

'''simple docstring'''




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   _SCREAMING_SNAKE_CASE    (    lowerCAmelCase_	):
 __SCREAMING_SNAKE_CASE     :Union[str, Any]			       =     ["""image_processor""", """tokenizer"""]
 __SCREAMING_SNAKE_CASE     :List[str]			       =     """AutoImageProcessor"""
 __SCREAMING_SNAKE_CASE     :List[str]			       =     """AutoTokenizer"""



 def __init__(    self		:    Any ,      a__		:    int=None ,      a__		:    Union[str, Any]=None ,      **a__		:    Optional[Any]			):
      __magic_name__						   =				None
      if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.''' ,      __SCREAMING_SNAKE_CASE ,      )
           __magic_name__						   =				kwargs.pop('''feature_extractor'''			)

      __magic_name__						   =				image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError('''You have to specify an image_processor.'''			)
      if tokenizer is None:
           raise ValueError('''You have to specify a tokenizer.'''			)

      super().__init__(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE			)
      __magic_name__						   =				self.image_processor



 def __call__(    self		:    Union[str, Any] ,      a__		:    Union[str, Any]=None ,      a__		:    Dict=None ,      a__		:    Dict=None ,      **a__		:    str			):

      if text is None and images is None:
           raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

      if text is not None:
           __magic_name__						   =				self.tokenizer(__SCREAMING_SNAKE_CASE ,      return_tensors=__SCREAMING_SNAKE_CASE ,      **__SCREAMING_SNAKE_CASE			)

      if images is not None:
           __magic_name__						   =				self.image_processor(__SCREAMING_SNAKE_CASE ,      return_tensors=__SCREAMING_SNAKE_CASE ,      **__SCREAMING_SNAKE_CASE			)

      if text is not None and images is not None:
           __magic_name__						   =				image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE			) ,      tensor_type=__SCREAMING_SNAKE_CASE			)



 def  snake_case__  (    self		:    Dict ,      *a__		:    Optional[int] ,      **a__		:    Any			):
      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE ,      **__SCREAMING_SNAKE_CASE			)



 def  snake_case__  (    self		:    List[Any] ,      *a__		:    List[str] ,      **a__		:    str			):
      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE ,      **__SCREAMING_SNAKE_CASE			)



 @property
 def  snake_case__  (    self		:    Optional[Any]			):
      __magic_name__						   =				self.tokenizer.model_input_names
      __magic_name__						   =				self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



 @property
 def  snake_case__  (    self		:    Union[str, Any]			):
      warnings.warn(
          '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,      __SCREAMING_SNAKE_CASE ,      )
      return self.image_processor_class



 @property
 def  snake_case__  (    self		:    Tuple			):
      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,      __SCREAMING_SNAKE_CASE ,      )
      return self.image_processor













'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
			    )	)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""					,	type=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

			# positional
			parser.add_argument(
			    """training_script"""					,	type=lowercase					,	help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )					,	)

			# rest from the training program
			parser.add_argument("""training_script_args"""					,	nargs=lowercase	)

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

			# Import training_script as a module.
			__lowerCAmelCase											=				Path(args.training_script	)
			sys.path.append(str(script_fpath.parent.resolve()	)	)
			__lowerCAmelCase											=				script_fpath.stem
			__lowerCAmelCase											=				importlib.import_module(lowercase	)

			# Patch sys.argv
			__lowerCAmelCase											=				[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

			xmp.spawn(mod._mp_fn					,	args=()					,	nprocs=args.num_cores	)


if __name__ == "__main__":
	main()

