




'''simple docstring'''






from __future__ import annotations

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
			__UpperCamelCase		, __UpperCamelCase				:      int									=       m - 1, 0
			while i >= 0 and j < n:
						if key == mat[i][j]:
									print(F"Key {key} found at row- {i + 1} column- {j + 1}"						)
									return
						if key < mat[i][j]:
									i -= 1
						else:
									j += 1
			print(F"Key {key} not found"						)

def       __lowerCAmelCase (   ):
			__UpperCamelCase				:      int									=       [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
			__UpperCamelCase				:      Tuple									=       int(input("Enter the element to be searched:"						)						)
			print(snake_case__						)
			search_in_a_sorted_matrix(snake_case__ ,				len(snake_case__						) ,				len(mat[0]						) ,				snake_case__						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()









'''simple docstring'''






from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				     = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

_lowerCAmelCase				     = {
    '''openai-gpt''': 512,
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    VOCAB_FILES_NAMES
				A				     =    PRETRAINED_VOCAB_FILES_MAP
				A				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A				     =    ["input_ids", "attention_mask"]
				A				     =    OpenAIGPTTokenizer
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase="<unk>"			,  **_UpperCAmelCase      )     ->	str:
							super().__init__(_UpperCAmelCase			,  _UpperCAmelCase			,  tokenizer_file=_UpperCAmelCase			,  unk_token=_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict:
							return True





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None      )     ->	Tuple[str]:
							__UpperCamelCase				:      Optional[Any]									=       self._tokenizer.model.save(_UpperCAmelCase			,  name=_UpperCAmelCase      )
							return tuple(_UpperCAmelCase      )



