

from __future__ import annotations





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    None:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase       		=    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 						__SCREAMING_SNAKE_CASE  (    )  ->    None:







			'''simple docstring'''

			UpperCAmelCase       		=    [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
			UpperCAmelCase       		=    int(input('''Enter the element to be searched:'''			)			)
			print(UpperCamelCase__			)
			search_in_a_sorted_matrix(UpperCamelCase__      ,      len(UpperCamelCase__			)      ,      len(mat[0]			)      ,      UpperCamelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__A    :   Tuple          =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[str]          =       [
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				__A    :   Tuple          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



