



'''simple docstring'''





from math import factorial





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:

      # If either of the conditions are true, the function is being asked
      # to calculate a factorial of a negative number, which is not possible
      if n < k or k < 0:
            raise ValueError("""Please enter positive integers for n and k where n >= k"""		)
      return factorial(_lowerCAmelCase		) // (factorial(_lowerCAmelCase		) * factorial(n - k		))


if __name__ == "__main__":
       print(
           "The number of five-card hands possible from a standard",
           F"fifty-two card deck is: {combinations(52, 5)}\n",
       )

       print(
           "If a class of 40 students must be arranged into groups of",
           F"4 for group projects, there are {combinations(40, 4)} ways",
           "to arrange them.\n",
       )

       print(
           "If 10 teams are competing in a Formula One race, there",
           F"are {combinations(10, 3)} ways that first, second and",
           "third place can be awarded.",
       )





'''simple docstring'''





from typing import TYPE_CHECKING

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


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

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