str:





      """simple docstring"""



      if activation_string in ACTaFN:
            return ACTaFN[activation_string]
      else:
            raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}'''  )


