

'''simple docstring'''
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 							A__    (       A__  ):
  A__		 =				['image_processor', 'tokenizer']
  A__		 =				'LayoutLMv2ImageProcessor'
  A__		 =				('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





  def __init__(		self      :	Tuple							,					_a      :	List[Any]=None							,					_a      :	Any=None							,					**_a      :	int )	->    str:





       '''simple docstring'''






       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'							,					_a							,					)
            _SCREAMING_SNAKE_CASE							    =kwargs.pop('feature_extractor' )

       _SCREAMING_SNAKE_CASE							    =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__(_a							,					_a )





  def __call__(		self      :	int							,					_a      :	List[str]							,					_a      :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,					_a      :	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,					_a      :	Union[List[List[int]], List[List[List[int]]]] = None							,					_a      :	Optional[Union[List[int], List[List[int]]]] = None							,					_a      :	bool = True							,					_a      :	Union[bool, str, PaddingStrategy] = False							,					_a      :	Union[bool, str, TruncationStrategy] = None							,					_a      :	Optional[int] = None							,					_a      :	int = 0							,					_a      :	Optional[int] = None							,					_a      :	Optional[bool] = None							,					_a      :	Optional[bool] = None							,					_a      :	bool = False							,					_a      :	bool = False							,					_a      :	bool = False							,					_a      :	bool = False							,					_a      :	bool = True							,					_a      :	Optional[Union[str, TensorType]] = None							,					**_a      :	List[str]							,					)	->    BatchEncoding:





       '''simple docstring'''






       if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                'You cannot provide bounding boxes '
                'if you initialized the image processor with apply_ocr set to True.' )

       if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )

       if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.' )

       # first, apply the image processor
       _SCREAMING_SNAKE_CASE							    =self.image_processor(images=_a							,					return_tensors=_a )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =[text]  # add batch dimension (as the image processor always adds a batch dimension)
            _SCREAMING_SNAKE_CASE							    =features['words']

       _SCREAMING_SNAKE_CASE							    =self.tokenizer(
           text=text if text is not None else features['words']							,					text_pair=text_pair if text_pair is not None else None							,					boxes=boxes if boxes is not None else features['boxes']							,					word_labels=_a							,					add_special_tokens=_a							,					padding=_a							,					truncation=_a							,					max_length=_a							,					stride=_a							,					pad_to_multiple_of=_a							,					return_token_type_ids=_a							,					return_attention_mask=_a							,					return_overflowing_tokens=_a							,					return_special_tokens_mask=_a							,					return_offsets_mapping=_a							,					return_length=_a							,					verbose=_a							,					return_tensors=_a							,					**_a							,					)

       # add pixel values
       _SCREAMING_SNAKE_CASE							    =features.pop('pixel_values' )
       if return_overflowing_tokens is True:
            _SCREAMING_SNAKE_CASE							    =self.get_overflowing_images(_a							,					encoded_inputs['overflow_to_sample_mapping'] )
       _SCREAMING_SNAKE_CASE							    =images

       return encoded_inputs





  def 						A  (		self      :	Any							,					_a      :	int							,					_a      :	Optional[Any] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx] )

       if len(_a ) != len(_a ):
            raise ValueError(
                'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                f" {len(_a )} and {len(_a )}" )

       return images_with_overflow





  def 						A  (		self      :	Dict							,					*_a      :	Any							,					**_a      :	Tuple )	->    int:





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							,					**_a )





  def 						A  (		self      :	List[Any]							,					*_a      :	Any							,					**_a      :	Optional[int] )	->    List[Any]:





       '''simple docstring'''






       return self.tokenizer.decode(*_a							,					**_a )





  @property
  def 						A  (		self      :	List[Any] )	->    Optional[Any]:





       '''simple docstring'''






       return ["input_ids", "bbox", "attention_mask", "image"]





  @property
  def 						A  (		self      :	int )	->    List[Any]:





       '''simple docstring'''






       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,					_a							,					)
       return self.image_processor_class





  @property
  def 						A  (		self      :	List[Any] )	->    Dict:





       '''simple docstring'''






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,					_a							,					)
       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     a__ (				)						->					Union[str, Any]:
			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=snake_case__       , default=1       , help="""Number of TPU cores to use (1 or 8)."""				)

			# positional
			parser.add_argument(
			    """training_script"""       , type=snake_case__       , 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=snake_case__				)

			return parser.parse_args()



def     a__ (				)						->					List[str]:
			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(snake_case__				)

			# 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()



