code: from collections import namedtuple
  
  _SCREAMING_SNAKE_CASE   						=    namedtuple("""from_to""", """from_ to""")
  
  _SCREAMING_SNAKE_CASE   						=    {
      """millimeter""": """mm""",
      """centimeter""": """cm""",
      """meter""": """m""",
      """kilometer""": """km""",
      """inch""": """in""",
      """inche""": """in""",  # Trailing 's' has been stripped off
      """feet""": """ft""",
      """foot""": """ft""",
      """yard""": """yd""",
      """mile""": """mi""",
  }
  
  _SCREAMING_SNAKE_CASE   						=    {
      """mm""": from_to(0.001, 1000),
      """cm""": from_to(0.01, 100),
      """m""": from_to(1, 1),
      """km""": from_to(1000, 0.001),
      """in""": from_to(0.0254, 39.3701),
      """ft""": from_to(0.3048, 3.28084),
      """yd""": from_to(0.9144, 1.09361),
      """mi""": from_to(1609.34, 0.000621371),
  }
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->float:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     from_type.lower().rstrip("s"					)
    a__     		=     TYPE_CONVERSION.get(lowerCamelCase__ ,				lowerCamelCase__					)
    a__     		=     to_type.lower().rstrip("s"					)
    a__     		=     TYPE_CONVERSION.get(lowerCamelCase__ ,				lowerCamelCase__					)
    if new_from not in METRIC_CONVERSION:
      a__     		=     (
          f"Invalid 'from_type' value: {from_type!r}.\n"
          f"Conversion abbreviations are: {', '.join(lowerCamelCase__					)}"
      )
      raise ValueError(lowerCamelCase__					)
    if new_to not in METRIC_CONVERSION:
      a__     		=     (
          f"Invalid 'to_type' value: {to_type!r}.\n"
          f"Conversion abbreviations are: {', '.join(lowerCamelCase__					)}"
      )
      raise ValueError(lowerCamelCase__					)
    return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to
  
  
  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,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """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   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_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
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  