






"""simple docstring"""







import json
import logging
import os
import subprocess
from argparse import ArgumentParser


lowerCAmelCase_		:			Optional[int]				   =					logging.getLogger(__name__)






def 					_lowerCAmelCase				(					):







       '''simple docstring'''
       UpperCAmelCase					=	ArgumentParser()
       UpperCAmelCase			,				UpperCAmelCase					=	parser.parse_known_args()
       for arg in unknown:
              if arg.startswith(("""-""", """--""")						):
                     parser.add_argument(arg.split("""="""						)[0]						)

       return parser.parse_args()






def 					_lowerCAmelCase				(					):







       '''simple docstring'''
       UpperCAmelCase					=	parse_args()
       UpperCAmelCase					=	8888
       UpperCAmelCase					=	int(os.environ["""SM_NUM_GPUS"""]						)
       UpperCAmelCase					=	json.loads(os.environ["""SM_HOSTS"""]						)
       UpperCAmelCase					=	len(lowerCAmelCase						)
       UpperCAmelCase					=	os.environ["""SM_CURRENT_HOST"""]
       UpperCAmelCase					=	hosts.index(lowerCAmelCase						)
       UpperCAmelCase					=	"""INFO"""

       if num_nodes > 1:
              UpperCAmelCase					=	F'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]						)}'''
       else:
              UpperCAmelCase					=	F'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]						)}'''
       try:
              subprocess.run(lowerCAmelCase		,					shell=lowerCAmelCase						)
       except Exception as e:
              logger.info(lowerCAmelCase						)


if __name__ == "__main__":
 main()






'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




