




class 		lowercase							:  # Public class to implement a graph

     def __init__(						self	,  _a	,  _a	,  _a       )		->							None:
           _A							:  Optional[int]    =		row
           _A							:  Optional[Any]    =		col
           _A							:  Any    =		graph

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							bool:
           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							None:
           # Checking all 8 elements surrounding nth element
           _A							:  Dict    =		[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           _A							:  int    =		[-1, 0, 1, -1, 1, -1, 0, 1]
           _A							:  Optional[int]    =		True  # Make those cells visited
           for k in range(8       ):
                 if self.is_safe(i + row_nbr[k]	,  j + col_nbr[k]	,  _a       ):
                       self.diffs(i + row_nbr[k]	,  j + col_nbr[k]	,  _a       )



     def       a__			(						self       )		->							int:  # And finally, count all islands.
           _A							:  Union[str, Any]    =		[[False for j in range(self.COL       )] for i in range(self.ROW       )]
           _A							:  Dict    =		0
           for i in range(self.ROW       ):
                 for j in range(self.COL       ):
                       if visited[i][j] is False and self.graph[i][j] == 1:
                             self.diffs(_a	,  _a	,  _a       )
                             count += 1
           return count







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 		lowercase							(     tf.keras.layers.Layer	):

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

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

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



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

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

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

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

