code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      assert (
          isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) and mask > 0
      ), F'''mask needs to be positive integer, your input {mask}'''
      UpperCamelCase :Any										=     []
      UpperCamelCase :Tuple										=     mask
  
      while submask:
          all_submasks.append(SCREAMING_SNAKE_CASE__ )
          UpperCamelCase :Optional[int]										=     (submask - 1) & mask
  
      return all_submasks
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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__)
  
  
  
  
  
  