

from typing import List, Optional

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'autoformer'
			__lowerCamelCase       				=				{
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			    'num_hidden_layers': 'encoder_layers',
			}


			def __init__(			self					,					lowercase = None					,					lowercase = None					,					lowercase = "student_t"					,					lowercase = "nll"					,					lowercase = 1					,					lowercase = [1, 2, 3, 4, 5, 6, 7]					,					lowercase = True					,					lowercase = 0					,					lowercase = 0					,					lowercase = 0					,					lowercase = 0					,					lowercase = None					,					lowercase = None					,					lowercase = 64					,					lowercase = 2					,					lowercase = 2					,					lowercase = 2					,					lowercase = 2					,					lowercase = 32					,					lowercase = 32					,					lowercase = "gelu"					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 100					,					lowercase = 0.02					,					lowercase = True					,					lowercase=True					,					lowercase = 10					,					lowercase = 25					,					lowercase = 3					,					**lowercase					,					)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	prediction_length
							A__						=	context_length if context_length is not None else prediction_length
							A__						=	distribution_output
							A__						=	loss
							A__						=	input_size
							A__						=	num_time_features
							A__						=	lags_sequence
							A__						=	scaling
							A__						=	num_dynamic_real_features
							A__						=	num_static_real_features
							A__						=	num_static_categorical_features
							if cardinality is not None and num_static_categorical_features > 0:
											if len(lowercase				) != num_static_categorical_features:
															raise ValueError(
															    "The cardinality should be a list of the same length as `num_static_categorical_features`"				)
											A__						=	cardinality
							else:
											A__						=	[0]
							if embedding_dimension is not None and num_static_categorical_features > 0:
											if len(lowercase				) != num_static_categorical_features:
															raise ValueError(
															    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"				)
											A__						=	embedding_dimension
							else:
											A__						=	[min(50					,					(cat + 1) // 2				) for cat in self.cardinality]
							A__						=	num_parallel_samples

							# Transformer architecture configuration
							A__						=	input_size * len(self.lags_sequence				) + self._number_of_features
							A__						=	d_model
							A__						=	encoder_attention_heads
							A__						=	decoder_attention_heads
							A__						=	encoder_ffn_dim
							A__						=	decoder_ffn_dim
							A__						=	encoder_layers
							A__						=	decoder_layers

							A__						=	dropout
							A__						=	attention_dropout
							A__						=	activation_dropout
							A__						=	encoder_layerdrop
							A__						=	decoder_layerdrop

							A__						=	activation_function
							A__						=	init_std

							A__						=	use_cache

							# Autoformer
							A__						=	label_length
							A__						=	moving_average
							A__						=	autocorrelation_factor

							super().__init__(is_encoder_decoder=lowercase					,					**lowercase				)


			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return (
							    sum(self.embedding_dimension				)
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def        a						(      *_UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		with open(_UpperCAmelCase ,   '''r'''						) as fh:
				fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_EX						)
				try:
						print(*_UpperCAmelCase						)
				finally:
						fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_UN						)


__A   			=int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__A   			=torch.device("cuda", local_rank)
__A   			=socket.gethostname()

__A   			=f'''[{hostname}-{local_rank}]'''

try:
				# test distributed
				dist.init_process_group("nccl")
				dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
				dist.barrier()

				# test cuda is available and can allocate memory
				torch.cuda.is_available()
				torch.ones(1).cuda(local_rank)

				# global rank
				__A   			=dist.get_rank()
				__A   			=dist.get_world_size()

				printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

				dist.barrier()
				if rank == 0:
								printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
				printflock(f'''{gpu} is broken''')
				raise





