
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    '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 	UpperCamelCase      (			snake_case__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Optional[int]		    =							"""plbart"""
				UpperCAmelCase_							: Optional[int]		    =							["""past_key_values"""]
				UpperCAmelCase_							: List[Any]		    =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


				def __init__(							self						,      lowercase__=50005						,      lowercase__=1024						,      lowercase__=6						,      lowercase__=3072						,      lowercase__=12						,      lowercase__=6						,      lowercase__=3072						,      lowercase__=12						,      lowercase__=0.0						,      lowercase__=0.0						,      lowercase__=True						,      lowercase__=True						,      lowercase__="gelu"						,      lowercase__=768						,      lowercase__=0.1						,      lowercase__=0.1						,      lowercase__=0.0						,      lowercase__=0.02						,      lowercase__=0.0						,      lowercase__=True						,      lowercase__=1						,      lowercase__=0						,      lowercase__=2						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      vocab_size
									SCREAMING_SNAKE_CASE           =      max_position_embeddings
									SCREAMING_SNAKE_CASE           =      d_model
									SCREAMING_SNAKE_CASE           =      encoder_ffn_dim
									SCREAMING_SNAKE_CASE           =      encoder_layers
									SCREAMING_SNAKE_CASE           =      encoder_attention_heads
									SCREAMING_SNAKE_CASE           =      decoder_ffn_dim
									SCREAMING_SNAKE_CASE           =      decoder_layers
									SCREAMING_SNAKE_CASE           =      decoder_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      activation_dropout
									SCREAMING_SNAKE_CASE           =      activation_function
									SCREAMING_SNAKE_CASE           =      init_std
									SCREAMING_SNAKE_CASE           =      encoder_layerdrop
									SCREAMING_SNAKE_CASE           =      decoder_layerdrop
									SCREAMING_SNAKE_CASE           =      classifier_dropout
									SCREAMING_SNAKE_CASE           =      use_cache
									SCREAMING_SNAKE_CASE           =      encoder_layers
									SCREAMING_SNAKE_CASE           =      scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=_A						,      bos_token_id=_A						,      eos_token_id=_A						,      is_encoder_decoder=_A						,      forced_eos_token_id=_A						,      **_A						,      )






class 	UpperCamelCase      (			snake_case__ ):

				"""simple docstring"""



				@property
				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]     )




				@property
				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									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'}),
														    ]     )






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


UpperCamelCase__            =     logging.getLogger(__name__)







def       _a (		):
					__lowerCAmelCase							  =			ArgumentParser()
					__lowerCAmelCase  ,				__lowerCAmelCase							  =			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(("-", "--")	):
															parser.add_argument(arg.split("="	)[0]	)

					return parser.parse_args()







def       _a (		):
					__lowerCAmelCase							  =			parse_args()
					__lowerCAmelCase							  =			88_88
					__lowerCAmelCase							  =			int(os.environ["SM_NUM_GPUS"]	)
					__lowerCAmelCase							  =			json.loads(os.environ["SM_HOSTS"]	)
					__lowerCAmelCase							  =			len(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			os.environ["SM_CURRENT_HOST"]
					__lowerCAmelCase							  =			hosts.index(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			"INFO"

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


if __name__ == "__main__":
					main()




