


'''simple docstring'''
from __future__ import annotations


def       _lowercase							(					UpperCamelCase__							:       list[int],					UpperCamelCase__							:       int      ):
 if len(UpperCamelCase__      ) < k or k < 0:
  raise ValueError('Invalid Input'      )
 __A	:		Optional[int]	  =      sum(array[:k]      )
 for i in range(len(UpperCamelCase__      ) - k      ):
  __A	:		Tuple	  =      current_sum - array[i] + array[i + k]
  __A	:		List[str]	  =      max(UpperCamelCase__,					UpperCamelCase__      )
 return max_sum


if __name__ == "__main__":
 from doctest import testmod
 from random import randint

 testmod()
 UpperCAmelCase_			:  Tuple										=   [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0_0)]
 UpperCAmelCase_			:  Union[str, Any]										=   randint(0, 1_1_0)
 print(f'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')










'''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

UpperCAmelCase_			:  List[Any]										=   logging.get_logger(__name__)


UpperCAmelCase_			:  Union[str, Any]										=   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_			:  List[Any]										=   {
    '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'
    },
}

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

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    VOCAB_FILES_NAMES
 __lowercase					:		List[Any]     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		Optional[Any]     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Tuple     				=    ['''input_ids''', '''attention_mask''']
 __lowercase					:		str     				=    BlenderbotTokenizer


 def __init__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase="replace"   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="</s>"   ,   __lowercase="<s>"   ,   __lowercase="<unk>"   ,   __lowercase="<pad>"   ,   __lowercase="<mask>"   ,   __lowercase=False   ,   __lowercase=True   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   __lowercase   ,   tokenizer_file=__lowercase   ,   errors=__lowercase   ,   bos_token=__lowercase   ,   eos_token=__lowercase   ,   sep_token=__lowercase   ,   cls_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   mask_token=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase   ,   **__lowercase   ,   )

  __A	:		str	  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
   __A	:		List[Any]	  =      getattr(__lowercase   ,   pre_tok_state.pop('type'    )    )
   __A	:		Any	  =      add_prefix_space
   __A	:		Any	  =      pre_tok_class(**__lowercase    )

  __A	:		Union[str, Any]	  =      add_prefix_space

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

   __A	:		Any	  =      False

   if state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
    __A	:		Any	  =      add_prefix_space
    __A	:		int	  =      True

   if state.get('trim_offsets'   ,   __lowercase    ) != trim_offsets:
    __A	:		List[Any]	  =      trim_offsets
    __A	:		List[str]	  =      True

   if changes_to_apply:
    __A	:		Optional[int]	  =      getattr(__lowercase   ,   state.pop('type'    )    )
    __A	:		Optional[int]	  =      component_class(**__lowercase    )
    setattr(self.backend_tokenizer   ,   __lowercase   ,   __lowercase    )


 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 			snake_case__ (   self    ):

  """simple docstring"""







  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 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      AddedToken(__lowercase   ,   lstrip=__lowercase   ,   rstrip=__lowercase    ) if isinstance(__lowercase   ,   __lowercase    ) else value
  __A	:		List[str]	  =      value


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )
  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(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )

  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(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		str	  =      self._tokenizer.model.save(__lowercase   ,   name=__lowercase    )
  return tuple(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		str	  =      [self.sep_token_id]
  __A	:		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 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  return token_ids_a + [self.eos_token_id]







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		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(__lowercase    )

  __A	:		Tuple	  =      '  '.join(__lowercase    )
  __A	:		List[Any]	  =      self.encode(__lowercase    )
  if len(__lowercase    ) > self.model_max_length:
   __A	:		Any	  =      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






