'''simple docstring'''

def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int   )		->					int:





   '''simple docstring'''







   if not isinstance(lowercase_    ,		lowercase_   ) or number < 0:
      raise ValueError("Input must be a non-negative integer"   )

   UpperCamelCase__ 						=						number & ~(number - 1)
   UpperCamelCase__ 						=						0
   while intermediate:
      intermediate >>= 1
      index += 1
   return index - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)



'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowercase:		int							=     {
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		Union[str, Any]							=     [
           "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

       __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)