



from __future__ import annotations


def 	_UpperCAmelCase       (   snake_case    ):





     """simple docstring"""

     _lowerCAmelCase      			=					{1}
     _lowerCAmelCase      			=					[1]
     for _ in range(2			,		depth + 1    ):
          _lowerCAmelCase      			=					[*previous_coefficients, 0]
          _lowerCAmelCase      			=					[0, *previous_coefficients]
          _lowerCAmelCase      			=					[]
          for x, y in zip(snake_case			,		snake_case    ):
               coefficients.add(x + y    )
               previous_coefficients.append(x + y    )
     return coefficients


def 	_UpperCAmelCase       (   snake_case    ):





     """simple docstring"""

     _lowerCAmelCase      			=					set()
     for number in unique_coefficients:
          _lowerCAmelCase      			=					2
          _lowerCAmelCase      			=					number
          while divisor**2 <= copy_number:
               _lowerCAmelCase      			=					0
               while copy_number % divisor == 0:
                    copy_number //= divisor
                    multiplicity += 1
               if multiplicity >= 2:
                    non_squarefrees.add(snake_case    )
                    break
               divisor += 1

     return unique_coefficients.difference(snake_case    )


def 	_UpperCAmelCase       (   snake_case = 51    ):





     """simple docstring"""

     _lowerCAmelCase      			=					get_pascal_triangle_unique_coefficients(snake_case    )
     _lowerCAmelCase      			=					get_squarefrees(snake_case    )
     return sum(snake_case    )


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








from typing import TYPE_CHECKING

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


A__			   = {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """HubertForCTC""",
	    """HubertForSequenceClassification""",
	    """HubertModel""",
	    """HubertPreTrainedModel""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFHubertForCTC""",
	    """TFHubertModel""",
	    """TFHubertPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_hubert import (
		    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    HubertForCTC,
		    HubertForSequenceClassification,
		    HubertModel,
		    HubertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_hubert import (
		    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFHubertForCTC,
		    TFHubertModel,
		    TFHubertPreTrainedModel,
		)


else:
	import sys

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



