
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_      							=       logging.get_logger(__name__)


def 				a__ (				snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Tuple =						torch.load(__snake_case    ,       map_location='''cpu'''				)
  if "model" in sd.keys():
    __SCREAMING_SNAKE_CASE	:     Any =						torch.load(__snake_case    ,       map_location='''cpu'''				)["model"]

  # pop unnecessary weights
  __SCREAMING_SNAKE_CASE	:     List[Any] =						[
      "decoder.version",
      "decoder.output_projection.weight",
  ]
  for key in keys_to_delete:
    if key in sd:
      sd.pop(__snake_case				)

  __SCREAMING_SNAKE_CASE	:     Dict =						{
      "decoder.project_in_dim.weight": "decoder.project_in.weight",
      "decoder.project_out_dim.weight": "decoder.project_out.weight",
      "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
      "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
  }
  for old_key, new_key in keys_to_rename.items():
    if old_key in sd:
      __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						sd.pop(__snake_case				)

  __SCREAMING_SNAKE_CASE	:     Tuple =						list(sd.keys()				)
  for key in keys:
    if ".qkv_proj." in key:
      __SCREAMING_SNAKE_CASE	:     str =						sd[key]
      # We split QKV in separate Q,K,V

      __SCREAMING_SNAKE_CASE	:     Any =						key.replace('''.qkv_proj.'''    ,       '''.q_proj.'''				)
      __SCREAMING_SNAKE_CASE	:     List[str] =						key.replace('''.qkv_proj.'''    ,       '''.k_proj.'''				)
      __SCREAMING_SNAKE_CASE	:     Optional[Any] =						key.replace('''.qkv_proj.'''    ,       '''.v_proj.'''				)

      __SCREAMING_SNAKE_CASE	:     Optional[Any] =						value.shape[0]
      assert depth % 3 == 0
      # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
      # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
      __SCREAMING_SNAKE_CASE	:     Optional[int] =						torch.split(__snake_case    ,       depth // 3    ,       dim=0				)

      __SCREAMING_SNAKE_CASE	:     Optional[Any] =						q
      __SCREAMING_SNAKE_CASE	:     Any =						k
      __SCREAMING_SNAKE_CASE	:     Optional[int] =						v
      del sd[key]

  return sd


@torch.no_grad()
def 				a__ (				snake_case    ,       snake_case    ,       snake_case=None				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Optional[Any] =						load_checkpoint(__snake_case				)

  if config is not None:
    __SCREAMING_SNAKE_CASE	:     str =						OPTConfig.from_pretrained(__snake_case				)
  else:
    __SCREAMING_SNAKE_CASE	:     str =						OPTConfig()

  __SCREAMING_SNAKE_CASE	:     int =						OPTModel(__snake_case				).half().eval()
  model.load_state_dict(__snake_case				)

  # Check results
  Path(__snake_case				).mkdir(exist_ok=__snake_case				)
  model.save_pretrained(__snake_case				)


if __name__ == "__main__":
   lowercase_      							=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--fairseq_path""",
       type=str,
       help=(
           """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
           """ https://huggingface.co/models?other=opt_metasq"""
       ),
   )
   parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
   lowercase_      							=       parser.parse_args()
   convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





"""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