




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def    snake_case   (				*A     )				->				Optional[int]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				torch.cuda.device_count()
	print(f"Device count: {num_processes}"     )
	with patch_environment(
	    world_size=A		,  master_addr='127.0.01'		,  master_port='29500'		,  mixed_precision=args[1].mixed_precision     ):
		lowerCamelCase_				:						Optional[Any]				  =				PrepareForLaunch(A		,  distributed_type='MULTI_GPU'     )
		torch.multiprocessing.start_processes(A		,  args=A		,  nprocs=A		,  start_method='spawn'     )


if __name__ == "__main__":
							# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
							# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

							# on-demand GPU
							# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
							UpperCamelCase__						=		rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
							gpu.up_if_not()

							# on-prem GPU
							# gpu = rh.cluster(
							#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
							#       )

							# Set up remote function
							UpperCamelCase__						=		[
							    'pip:./',
							    'transformers',
							    'datasets',
							    'evaluate',
							    'tqdm',
							    'scipy',
							    'scikit-learn',
							    'tensorboard',
							    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
							]
							UpperCamelCase__						=		rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

							# Define train args/config, run train function
							UpperCamelCase__						=		argparse.Namespace(cpu=False, mixed_precision='fp16')
							UpperCamelCase__						=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							launch_train_gpu(config, train_args, stream_logs=True)

							# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
							# gpu.install_packages(reqs)
							# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






import copy

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


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt_decoder"""

					def __init__(							self					,			__lowercase=0					,			__lowercase=0   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Tuple				  =				vocab_size
						lowerCamelCase_				:						Union[str, Any]				  =				bos_token_id


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt"""
					_lowerCamelCase						    =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(							self					,			__lowercase=["en", "de"]					,			__lowercase=42_024					,			__lowercase=42_024					,			__lowercase="relu"					,			__lowercase=1_024					,			__lowercase=200					,			__lowercase=1_024					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=0.0					,			__lowercase=0.1					,			__lowercase=0.0					,			__lowercase=0.02					,			__lowercase=2					,			__lowercase=True					,			__lowercase=True					,			__lowercase=False					,			__lowercase=5					,			__lowercase=1.0					,			__lowercase=False					,			__lowercase=True					,			__lowercase=1					,			__lowercase=0					,			__lowercase=2					,			__lowercase=2					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				langs
						lowerCamelCase_				:						Union[str, Any]				  =				src_vocab_size
						lowerCamelCase_				:						Tuple				  =				tgt_vocab_size
						lowerCamelCase_				:						List[Any]				  =				d_model  # encoder_embed_dim and decoder_embed_dim

						lowerCamelCase_				:						str				  =				encoder_ffn_dim
						lowerCamelCase_				:						str				  =				encoder_layers
						lowerCamelCase_				:						Dict				  =				encoder_attention_heads
						lowerCamelCase_				:						Any				  =				encoder_layerdrop
						lowerCamelCase_				:						Union[str, Any]				  =				decoder_layerdrop
						lowerCamelCase_				:						Optional[int]				  =				decoder_ffn_dim
						lowerCamelCase_				:						List[str]				  =				decoder_layers
						lowerCamelCase_				:						int				  =				decoder_attention_heads
						lowerCamelCase_				:						List[str]				  =				max_position_embeddings
						lowerCamelCase_				:						int				  =				init_std  # Normal(0, this parameter)
						lowerCamelCase_				:						Optional[Any]				  =				activation_function

						lowerCamelCase_				:						Union[str, Any]				  =				DecoderConfig(vocab_size=__lowercase					,			bos_token_id=__lowercase   )
						if "decoder" in common_kwargs:
							del common_kwargs["decoder"]

						lowerCamelCase_				:						int				  =				scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						lowerCamelCase_				:						Optional[int]				  =				attention_dropout
						lowerCamelCase_				:						Union[str, Any]				  =				activation_dropout
						lowerCamelCase_				:						Dict				  =				dropout

						lowerCamelCase_				:						Optional[int]				  =				use_cache
						super().__init__(
						    pad_token_id=__lowercase					,			bos_token_id=__lowercase					,			eos_token_id=__lowercase					,			decoder_start_token_id=__lowercase					,			is_encoder_decoder=__lowercase					,			tie_word_embeddings=__lowercase					,			forced_eos_token_id=__lowercase					,			max_length=__lowercase					,			num_beams=__lowercase					,			length_penalty=__lowercase					,			early_stopping=__lowercase					,			**__lowercase					,			)





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				copy.deepcopy(self.__dict__   )
						lowerCamelCase_				:						Optional[int]				  =				self.decoder.to_dict()
						lowerCamelCase_				:						Dict				  =				self.__class__.model_type
						return output
