





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[str]       =	["""image_processor""", """tokenizer"""]
		__a       :       List[str]       =	"""AutoImageProcessor"""
		__a       :       Dict       =	"""AutoTokenizer"""






		def __init__(							self,   snake_case__=None,   snake_case__=None,   **snake_case__  )       -> Any:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																    """ instead.""",   snake_case__,   )
																lowercase_	:   Optional[int]  				=     kwargs.pop("""feature_extractor"""  )

									lowercase_	:   List[str]  				=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(snake_case__,   snake_case__  )
									lowercase_	:   Union[str, Any]  				=     self.image_processor
									lowercase_	:   int  				=     False






		def __call__(							self,   *snake_case__,   **snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									# For backward compatibility
									if self._in_target_context_manager:
																return self.current_processor(*snake_case__,   **snake_case__  )

									lowercase_	:   Tuple  				=     kwargs.pop("""images""",   snake_case__  )
									lowercase_	:   Optional[int]  				=     kwargs.pop("""text""",   snake_case__  )
									if len(snake_case__  ) > 0:
																lowercase_	:   Dict  				=     args[0]
																lowercase_	:   Tuple  				=     args[1:]

									if images is None and text is None:
																raise ValueError("""You need to specify either an `images` or `text` input to process."""  )

									if images is not None:
																lowercase_	:   Any  				=     self.image_processor(snake_case__,   *snake_case__,   **snake_case__  )
									if text is not None:
																lowercase_	:   Union[str, Any]  				=     self.tokenizer(snake_case__,   **snake_case__  )

									if text is None:
																return inputs
									elif images is None:
																return encodings
									else:
																lowercase_	:   Tuple  				=     encodings["""input_ids"""]
																return inputs






		def 						snake_case__				(							self,   *snake_case__,   **snake_case__  )       -> Any:






									"""simple docstring"""
									return self.tokenizer.batch_decode(*snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   *snake_case__,   **snake_case__  )       -> Any:






									"""simple docstring"""
									return self.tokenizer.decode(*snake_case__,   **snake_case__  )






		@contextmanager
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""  )
									lowercase_	:   str  				=     True
									lowercase_	:   int  				=     self.tokenizer
									yield
									lowercase_	:   Dict  				=     self.image_processor
									lowercase_	:   List[str]  				=     False






		def 						snake_case__				(							self,   snake_case__,   snake_case__=False,   snake_case__=None  )       -> Any:






									"""simple docstring"""
									if added_vocab is None:
																lowercase_	:   Tuple  				=     self.tokenizer.get_added_vocab()

									lowercase_	:   int  				=     {}

									while tokens:
																lowercase_	:   Optional[Any]  				=     re.search(r"""<s_(.*?)>""",   snake_case__,   re.IGNORECASE  )
																if start_token is None:
																							break
																lowercase_	:   Dict  				=     start_token.group(1  )
																lowercase_	:   Optional[int]  				=     re.search(rf"""</s_{key}>""",   snake_case__,   re.IGNORECASE  )
																lowercase_	:   Any  				=     start_token.group()
																if end_token is None:
																							lowercase_	:   Union[str, Any]  				=     tokens.replace(snake_case__,   """"""  )
																else:
																							lowercase_	:   Any  				=     end_token.group()
																							lowercase_	:   Dict  				=     re.escape(snake_case__  )
																							lowercase_	:   Tuple  				=     re.escape(snake_case__  )
																							lowercase_	:   Any  				=     re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""",   snake_case__,   re.IGNORECASE  )
																							if content is not None:
																														lowercase_	:   Optional[int]  				=     content.group(1  ).strip()
																														if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																					lowercase_	:   Any  				=     self.tokenajson(snake_case__,   is_inner_value=snake_case__,   added_vocab=snake_case__  )
																																					if value:
																																												if len(snake_case__  ) == 1:
																																																			lowercase_	:   Tuple  				=     value[0]
																																												lowercase_	:   Tuple  				=     value
																														else:  # leaf nodes
																																					lowercase_	:   List[Any]  				=     []
																																					for leaf in content.split(r"""<sep/>"""  ):
																																												lowercase_	:   Optional[Any]  				=     leaf.strip()
																																												if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																			lowercase_	:   str  				=     leaf[1:-2]  # for categorical special tokens
																																												output[key].append(snake_case__  )
																																					if len(output[key]  ) == 1:
																																												lowercase_	:   int  				=     output[key][0]

																							lowercase_	:   Union[str, Any]  				=     tokens[tokens.find(snake_case__  ) + len(snake_case__  ) :].strip()
																							if tokens[:6] == r"<sep/>":  # non-leaf nodes
																														return [output] + self.tokenajson(tokens[6:],   is_inner_value=snake_case__,   added_vocab=snake_case__  )

									if len(snake_case__  ):
																return [output] if is_inner_value else output
									else:
																return [] if is_inner_value else {"text_sequence": tokens}






		@property
		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""",   snake_case__,   )
									return self.image_processor_class






		@property
		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",   snake_case__,   )
									return self.image_processor






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

import torch_xla.distributed.xla_multiprocessing as xmp





def  __magic_name__  (				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     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  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     parse_args()

							# Import training_script as a module.
							lowercase_	:   List[Any]  				=     Path(args.training_script				)
							sys.path.append(str(script_fpath.parent.resolve()				)				)
							lowercase_	:   Tuple  				=     script_fpath.stem
							lowercase_	:   List[str]  				=     importlib.import_module(lowercase				)

							# Patch sys.argv
							lowercase_	:   Optional[int]  				=     [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()