






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				List[str] 			=					{
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[Any] 			=					["ElectraTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Dict 			=					[
         "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ElectraForCausalLM",
         "ElectraForMaskedLM",
         "ElectraForMultipleChoice",
         "ElectraForPreTraining",
         "ElectraForQuestionAnswering",
         "ElectraForSequenceClassification",
         "ElectraForTokenClassification",
         "ElectraModel",
         "ElectraPreTrainedModel",
         "load_tf_weights_in_electra",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Any 			=					[
         "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFElectraForMaskedLM",
         "TFElectraForMultipleChoice",
         "TFElectraForPreTraining",
         "TFElectraForQuestionAnswering",
         "TFElectraForSequenceClassification",
         "TFElectraForTokenClassification",
         "TFElectraModel",
         "TFElectraPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				int 			=					[
         "FlaxElectraForCausalLM",
         "FlaxElectraForMaskedLM",
         "FlaxElectraForMultipleChoice",
         "FlaxElectraForPreTraining",
         "FlaxElectraForQuestionAnswering",
         "FlaxElectraForSequenceClassification",
         "FlaxElectraForTokenClassification",
         "FlaxElectraModel",
         "FlaxElectraPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
     from .tokenization_electra import ElectraTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_electra_fast import ElectraTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_electra import (
              ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              ElectraForCausalLM,
              ElectraForMaskedLM,
              ElectraForMultipleChoice,
              ElectraForPreTraining,
              ElectraForQuestionAnswering,
              ElectraForSequenceClassification,
              ElectraForTokenClassification,
              ElectraModel,
              ElectraPreTrainedModel,
              load_tf_weights_in_electra,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_electra import (
              TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFElectraForMaskedLM,
              TFElectraForMultipleChoice,
              TFElectraForPreTraining,
              TFElectraForQuestionAnswering,
              TFElectraForSequenceClassification,
              TFElectraForTokenClassification,
              TFElectraModel,
              TFElectraPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_electra import (
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForMultipleChoice,
              FlaxElectraForPreTraining,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForSequenceClassification,
              FlaxElectraForTokenClassification,
              FlaxElectraModel,
              FlaxElectraPreTrainedModel,
          )

else:
     import sys

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



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					int = 50    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[[0] * 3 for _ in range(length + 1    )]

     for row_length in range(length + 1    ):
          for tile_length in range(2							,			5    ):
               for tile_start in range(row_length - tile_length + 1    ):
                    different_colour_ways_number[row_length][tile_length - 2] += (
                        different_colour_ways_number[row_length - tile_start - tile_length][
                            tile_length - 2
                        ]
                        + 1
                    )

     return sum(different_colour_ways_number[length]    )


if __name__ == "__main__":
     print(f'''{solution() = }''')
