




'''simple docstring'''

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							:     Any		     =logging.get_logger(__name__)


__lowerCAmelCase							:     Optional[Any]		     ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase							:     List[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							:     Tuple		     ={
    "allenai/led-base-16384": 1_6384,
}






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    VOCAB_FILES_NAMES
 __lowercase       		=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase       		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase       		=    LEDTokenizer
 __lowercase       		=    ["""input_ids""", """attention_mask"""]
 def __init__(    self				:Union[str, Any]						,	lowercase_				:Optional[Any]=None						,	lowercase_				:List[Any]=None						,	lowercase_				:Dict=None						,	lowercase_				:List[str]="replace"						,	lowercase_				:Dict="<s>"						,	lowercase_				:Union[str, Any]="</s>"						,	lowercase_				:Union[str, Any]="</s>"						,	lowercase_				:int="<s>"						,	lowercase_				:Optional[Any]="<unk>"						,	lowercase_				:List[str]="<pad>"						,	lowercase_				:Optional[int]="<mask>"						,	lowercase_				:Union[str, Any]=False						,	lowercase_				:Dict=True						,	**lowercase_				:Optional[Any]						,	)->							Optional[int]:
     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				:str							)->							str:
     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				:List[Any]						,	lowercase_				:List[str]							)->							Optional[int]:
     A__    =      AddedToken(lowercase_						,	lstrip=lowercase_						,	rstrip=lowercase_							) if isinstance(lowercase_						,	lowercase_							) else value
     A__    =      value
 def 					UpperCAmelCase_							(    self				:List[Any]						,	*lowercase_				:Optional[Any]						,	**lowercase_				:Tuple							)->							BatchEncoding:
     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				:str						,	*lowercase_				:Optional[Any]						,	**lowercase_				:Optional[Any]							)->							BatchEncoding:
     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				:str						,	lowercase_				:str						,	lowercase_				:Optional[str] = None							)->							Tuple[str]:
     A__    =      self._tokenizer.model.save(lowercase_						,	name=lowercase_							)
     return tuple(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=None							)->							Optional[Any]:
     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				:List[str]						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None							)->							List[int]:
     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				:Union[str, Any]						,	lowercase_				:Union[Dict[str, EncodedInput], BatchEncoding]						,	lowercase_				:Optional[int] = None						,	lowercase_				:PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,	lowercase_				:Optional[int] = None						,	lowercase_				:Optional[bool] = None						,	)->							dict:
     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










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple	):
    # Return True if there is node that has not iterated.
    A__    =      [False] * len(_lowerCamelCase	)
    A__    =      []
    queue.append(_lowerCamelCase	)
    A__    =      True

    while queue:
        A__    =      queue.pop(0	)
        for ind in range(len(graph[u]	)	):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_lowerCamelCase	)
                A__    =      True
                A__    =      u

    return visited[t]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]	):
    # This array is filled by BFS and to store path
    A__    =      [-1] * (len(_lowerCamelCase	))
    A__    =      0
    while bfs(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      float("Inf"	)
        A__    =      sink

        while s != source:
            # Find the minimum value in select path
            A__    =      min(_lowerCamelCase						,	graph[parent[s]][s]	)
            A__    =      parent[s]

        max_flow += path_flow
        A__    =      sink

        while v != source:
            A__    =      parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            A__    =      parent[v]
    return max_flow


__lowerCAmelCase							:     Any		     =[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__lowerCAmelCase     ,					__lowerCAmelCase							:     int		     =0, 5



print(ford_fulkerson(graph, source, sink))



