



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class 					_A (			tf.keras.layers.Layer					):



       def __init__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  int = None  ,	__SCREAMING_SNAKE_CASE							:  int = None):


           '''simple docstring'''

           super().__init__()
           __a		 =       pad_token_id
           __a		 =       max_length
           __a		 =       vocab
           __a		 =       merges
           __a		 =       BytePairTokenizer(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	sequence_length=__SCREAMING_SNAKE_CASE)



       @classmethod
       def 	_lowerCamelCase    (	cls							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  GPTaTokenizer  ,	*__SCREAMING_SNAKE_CASE							:  int  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __a		 =       [''' '''.join(__SCREAMING_SNAKE_CASE) for m in tokenizer.bpe_ranks.keys()]
           __a		 =       tokenizer.get_vocab()
           return cls(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       @classmethod
       def 	_lowerCamelCase    (	cls							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, os.PathLike]  ,	*__SCREAMING_SNAKE_CASE							:  Tuple  ,	**__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           __a		 =       GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  ,	*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)
           return cls.from_tokenizer(__SCREAMING_SNAKE_CASE  ,	*__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       @classmethod
       def 	_lowerCamelCase    (	cls							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           return cls(**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }





       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  int = None):


           '''simple docstring'''

           __a		 =       self.tf_tokenizer(__SCREAMING_SNAKE_CASE)
           __a		 =       tf.ones_like(__SCREAMING_SNAKE_CASE)

           if self.pad_token_id is not None:
               # pad the tokens up to max length
               __a		 =       max_length if max_length is not None else self.max_length

               if max_length is not None:
                   __a ,     __a		 =       pad_model_inputs(
                       __SCREAMING_SNAKE_CASE  ,	max_seq_length=__SCREAMING_SNAKE_CASE  ,	pad_value=self.pad_token_id)

           return {"attention_mask": attention_mask, "input_ids": input_ids}










__snake_case		:str   	=  [
    [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],
]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # Return True if there is node that has not iterated.
    __a		 =       [False] * len(_UpperCAmelCase       )
    __a		 =       [s]
    __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(_UpperCAmelCase       )
                __a		 =       True
                __a		 =       u

    return visited[t]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [-1] * (len(_UpperCAmelCase       ))
    __a		 =       0
    __a		 =       []
    __a		 =       [i[:] for i in graph]  # Record original cut, copy.
    while bfs(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       float('''Inf'''       )
        __a		 =       sink

        while s != source:
            # Find the minimum value in select path
            __a		 =       min(_UpperCAmelCase     ,				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]

    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(graph[0]       )       ):
            if graph[i][j] == 0 and temp[i][j] > 0:
                res.append((i, j)       )

    return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))





