





from collections import defaultdict
from math import gcd




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int = 150_0000   )   ->			int:
    UpperCamelCase__							:       defaultdict      =   defaultdict(__UpperCAmelCase   )
    UpperCamelCase__							:       List[str]      =   2
    while 2 * euclid_m * (euclid_m + 1) <= limit:
        for euclid_n in range((euclid_m % 2) + 1	,     __UpperCAmelCase	,     2   ):
            if gcd(__UpperCAmelCase	,     __UpperCAmelCase   ) > 1:
                continue
            UpperCamelCase__							:       List[Any]      =   2 * euclid_m * (euclid_m + euclid_n)
            for perimeter in range(__UpperCAmelCase	,     limit + 1	,     __UpperCAmelCase   ):
                frequencies[perimeter] += 1
        euclid_m += 1
    return sum(1 for frequency in frequencies.values() if frequency == 1   )


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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

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

