


from functools import lru_cache







@lru_cache
def 	A (		A__			):
     if not isinstance(A__		,		A__			):
          raise TypeError("k must be an integer."			)
     if k < 0:
          raise ValueError("k integer must be greater or equal to zero."			)
     if k == 0:
          return 0
     elif k == 1:
          return 1
     else:
          return fibonacci(k - 1			) + fibonacci(k - 2			)







def 	A (		A__		,		A__			):
     UpperCAmelCase    :					Dict			=      len(A__			)
     # Find m such that F_m >= n where F_i is the i_th fibonacci number.
     UpperCAmelCase    :					Dict			=      0
     while True:
          if fibonacci(A__			) >= len_list:
               UpperCAmelCase    :					Union[str, Any]			=      i
               break
          i += 1
     UpperCAmelCase    :					Union[str, Any]			=      0
     while fibb_k > 0:
          UpperCAmelCase    :					Tuple			=      min(
              offset + fibonacci(fibb_k - 1			)		,		len_list - 1			)  # Prevent out of range
          UpperCAmelCase    :					Any			=      arr[index_k]
          if item_k_1 == val:
               return index_k
          elif val < item_k_1:
               fibb_k -= 1
          elif val > item_k_1:
               offset += fibonacci(fibb_k - 1			)
               fibb_k -= 2
     else:
          return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()







from typing import TYPE_CHECKING

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


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

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



