

"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


a__ :	List[Any]						= logging.get_logger(__name__)

a__ :	Optional[Any]						= {
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}


class 			_snake_case     (						UpperCamelCase       ):
      __snake_case 			=     'plbart'
      __snake_case 			=     ['past_key_values']
      __snake_case 			=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__(self     ,							UpperCAmelCase_=5_0_0_0_5     ,							UpperCAmelCase_=1_0_2_4     ,							UpperCAmelCase_=6     ,							UpperCAmelCase_=3_0_7_2     ,							UpperCAmelCase_=1_2     ,							UpperCAmelCase_=6     ,							UpperCAmelCase_=3_0_7_2     ,							UpperCAmelCase_=1_2     ,							UpperCAmelCase_=0.0     ,							UpperCAmelCase_=0.0     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_="gelu"     ,							UpperCAmelCase_=7_6_8     ,							UpperCAmelCase_=0.1     ,							UpperCAmelCase_=0.1     ,							UpperCAmelCase_=0.0     ,							UpperCAmelCase_=0.0_2     ,							UpperCAmelCase_=0.0     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=1     ,							UpperCAmelCase_=0     ,							UpperCAmelCase_=2     ,							UpperCAmelCase_=2     ,							**UpperCAmelCase_     ,							)			->						List[Any]:
           __lowercase												=			vocab_size
           __lowercase												=			max_position_embeddings
           __lowercase												=			d_model
           __lowercase												=			encoder_ffn_dim
           __lowercase												=			encoder_layers
           __lowercase												=			encoder_attention_heads
           __lowercase												=			decoder_ffn_dim
           __lowercase												=			decoder_layers
           __lowercase												=			decoder_attention_heads
           __lowercase												=			dropout
           __lowercase												=			attention_dropout
           __lowercase												=			activation_dropout
           __lowercase												=			activation_function
           __lowercase												=			init_std
           __lowercase												=			encoder_layerdrop
           __lowercase												=			decoder_layerdrop
           __lowercase												=			classifier_dropout
           __lowercase												=			use_cache
           __lowercase												=			encoder_layers
           __lowercase												=			scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=UpperCAmelCase_     ,							bos_token_id=UpperCAmelCase_     ,							eos_token_id=UpperCAmelCase_     ,							is_encoder_decoder=UpperCAmelCase_     ,							forced_eos_token_id=UpperCAmelCase_     ,							**UpperCAmelCase_     ,							)



class 			_snake_case     (						UpperCamelCase       ):







      @property
      def        _UpperCamelCase    (self							)			->						Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
               ]							)







      @property
      def        _UpperCamelCase    (self							)			->						Mapping[str, Mapping[int, str]]:
           if self.use_past:
                return OrderedDict(
                    [
                        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
                        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                    ]							)
           else:
                return OrderedDict(
                    [
                        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                    ]							)





"""simple docstring"""

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


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



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

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			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:
										__lowercase												=			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(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


