





'''simple docstring'''




from typing import TYPE_CHECKING

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


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

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

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

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

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









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   int			)   ->   bool:
					if not isinstance(lowerCamelCase__			,		lowerCamelCase__			):
										A_ :     int =							f'Input value of [number={number}] must be an integer'
										raise TypeError(lowerCamelCase__			)
					if number < 0 or number % 2 == 1:
										return False
					A_ :     Dict =							int(number**0.5			)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



