


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

import torch_xla.distributed.xla_multiprocessing as xmp







def 						__lowerCAmelCase		(		)			->		str:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					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 						__lowerCAmelCase		(		)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   List[str]         =					parse_args()

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

    # Patch sys.argv
    snake_case					:   List[Any]         =					[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()









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Optional[Any]													=						'''big_bird'''







   def __init__(			self		,			UpperCamelCase__=5_0358		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu_new"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=4096		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=True		,			UpperCamelCase__=0		,			UpperCamelCase__=1		,			UpperCamelCase__=2		,			UpperCamelCase__=66		,			UpperCamelCase__="block_sparse"		,			UpperCamelCase__=True		,			UpperCamelCase__=False		,			UpperCamelCase__=64		,			UpperCamelCase__=3		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Tuple:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			sep_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)

       snake_case					:   Union[str, Any]         =					vocab_size
       snake_case					:   List[Any]         =					max_position_embeddings
       snake_case					:   int         =					hidden_size
       snake_case					:   str         =					num_hidden_layers
       snake_case					:   Any         =					num_attention_heads
       snake_case					:   int         =					intermediate_size
       snake_case					:   Union[str, Any]         =					hidden_act
       snake_case					:   Optional[Any]         =					hidden_dropout_prob
       snake_case					:   List[Any]         =					attention_probs_dropout_prob
       snake_case					:   int         =					initializer_range
       snake_case					:   List[str]         =					type_vocab_size
       snake_case					:   Optional[Any]         =					layer_norm_eps
       snake_case					:   Optional[Any]         =					use_cache

       snake_case					:   List[Any]         =					rescale_embeddings
       snake_case					:   Any         =					attention_type
       snake_case					:   List[Any]         =					use_bias
       snake_case					:   int         =					block_size
       snake_case					:   int         =					num_random_blocks
       snake_case					:   Optional[int]         =					classifier_dropout







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   int         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Optional[Any]         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





