
"""simple docstring"""




from math import pi, sqrt


def   __magic_name__	(      __snake_case				:       float  ) ->				float:
       if num <= 0:
              raise ValueError("math domain error"  )
       if num > 1_71.5:
              raise OverflowError("math range error"  )
       elif num - int(__snake_case  ) not in (0, 0.5):
              raise NotImplementedError("num must be an integer or a half-integer"  )
       elif num == 0.5:
              return sqrt(__snake_case  )
       else:
              return 1.0 if num == 1 else (num - 1) * gamma(num - 1  )


def   __magic_name__	(      ) ->				None:
       assert gamma(0.5  ) == sqrt(__snake_case  )
       assert gamma(1  ) == 1.0
       assert gamma(2  ) == 1.0


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   _A			:       Tuple   		=       1.0
   while num:
      _A			:       List[Any]   		=       float(input("""Gamma of: """))
      print(F"gamma({num}) = {gamma(num)}")
      print("""\nEnter 0 to exit...""")




"""simple docstring"""




from typing import TYPE_CHECKING

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


_A			:       Optional[int]   		=       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Union[str, Any]   		=       ["""LlamaTokenizer"""]

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       int   		=       [
       """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			:       str   		=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


