import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_								=							'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCAmelCase_								=							[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	SavedModel()
       _A=	[]

       with open(os.path.join(lowerCAmelCase_       ,					'utils'       ,					'tf_ops'       ,					'onnx.json'   )   ) as f:
              _A=	json.load(lowerCAmelCase_   )['opsets']

       for i in range(1       ,					opset + 1   ):
              onnx_ops.extend(onnx_opsets[str(lowerCAmelCase_   )]   )

       with open(lowerCAmelCase_       ,					'rb'   ) as f:
              saved_model.ParseFromString(f.read()   )

       _A=	set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node   )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def   )

    # Convert to list, sorted if you want
       _A=	sorted(lowerCAmelCase_   )
       _A=	[]

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(lowerCAmelCase_   )

       if strict and len(lowerCAmelCase_   ) > 0:
              raise Exception(F"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops   )
       elif len(lowerCAmelCase_   ) > 0:
              print(F"Found the following incompatible ops for the opset {opset}:"   )
              print(*lowerCAmelCase_       ,					sep='\n'   )
       else:
              print(F"The saved model {saved_model_path} can properly be converted with ONNX."   )


if __name__ == "__main__":
      UpperCAmelCase_								=							argparse.ArgumentParser()
      parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
      parser.add_argument(
          '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
      )
      parser.add_argument(
          '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
      )
      parser.add_argument(
          '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
      )
      UpperCAmelCase_								=							parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)
#
# 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 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






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


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

UpperCAmelCase_								=							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
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							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