

'''simple docstring'''






from typing import TYPE_CHECKING

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


snake_case_       =       {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case_       =       ['SegformerFeatureExtractor']
      snake_case_       =       ['SegformerImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case_       =       [
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case_       =       [
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_segformer import SegformerFeatureExtractor
            from .image_processing_segformer import SegformerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_segformer import (
                SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                SegformerDecodeHead,
                SegformerForImageClassification,
                SegformerForSemanticSegmentation,
                SegformerLayer,
                SegformerModel,
                SegformerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_segformer import (
                TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSegformerDecodeHead,
                TFSegformerForImageClassification,
                TFSegformerForSemanticSegmentation,
                TFSegformerModel,
                TFSegformerPreTrainedModel,
            )

else:
      import sys

      snake_case_       =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''
from __future__ import annotations


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> set[int]:


	"""simple docstring"""





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


def       a__    (  _SCREAMING_SNAKE_CASE					:    set[int]							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   set()
	for number in unique_coefficients:
		UpperCAmelCase_			:	Tuple      =   2
		UpperCAmelCase_			:	Tuple      =   number
		while divisor**2 <= copy_number:
			UpperCAmelCase_			:	List[Any]      =   0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(_SCREAMING_SNAKE_CASE							)
				break
			divisor += 1

	return unique_coefficients.difference(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    int = 51							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   get_pascal_triangle_unique_coefficients(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Union[str, Any]      =   get_squarefrees(_SCREAMING_SNAKE_CASE							)
	return sum(_SCREAMING_SNAKE_CASE							)


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




