code: 
  
  
  '''simple docstring'''
  
  
  
  from itertools import combinations_with_replacement
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       int = 1_00							)  ->	int:
       __lowerCamelCase     :			list[int]              =	[*list(range(1   ,					21							)							), 25]
       __lowerCamelCase     :			list[int]              =	[2 * x for x in range(1   ,					21							)] + [50]
       __lowerCamelCase     :			list[int]              =	[3 * x for x in range(1   ,					21							)]
       __lowerCamelCase     :			list[int]              =	singles + doubles + triples + [0]
  
       __lowerCamelCase     :			int              =	0
       __lowerCamelCase     :			int
       __lowerCamelCase     :			int
       __lowerCamelCase     :			int
       __lowerCamelCase     :			int
  
       for double in doubles:
            for throwa, throwa in combinations_with_replacement(UpperCAmelCase_   ,					2							):
                 __lowerCamelCase     :			Any              =	double + throwa + throwa
                 if checkout_total < limit:
                      num_checkouts += 1
  
       return num_checkouts
  
  
  if __name__ == "__main__":
        print(f'''{solution() = }''')
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A__	:      List[str]				=						{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """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:
        A__	:      int				=						[
            """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
  
        A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  