code: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		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=UpperCAmelCase__       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=UpperCAmelCase__       ,    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=UpperCAmelCase__      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[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()
style_context: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)