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      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Any      =				["""image_processor""", """tokenizer"""]
    a__     :						Dict      =				"""BridgeTowerImageProcessor"""
    a__     :						Union[str, Any]      =				("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__(     self			,			__lowercase			,			__lowercase)	->     Any:
        super().__init__(__a			,			__a)







    def __call__(     self			,			__lowercase			,			__lowercase = None			,			__lowercase = True			,			__lowercase = False			,			__lowercase = None			,			__lowercase = None			,			__lowercase = 0			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = False			,			__lowercase = False			,			__lowercase = False			,			__lowercase = False			,			__lowercase = True			,			__lowercase = None			,			**__lowercase			,			)	->     BatchEncoding:
        __UpperCamelCase					:Optional[Any]        =		self.tokenizer(
            text=__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 + pixel_mask
        __UpperCamelCase					:Tuple        =		self.image_processor(
            __a			,			return_tensors=__a			,			do_normalize=__a			,			do_center_crop=__a			,			**__a)
        encoding.update(__a)

        return encoding







    def       UpperCamelCase__       (     self			,			*__lowercase			,			**__lowercase)	->     Optional[int]:
        return self.tokenizer.batch_decode(*__a			,			**__a)







    def       UpperCamelCase__       (     self			,			*__lowercase			,			**__lowercase)	->     Dict:
        return self.tokenizer.decode(*__a			,			**__a)







    @property
    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		self.tokenizer.model_input_names
        __UpperCamelCase					:Optional[Any]        =		self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






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'''







    __UpperCamelCase					: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=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_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=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[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()





