from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__lowerCamelCase     :					List[Any]				  =						'''Update an existing config file with the latest defaults while maintaining the old configuration.'''



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[str]      )				->   Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= args.config_file
  if config_file is None and Path(a_      ).exists():
    SCREAMING_SNAKE_CASE__     				= default_config_file
  elif not Path(a_      ).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""      )
  SCREAMING_SNAKE_CASE__     				= load_config_from_file(a_      )

  if config_file.endswith(""".json"""      ):
    config.to_json_file(a_      )
  else:
    config.to_yaml_file(a_      )
  return config_file



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]					,	__UpperCamelCase       :			List[Any]      )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= parser.add_parser("""update"""					,	parents=a_					,	help=a_					,	formatter_class=a_      )
  parser.add_argument(
      """--config_file"""					,	default=a_					,	help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have """
          """such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed """
          """with \'huggingface\'."""
      )					,	)

  parser.set_defaults(func=a_      )
  return parser






def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]      )				->   Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= update_config(a_      )
  print(f"""Sucessfully updated the configuration file at {config_file}."""      )








'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor