






"""simple docstring"""







from typing import TYPE_CHECKING

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


_UpperCamelCase     			=  {
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase     			=  [
      """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """EsmForMaskedLM""",
      """EsmForSequenceClassification""",
      """EsmForTokenClassification""",
      """EsmModel""",
      """EsmPreTrainedModel""",
  ]
  _UpperCamelCase     			=  ["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase     			=  [
      """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFEsmForMaskedLM""",
      """TFEsmForSequenceClassification""",
      """TFEsmForTokenClassification""",
      """TFEsmModel""",
      """TFEsmPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
  from .tokenization_esm import EsmTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmForMaskedLM,
        EsmForSequenceClassification,
        EsmForTokenClassification,
        EsmModel,
        EsmPreTrainedModel,
    )
    from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_esm import (
        TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEsmForMaskedLM,
        TFEsmForSequenceClassification,
        TFEsmForTokenClassification,
        TFEsmModel,
        TFEsmPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase     			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





"""simple docstring"""




from __future__ import annotations

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	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   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	[[2, 5, 7], [4, 8, 1_3], [9, 1_1, 1_5], [1_2, 1_7, 2_0]]
					SCREAMING_SNAKE_CASE				=	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()
