

from __future__ import annotations




def       UpperCAmelCase__    (      _A				:			int							):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			set[int]							):





  '''simple docstring'''







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

  return unique_coefficients.difference(_A							)




def       UpperCAmelCase__    (      _A				:			int = 51							):





  '''simple docstring'''







  a__  =get_pascal_triangle_unique_coefficients(_A							)
  a__  =get_squarefrees(_A							)
  return sum(_A							)


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



from typing import TYPE_CHECKING

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


lowerCamelCase									= {
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaubertForMultipleChoice''',
           '''FlaubertForQuestionAnswering''',
           '''FlaubertForQuestionAnsweringSimple''',
           '''FlaubertForSequenceClassification''',
           '''FlaubertForTokenClassification''',
           '''FlaubertModel''',
           '''FlaubertWithLMHeadModel''',
           '''FlaubertPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFFlaubertForMultipleChoice''',
           '''TFFlaubertForQuestionAnsweringSimple''',
           '''TFFlaubertForSequenceClassification''',
           '''TFFlaubertForTokenClassification''',
           '''TFFlaubertModel''',
           '''TFFlaubertPreTrainedModel''',
           '''TFFlaubertWithLMHeadModel''',
       ]


if TYPE_CHECKING:
       from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
       from .tokenization_flaubert import FlaubertTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )

else:
       import sys

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