


'''simple docstring'''







# using dfs for finding eulerian path traversal






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A=None					):







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       (path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCAmelCase			,     _lowerCAmelCase    :							Union[str, Any]   			=       True, True
                     _lowerCAmelCase    :							int   			=       dfs(_A   ,						_A   ,						_A   ,						_A					)
       return path






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       0
       _lowerCAmelCase    :							Tuple   			=       -1
       for i in range(_A					):
              if i not in graph.keys():
                     continue
              if len(graph[i]					) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCAmelCase    :							List[str]   			=       i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       [[False for _ in range(max_node + 1					)] for _ in range(max_node + 1					)]
       _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       check_circuit_or_path(_A   ,						_A					)
       if check == 3:
              print('graph is not Eulerian'					)
              print('no path'					)
              return
       _lowerCAmelCase    :							Tuple   			=       1
       if check == 2:
              _lowerCAmelCase    :							int   			=       odd_node
              print('graph has a Euler path'					)
       if check == 1:
              print('graph has a Euler cycle'					)
       _lowerCAmelCase    :							Optional[Any]   			=       dfs(_A   ,						_A   ,						_A					)
       print(_A					)






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCAmelCase    :							Dict   			=       {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCAmelCase    :							int   			=       {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCAmelCase    :							int   			=       {1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCAmelCase    :							List[Any]   			=       {
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCAmelCase    :							int   			=       1_0
       check_euler(_A   ,						_A					)
       check_euler(_A   ,						_A					)
       check_euler(_A   ,						_A					)
       check_euler(_A   ,						_A					)
       check_euler(_A   ,						_A					)


if __name__ == "__main__":
       main()





'''simple docstring'''







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 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







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

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

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

