



"""simple docstring"""







from __future__ import annotations


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Tuple     =   {1}
		_lowercase				: Any     =   [1]
		for _ in range(2   ,				depth + 1      ):
				_lowercase				: Tuple     =   [*previous_coefficients, 0]
				_lowercase				: Union[str, Any]     =   [0, *previous_coefficients]
				_lowercase				: int     =   []
				for x, y in zip(__UpperCAmelCase   ,				__UpperCAmelCase      ):
						coefficients.add(x + y      )
						previous_coefficients.append(x + y      )
		return coefficients


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: str     =   set()
		for number in unique_coefficients:
				_lowercase				: List[Any]     =   2
				_lowercase				: List[Any]     =   number
				while divisor**2 <= copy_number:
						_lowercase				: List[Any]     =   0
						while copy_number % divisor == 0:
								copy_number //= divisor
								multiplicity += 1
						if multiplicity >= 2:
								non_squarefrees.add(__UpperCAmelCase      )
								break
						divisor += 1

		return unique_coefficients.difference(__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase = 51      ):
		_lowercase				: List[Any]     =   get_pascal_triangle_unique_coefficients(__UpperCAmelCase      )
		_lowercase				: Tuple     =   get_squarefrees(__UpperCAmelCase      )
		return sum(__UpperCAmelCase      )


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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Dict     						=							[
			    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SamModel''',
			    '''SamPreTrainedModel''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							[
			    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFSamModel''',
			    '''TFSamPreTrainedModel''',
			]
try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							['''SamImageProcessor''']


if TYPE_CHECKING:
			from .configuration_sam import (
			    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SamConfig,
			    SamMaskDecoderConfig,
			    SamPromptEncoderConfig,
			    SamVisionConfig,
			)
			from .processing_sam import SamProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_sam import SamImageProcessor

else:
			import sys

			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

