




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase__     						=						{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """GPTNeoForCausalLM""",
			    """GPTNeoForQuestionAnswering""",
			    """GPTNeoForSequenceClassification""",
			    """GPTNeoForTokenClassification""",
			    """GPTNeoModel""",
			    """GPTNeoPreTrainedModel""",
			    """load_tf_weights_in_gpt_neo""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """FlaxGPTNeoForCausalLM""",
			    """FlaxGPTNeoModel""",
			    """FlaxGPTNeoPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

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





"""simple docstring"""






import math







def      _snake_case			(				lowercase__		):
				return math.sqrt(lowercase__		) * math.sqrt(lowercase__		) == num







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			List[Any]    =    n
				while left <= right:
								_lowerCamelCase     :			str    =    (left + right) // 2
								if mid**2 == n:
												return True
								elif mid**2 > n:
												_lowerCamelCase     :			str    =    mid - 1
								else:
												_lowerCamelCase     :			Optional[int]    =    mid + 1
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()