from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   'tvlt'




 def __init__(     self   :    int     ,       lowercase__   :    str=224     ,       lowercase__   :    Any=2_048     ,       lowercase__   :    Tuple=128     ,       lowercase__   :    Dict=[16, 16]     ,       lowercase__   :    Optional[Any]=[16, 16]     ,       lowercase__   :    List[Any]=3     ,       lowercase__   :    Union[str, Any]=1     ,       lowercase__   :    List[Any]=8     ,       lowercase__   :    Any=768     ,       lowercase__   :    List[Any]=12     ,       lowercase__   :    Tuple=12     ,       lowercase__   :    Tuple=3_072     ,       lowercase__   :    str="gelu"     ,       lowercase__   :    int=0.0     ,       lowercase__   :    Optional[int]=0.0     ,       lowercase__   :    Any=0.02     ,       lowercase__   :    Optional[int]=1e-6     ,       lowercase__   :    Optional[int]=True     ,       lowercase__   :    List[Any]=False     ,       lowercase__   :    int=16     ,       lowercase__   :    Any=512     ,       lowercase__   :    List[str]=8     ,       lowercase__   :    Any=2_048     ,       lowercase__   :    Optional[Any]=0.75     ,       lowercase__   :    List[Any]=0.15     ,       lowercase__   :    Tuple="frame-level"     ,       lowercase__   :    List[Any]=True     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Tuple="classification"     ,       **lowercase__   :    List[str]     ,       ):







        '''simple docstring'''



        super().__init__(**lowercase__)

        if audio_mask_type not in ("frame-level", "patch_level"):
               raise ValueError(
                   'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                   F"""got {audio_mask_type}""")

        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   spectrogram_length
        lowerCAmelCase__	      =   frequency_length
        lowerCAmelCase__	      =   image_patch_size
        lowerCAmelCase__	      =   audio_patch_size
        lowerCAmelCase__	      =   num_image_channels
        lowerCAmelCase__	      =   num_audio_channels
        lowerCAmelCase__	      =   num_frames

        lowerCAmelCase__	      =   hidden_size
        lowerCAmelCase__	      =   num_hidden_layers
        lowerCAmelCase__	      =   num_attention_heads
        lowerCAmelCase__	      =   intermediate_size
        lowerCAmelCase__	      =   hidden_act
        lowerCAmelCase__	      =   hidden_dropout_prob
        lowerCAmelCase__	      =   attention_probs_dropout_prob
        lowerCAmelCase__	      =   initializer_range
        lowerCAmelCase__	      =   layer_norm_eps
        lowerCAmelCase__	      =   qkv_bias
        lowerCAmelCase__	      =   use_mean_pooling

        lowerCAmelCase__	      =   decoder_num_attention_heads
        lowerCAmelCase__	      =   decoder_hidden_size
        lowerCAmelCase__	      =   decoder_num_hidden_layers
        lowerCAmelCase__	      =   decoder_intermediate_size
        lowerCAmelCase__	      =   pixel_mask_ratio
        lowerCAmelCase__	      =   audio_mask_ratio
        lowerCAmelCase__	      =   audio_mask_type

        lowerCAmelCase__	      =   task_matching
        lowerCAmelCase__	      =   task_mae
        lowerCAmelCase__	      =   loss_type

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

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       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=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

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

       return parser.parse_args()






def  __lowerCamelCase						(		):
       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(lowerCAmelCase__      )

       # 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()
