



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 				_snake_case			(					tf.keras.layers.Layer):




				def __init__(						self					:						Optional[Any],					__lowercase					:						Dict[str, int],					__lowercase					:						List[str],					__lowercase					:						int = None,					__lowercase					:						int = None						):
							super().__init__()
							lowercase__					  =				pad_token_id
							lowercase__					  =				max_length
							lowercase__					  =				vocab
							lowercase__					  =				merges
							lowercase__					  =				BytePairTokenizer(__lowercase,					__lowercase,					sequence_length=__lowercase						)




				@classmethod
				def 							A__      (						cls					:						List[Any],					__lowercase					:						GPTaTokenizer,					*__lowercase					:						Optional[int],					**__lowercase					:						int						):
							lowercase__					  =				[" ".join(__lowercase						) for m in tokenizer.bpe_ranks.keys()]
							lowercase__					  =				tokenizer.get_vocab()
							return cls(__lowercase,					__lowercase,					*__lowercase,					**__lowercase						)




				@classmethod
				def 							A__      (						cls					:						List[Any],					__lowercase					:						Union[str, os.PathLike],					*__lowercase					:						Any,					**__lowercase					:						str						):
							lowercase__					  =				GPTaTokenizer.from_pretrained(__lowercase,					*__lowercase,					**__lowercase						)
							return cls.from_tokenizer(__lowercase,					*__lowercase,					**__lowercase						)




				@classmethod
				def 							A__      (						cls					:						str,					__lowercase					:						Union[str, Any]						):
							return cls(**__lowercase						)




				def 							A__      (						self					:						Optional[Any]						):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}




				def 							A__      (						self					:						Any,					__lowercase					:						List[str],					__lowercase					:						int = None						):
							lowercase__					  =				self.tf_tokenizer(__lowercase						)
							lowercase__					  =				tf.ones_like(__lowercase						)

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

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

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__  ,       lowercase__					  =				len(SCREAMING_SNAKE_CASE_  ), len(grid[0]  )
			if (
			    min(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)  )

			lowercase__					  =				0
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row + 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row - 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col + 1						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col - 1						,  SCREAMING_SNAKE_CASE_  )

			visit.remove((row, col)  )
			return count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




