code: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A							    =	{
      """configuration_mpnet""": ["""MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MPNetConfig"""],
      """tokenization_mpnet""": ["""MPNetTokenizer"""],
  }
  
  try:
  							if not is_tokenizers_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	["""MPNetTokenizerFast"""]
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """MPNetForMaskedLM""",
  							    """MPNetForMultipleChoice""",
  							    """MPNetForQuestionAnswering""",
  							    """MPNetForSequenceClassification""",
  							    """MPNetForTokenClassification""",
  							    """MPNetLayer""",
  							    """MPNetModel""",
  							    """MPNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFMPNetEmbeddings""",
  							    """TFMPNetForMaskedLM""",
  							    """TFMPNetForMultipleChoice""",
  							    """TFMPNetForQuestionAnswering""",
  							    """TFMPNetForSequenceClassification""",
  							    """TFMPNetForTokenClassification""",
  							    """TFMPNetMainLayer""",
  							    """TFMPNetModel""",
  							    """TFMPNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
  							from .tokenization_mpnet import MPNetTokenizer
  
  							try:
  														if not is_tokenizers_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .tokenization_mpnet_fast import MPNetTokenizerFast
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_mpnet import (
  														    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    MPNetForMaskedLM,
  														    MPNetForMultipleChoice,
  														    MPNetForQuestionAnswering,
  														    MPNetForSequenceClassification,
  														    MPNetForTokenClassification,
  														    MPNetLayer,
  														    MPNetModel,
  														    MPNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_mpnet import (
  														    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFMPNetEmbeddings,
  														    TFMPNetForMaskedLM,
  														    TFMPNetForMultipleChoice,
  														    TFMPNetForQuestionAnswering,
  														    TFMPNetForSequenceClassification,
  														    TFMPNetForTokenClassification,
  														    TFMPNetMainLayer,
  														    TFMPNetModel,
  														    TFMPNetPreTrainedModel,
  														)
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from __future__ import annotations
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =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_       (	)       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  							SCREAMING_SNAKE_CASE			: List[str]	     =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()
  
  
  
  
  
  
  