






from itertools import combinations_with_replacement






def        lowerCamelCase__   (					__lowerCamelCase    :  int = 100	):
  __UpperCAmelCase							:			list[int]     =						[*list(range(1						,	21	)	), 25]
  __UpperCAmelCase							:			list[int]     =						[2 * x for x in range(1						,	21	)] + [50]
  __UpperCAmelCase							:			list[int]     =						[3 * x for x in range(1						,	21	)]
  __UpperCAmelCase							:			list[int]     =						singles + doubles + triples + [0]

  __UpperCAmelCase							:			int     =						0
  __UpperCAmelCase							:			int
  __UpperCAmelCase							:			int
  __UpperCAmelCase							:			int
  __UpperCAmelCase							:			int

  for double in doubles:
    for throwa, throwa in combinations_with_replacement(__lowerCamelCase						,	2	):
      __UpperCAmelCase							:			str     =						double + throwa + throwa
      if checkout_total < limit:
        num_checkouts += 1

  return num_checkouts


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










from typing import TYPE_CHECKING

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


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

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


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "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	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


