code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["LlamaTokenizer"]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["LlamaTokenizerFast"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "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
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		bool:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if not isinstance(UpperCAmelCase				,UpperCAmelCase      ):
  										SCREAMING_SNAKE_CASE_	      =       f'''Input value of [number={number}] must be an integer'''
  										raise TypeError(UpperCAmelCase      )
  					if number < 0 or number % 2 == 1:
  										return False
  					SCREAMING_SNAKE_CASE_	      =       int(number**0.5      )
  					return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  