



"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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


__snake_case							:   Dict      =    {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'codegen'
	SCREAMING_SNAKE_CASE          =	{
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}

	def __init__(     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Any=5_0400       ,			_SCREAMING_SNAKE_CASE:		str=2048       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=2048       ,			_SCREAMING_SNAKE_CASE:		List[str]=4096       ,			_SCREAMING_SNAKE_CASE:		int=28       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=16       ,			_SCREAMING_SNAKE_CASE:		int=64       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Optional[int]="gelu_new"       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.0       ,			_SCREAMING_SNAKE_CASE:		int=1e-5       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.02       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		List[str]=5_0256       ,			_SCREAMING_SNAKE_CASE:		int=5_0256       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			**_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       vocab_size
				__lowerCAmelCase      :  List[Any]												=       n_ctx
				__lowerCAmelCase      :  Union[str, Any]												=       n_positions
				__lowerCAmelCase      :  int												=       n_embd
				__lowerCAmelCase      :  Any												=       n_layer
				__lowerCAmelCase      :  Dict												=       n_head
				__lowerCAmelCase      :  List[Any]												=       n_inner
				__lowerCAmelCase      :  int												=       rotary_dim
				__lowerCAmelCase      :  List[str]												=       activation_function
				__lowerCAmelCase      :  str												=       resid_pdrop
				__lowerCAmelCase      :  List[Any]												=       embd_pdrop
				__lowerCAmelCase      :  Union[str, Any]												=       attn_pdrop
				__lowerCAmelCase      :  Tuple												=       layer_norm_epsilon
				__lowerCAmelCase      :  Union[str, Any]												=       initializer_range
				__lowerCAmelCase      :  Union[str, Any]												=       use_cache

				__lowerCAmelCase      :  Optional[Any]												=       bos_token_id
				__lowerCAmelCase      :  List[Any]												=       eos_token_id

				super().__init__(
				    bos_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE       ,			tie_word_embeddings=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)






class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def __init__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		PretrainedConfig       ,			_SCREAMING_SNAKE_CASE:		str = "default"       ,			_SCREAMING_SNAKE_CASE:		List[PatchingSpec] = None       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			)				->       Union[str, Any]:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE       ,			task=_SCREAMING_SNAKE_CASE       ,			patching_specs=_SCREAMING_SNAKE_CASE       ,			use_past=_SCREAMING_SNAKE_CASE)
				if not getattr(self._config       ,			"pad_token_id"       ,			_SCREAMING_SNAKE_CASE):
							# TODO: how to do that better?
							__lowerCAmelCase      :  Any												=       0

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
				if self.use_past:
							self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE       ,			direction="inputs")
							__lowerCAmelCase      :  Tuple												=       {0: "batch", 1: "past_sequence + sequence"}
				else:
							__lowerCAmelCase      :  Any												=       {0: "batch", 1: "sequence"}

				return common_inputs

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       int:

				"""simple docstring"""

				return self._config.n_layer

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       int:

				"""simple docstring"""

				return self._config.n_head

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		PreTrainedTokenizer       ,			_SCREAMING_SNAKE_CASE:		int = -1       ,			_SCREAMING_SNAKE_CASE:		int = -1       ,			_SCREAMING_SNAKE_CASE:		bool = False       ,			_SCREAMING_SNAKE_CASE:		Optional[TensorType] = None       ,			)				->       Mapping[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       super(_SCREAMING_SNAKE_CASE       ,			self).generate_dummy_inputs(
				    _SCREAMING_SNAKE_CASE       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			seq_length=_SCREAMING_SNAKE_CASE       ,			is_pair=_SCREAMING_SNAKE_CASE       ,			framework=_SCREAMING_SNAKE_CASE)

				# We need to order the input in the way they appears in the forward()
				__lowerCAmelCase      :  Union[str, Any]												=       OrderedDict({"input_ids": common_inputs["input_ids"]})

				# Need to add the past_keys
				if self.use_past:
							if not is_torch_available():
										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
							else:
										import torch

										__lowerCAmelCase						,	__lowerCAmelCase      :  Tuple												=       common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										__lowerCAmelCase      :  List[str]												=       seqlen + 2
										__lowerCAmelCase      :  str												=       (
										    batch,
										    self.num_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // self.num_attention_heads,
										)
										__lowerCAmelCase      :  Tuple												=       [
										    (torch.zeros(_SCREAMING_SNAKE_CASE), torch.zeros(_SCREAMING_SNAKE_CASE)) for _ in range(self.num_layers)
										]

				__lowerCAmelCase      :  Dict												=       common_inputs["attention_mask"]
				if self.use_past:
							__lowerCAmelCase      :  List[Any]												=       ordered_inputs["attention_mask"].dtype
							__lowerCAmelCase      :  List[Any]												=       torch.cat(
							    [ordered_inputs["attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			dtype=_SCREAMING_SNAKE_CASE)]       ,			dim=1)

				return ordered_inputs







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       int:

				"""simple docstring"""

				return 13




"""simple docstring"""






#
# 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 			_lowercase				(  *__snake_case	)						->   Optional[Any]:
			with open(__snake_case					,"r"	) as fh:
						fcntl.flock(__snake_case					,fcntl.LOCK_EX	)
						try:
									print(*__snake_case	)
						finally:
									fcntl.flock(__snake_case					,fcntl.LOCK_UN	)


__snake_case							:   List[Any]      =    int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__snake_case							:   List[str]      =    torch.device('cuda', local_rank)
__snake_case							:   Optional[Any]      =    socket.gethostname()

__snake_case							:   str      =    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
	__snake_case							:   Tuple      =    dist.get_rank()
	__snake_case							:   Any      =    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