



"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							int = 50							)	->       int:
				_lowerCamelCase        =				[1] * (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							):
																ways_number[row_length] += ways_number[
																    row_length - tile_start - tile_length
																]

				return ways_number[length]


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





"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
