





from __future__ import annotations







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					if len(lowerCamelCase_  ) < 2:
										raise ValueError('Monogons and Digons are not polygons in the Euclidean space'  )
					if any(i <= 0 for i in nums  ):
										raise ValueError('All values must be greater than 0'  )
					__A	:						Tuple       =				nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      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

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


