



"""simple docstring"""

from math import pi, sqrt





def       __magic_name__    (   _lowerCamelCase				: float					):
							if num <= 0:
														raise ValueError("""math domain error"""					)
							if num > 1_7_1.5:
														raise OverflowError("""math range error"""					)
							elif num - int(_lowerCamelCase					) not in (0, 0.5):
														raise NotImplementedError("""num must be an integer or a half-integer"""					)
							elif num == 0.5:
														return sqrt(_lowerCamelCase					)
							else:
														return 1.0 if num == 1 else (num - 1) * gamma(num - 1					)





def       __magic_name__    (   ):
							assert gamma(0.5					) == sqrt(_lowerCamelCase					)
							assert gamma(1					) == 1.0
							assert gamma(2					) == 1.0


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

	testmod()
	lowercase__			   =  1.0
	while num:
		lowercase__			   =  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,
)


lowercase__			   =  {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "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

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

