





"""simple docstring"""
import json
import logging
import os
import subprocess
from argparse import ArgumentParser


_snake_case	:						List[Any]					=					logging.getLogger(__name__)






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

							return parser.parse_args()






def    A__							(     ):
							A						=    parse_args()
							A						=    8_888
							A						=    int(os.environ["SM_NUM_GPUS"]			)
							A						=    json.loads(os.environ["SM_HOSTS"]			)
							A						=    len(UpperCamelCase			)
							A						=    os.environ["SM_CURRENT_HOST"]
							A						=    hosts.index(UpperCamelCase			)
							A						=    "INFO"

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


if __name__ == "__main__":
		main()









"""simple docstring"""
import copy

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


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


