




'''simple docstring'''



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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCamelCase_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase_	 =		{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase_	 =		{
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

lowerCamelCase_	 =		{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		VOCAB_FILES_NAMES
   lowerCamelCase_       						=		PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_       						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_       						=		['''input_ids''', '''attention_mask''', '''token_type_ids''']
   lowerCamelCase_       						=		DebertaTokenizer



   def __init__(					self      :			Optional[int]						,   __lowerCamelCase      :			Any=None						,   __lowerCamelCase      :			str=None						,   __lowerCamelCase      :			Tuple=None						,   __lowerCamelCase      :			List[str]="replace"						,   __lowerCamelCase      :			Optional[Any]="[CLS]"						,   __lowerCamelCase      :			Dict="[SEP]"						,   __lowerCamelCase      :			Dict="[SEP]"						,   __lowerCamelCase      :			Optional[int]="[CLS]"						,   __lowerCamelCase      :			str="[UNK]"						,   __lowerCamelCase      :			List[str]="[PAD]"						,   __lowerCamelCase      :			Union[str, Any]="[MASK]"						,   __lowerCamelCase      :			Any=False						,   **__lowerCamelCase      :			List[Any]						,   ):

      """simple docstring"""
      super().__init__(
          __lowerCamelCase						,   __lowerCamelCase						,   tokenizer_file=__lowerCamelCase						,   errors=__lowerCamelCase						,   bos_token=__lowerCamelCase						,   eos_token=__lowerCamelCase						,   unk_token=__lowerCamelCase						,   sep_token=__lowerCamelCase						,   cls_token=__lowerCamelCase						,   pad_token=__lowerCamelCase						,   mask_token=__lowerCamelCase						,   add_prefix_space=__lowerCamelCase						,   **__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   kwargs.pop("add_bos_token"						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
      if pre_tok_state.get("add_prefix_space"						,   __lowerCamelCase					) != add_prefix_space:
         _SCREAMING_SNAKE_CASE =   getattr(__lowerCamelCase						,   pre_tok_state.pop("type"					)					)
         _SCREAMING_SNAKE_CASE =   add_prefix_space
         _SCREAMING_SNAKE_CASE =   pre_tok_class(**__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   add_prefix_space



   @property
   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """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 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   AddedToken(__lowerCamelCase						,   lstrip=__lowerCamelCase						,   rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase						,   __lowerCamelCase					) else value
      _SCREAMING_SNAKE_CASE =   value



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			List[int]						,   __lowerCamelCase      :			Optional[List[int]] = None					):

      """simple docstring"""
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _SCREAMING_SNAKE_CASE =   [self.cls_token_id]
      _SCREAMING_SNAKE_CASE =   [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			List[int]						,   __lowerCamelCase      :			Optional[List[int]] = None					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   [self.sep_token_id]
      _SCREAMING_SNAKE_CASE =   [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					) * [0] + len(token_ids_a + sep					) * [1]



   def 							lowerCAmelCase_    (					self      :			Tuple						,   *__lowerCamelCase      :			Tuple						,   **__lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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      :			str						,   *__lowerCamelCase      :			List[Any]						,   **__lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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      :			Union[str, Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Optional[str] = None					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self._tokenizer.model.save(__lowerCamelCase						,   name=__lowerCamelCase					)
      return tuple(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			"Conversation"					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   []
      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:
         _SCREAMING_SNAKE_CASE =   input_ids[-self.model_max_length :]
      return input_ids







'''simple docstring'''



lowerCamelCase_	 =		[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      self.inbound.append(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      self.outbound.append(__lowerCamelCase					)



   def __repr__(					self      :			List[str]					):

      """simple docstring"""
      return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]=3 ,  __A       :				Optional[int]=0.8_5						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   1

   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   len(node.outbound						)

   for i in range(__A						):
      print(f"""======= Iteration {i + 1} ======="""						)
      for _, node in enumerate(__A						):
         _SCREAMING_SNAKE_CASE =   (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound						)
      print(__A						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   list(input("Enter Names of the Nodes: "						).split()						)

   _SCREAMING_SNAKE_CASE =   [Node(__A						) for name in names]

   for ri, row in enumerate(__A						):
      for ci, col in enumerate(__A						):
         if col == 1:
            nodes[ci].add_inbound(names[ri]						)
            nodes[ri].add_outbound(names[ci]						)

   print("======= Nodes ======="						)
   for node in nodes:
      print(__A						)

   page_rank(__A						)


if __name__ == "__main__":
 main()

