from __future__ import annotations
def 				__lowercase	(						lowerCamelCase__					)				->set[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(lowercase__ ,				lowercase__					):
      coefficients.add(x + y					)
      previous_coefficients.append(x + y					)
  return coefficients
def 				__lowercase	(						lowerCamelCase__					)				->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(lowercase__					)
        break
      divisor += 1

  return unique_coefficients.difference(lowercase__					)
def 				__lowercase	(						lowerCamelCase__ = 51					)				->int:

  '''simple docstring'''




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


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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   						=    {
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    [
         """OpenLlamaForCausalLM""",
         """OpenLlamaModel""",
         """OpenLlamaPreTrainedModel""",
         """OpenLlamaForSequenceClassification""",
     ]


if TYPE_CHECKING:
     from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_open_llama import (
              OpenLlamaForCausalLM,
              OpenLlamaForSequenceClassification,
              OpenLlamaModel,
              OpenLlamaPreTrainedModel,
          )


else:
     import sys

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

