



from __future__ import annotations




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				list[list[int]]   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int | float      )					->						None:
				__lowercase		, __lowercase								= 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:
				__lowercase								= [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
				__lowercase								= int(input('Enter the element to be searched:'      )      )
				print(SCREAMING_SNAKE_CASE      )
				search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE   ,							len(SCREAMING_SNAKE_CASE      )   ,							len(mat[0]      )   ,							SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





