






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 						SCREAMING_SNAKE_CASE				( tf.keras.layers.Layer					):





       """simple docstring"""

       def __init__(  self:   List[Any]		,     __A:   Dict[str, int]		,     __A:   List[str]		,     __A:   int = None		,     __A:   int = None   )    ->				Any:
            super().__init__()
            _A								= pad_token_id
            _A								= max_length
            _A								= vocab
            _A								= merges
            _A								= BytePairTokenizer(__A		,     __A		,     sequence_length=__A   )

       @classmethod
       def       __A (  cls:   int		,     __A:   GPTaTokenizer		,     *__A:   Any		,     **__A:   Optional[int]   )    ->				int:
            _A								= [''' '''.join(__A   ) for m in tokenizer.bpe_ranks.keys()]
            _A								= tokenizer.get_vocab()
            return cls(__A		,     __A		,     *__A		,     **__A   )

       @classmethod
       def       __A (  cls:   Optional[int]		,     __A:   Union[str, os.PathLike]		,     *__A:   str		,     **__A:   Tuple   )    ->				Optional[Any]:
            _A								= GPTaTokenizer.from_pretrained(__A		,     *__A		,     **__A   )
            return cls.from_tokenizer(__A		,     *__A		,     **__A   )

       @classmethod
       def       __A (  cls:   int		,     __A:   Tuple   )    ->				Tuple:
            return cls(**__A   )

       def       __A (  self:   str   )    ->				List[Any]:
            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }



       def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   int = None   )    ->				Dict:
            _A								= self.tf_tokenizer(__A   )
            _A								= tf.ones_like(__A   )

            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(
                          __A		,     max_seq_length=__A		,     pad_value=self.pad_token_id   )

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











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

					for values in graph.values():
										for i in values:
															indegree[i] += 1

					for i in range(len(_lowercase ) ):
										if indegree[i] == 0:
															queue.append(_lowercase )

					while queue:
										_A								= queue.pop(0 )
										cnt += 1
										topo.append(_lowercase )
										for x in graph[vertex]:
															indegree[x] -= 1
															if indegree[x] == 0:
																				queue.append(_lowercase )

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



