






"""simple docstring"""



import math

A_      =							10
A_      =							7
A_      =							BALLS_PER_COLOUR * NUM_COLOURS




def        _lowerCAmelCase						(							UpperCAmelCase__    :   int = 2_0 )						->str:
						A__  :					List[Any]											=					math.comb(UpperCAmelCase__,  UpperCAmelCase__ )
						A__  :					Optional[int]											=					math.comb(NUM_BALLS - BALLS_PER_COLOUR,  UpperCAmelCase__ )

						A__  :					int											=					NUM_COLOURS * (1 - missing_colour / total)

						return f'{result:.9f}'


if __name__ == "__main__":
						print(solution(20))














"""simple docstring"""



from typing import TYPE_CHECKING

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


A_      =							{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =							[
						    '''LlamaForCausalLM''',
						    '''LlamaModel''',
						    '''LlamaPreTrainedModel''',
						    '''LlamaForSequenceClassification''',
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

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

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

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

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






