
"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase          =							{
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase          =							[
					    """OpenLlamaForCausalLM""",
					    """OpenLlamaModel""",
					    """OpenLlamaPreTrainedModel""",
					    """OpenLlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

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

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

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

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




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     1.0
	for i in range(1,				r + 1 ):
		ret *= (n + 1 - i) / i
	return round(_lowerCamelCase )




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return choose(8 + n,				_lowerCamelCase ) + choose(9 + n,				_lowerCamelCase ) - 10




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return sum(non_bouncy_exact(_lowerCamelCase ) for i in range(1,				n + 1 ) )




def __magic_name__  (							_lowerCamelCase:		int = 100 )				->	int:

	'''simple docstring'''




	return non_bouncy_upto(_lowerCamelCase )


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


