





"""simple docstring"""
from __future__ import annotations




def 			__snake_case						(      __A ,__A ,__A ,__A    )		->				None:
   lowercase    :			Dict  =       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 			__snake_case						(      )		->				None:
   lowercase    :			Dict  =       [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
   lowercase    :			List[Any]  =       int(input("""Enter the element to be searched:"""    )    )
   print(__A    )
   search_in_a_sorted_matrix(__A ,len(__A    ) ,len(mat[0]    ) ,__A    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:	int							      =logging.get_logger(__name__)

lowerCAmelCase:	int							      ={
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""qdqbert"""



   def __init__(       self      ,   snake_case=3_0_5_2_2      ,   snake_case=7_6_8      ,   snake_case=1_2      ,   snake_case=1_2      ,   snake_case=3_0_7_2      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=5_1_2      ,   snake_case=2      ,   snake_case=0.02      ,   snake_case=1E-12      ,   snake_case=True      ,   snake_case=1      ,   snake_case=0      ,   snake_case=2      ,   **snake_case      ,   ) ->    List[str]:







      """simple docstring"""
      super().__init__(pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   **snake_case	)

      lowercase    :			Optional[Any]  =       vocab_size
      lowercase    :			int  =       max_position_embeddings
      lowercase    :			Dict  =       hidden_size
      lowercase    :			Union[str, Any]  =       num_hidden_layers
      lowercase    :			str  =       num_attention_heads
      lowercase    :			List[str]  =       intermediate_size
      lowercase    :			List[str]  =       hidden_act
      lowercase    :			List[str]  =       hidden_dropout_prob
      lowercase    :			Optional[Any]  =       attention_probs_dropout_prob
      lowercase    :			Optional[Any]  =       initializer_range
      lowercase    :			str  =       type_vocab_size
      lowercase    :			Optional[int]  =       layer_norm_eps
      lowercase    :			Optional[int]  =       use_cache


