

import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


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


_lowerCamelCase      :					List[str]					    =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase      :					str					    =      {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

_lowerCamelCase      :					Optional[int]					    =      {
    '''allenai/led-base-16384''': 1_6384,
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    LEDTokenizer
       lowercase_						=    ["""input_ids""", """attention_mask"""]

       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    =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
             if pre_tok_state.get('''add_prefix_space''' ,   lowercase__     ) != add_prefix_space:
                   __A    =getattr(lowercase__ ,   pre_tok_state.pop('''type'''     )     )
                   __A    =add_prefix_space
                   __A    =pre_tok_class(**lowercase__     )

             __A    =add_prefix_space

             # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
             __A    ='''post_processor'''
             __A    =getattr(self.backend_tokenizer ,   lowercase__ ,   lowercase__     )
             if tokenizer_component_instance:
                   __A    =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    =tuple(state['''sep''']     )
                   if "cls" in state:
                         __A    =tuple(state['''cls''']     )

                   __A    =False

                   if state.get('''add_prefix_space''' ,   lowercase__     ) != add_prefix_space:
                         __A    =add_prefix_space
                         __A    =True

                   if state.get('''trim_offsets''' ,   lowercase__     ) != trim_offsets:
                         __A    =trim_offsets
                         __A    =True

                   if changes_to_apply:
                         __A    =getattr(lowercase__ ,   state.pop('''type'''     )     )
                         __A    =component_class(**lowercase__     )
                         setattr(self.backend_tokenizer ,   lowercase__ ,   lowercase__     )

       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def     __UpperCamelCase     (							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     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else value
             __A    =value

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =kwargs.get('''is_split_into_words''' ,   lowercase__     )

             if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       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     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =kwargs.get('''is_split_into_words''' ,   lowercase__     )

             if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =self._tokenizer.model.save(lowercase__ ,   name=lowercase__     )
             return tuple(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=None     ):







             '''simple docstring'''



             __A    =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = PaddingStrategy.DO_NOT_PAD ,   lowercase__ = None ,   lowercase__ = None ,   ):







             '''simple docstring'''



             __A    =super()._pad(
                 encoded_inputs=lowercase__ ,   max_length=lowercase__ ,   padding_strategy=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_attention_mask=lowercase__ ,   )

             # Load from model defaults
             if return_attention_mask is None:
                   __A    ='''attention_mask''' in self.model_input_names

             if return_attention_mask and "global_attention_mask" in encoded_inputs:
                   __A    =encoded_inputs[self.model_input_names[0]]
                   # `global_attention_mask` need to have the same length as other (sequential) inputs.
                   __A    =len(encoded_inputs['''global_attention_mask''']     ) != len(lowercase__     )

                   if needs_to_be_padded:
                         __A    =len(lowercase__     ) - len(encoded_inputs['''global_attention_mask''']     )

                         if self.padding_side == "right":
                               # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                               __A    =(
                                   encoded_inputs['''global_attention_mask'''] + [-1] * difference
                               )
                         elif self.padding_side == "left":
                               __A    =[-1] * difference + encoded_inputs[
                                   '''global_attention_mask'''
                               ]
                         else:
                               raise ValueError('''Invalid padding strategy:''' + str(self.padding_side     )     )

             return encoded_inputs





from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


