code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Union[str, Any]          =    {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : int          =    ["LlamaTokenizer"]
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : List[Any]          =    ["LlamaTokenizerFast"]
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			SCREAMING_SNAKE_CASE : List[str]          =    [
  			    "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
  
  			SCREAMING_SNAKE_CASE : Union[str, Any]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
  							if n in (0, 1):
  														return 0
  							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  