import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


UpperCAmelCase_			    =							4
UpperCAmelCase_			    =							3




class  __magic_name__  (		__a    ):





   """simple docstring"""




   pass




def       lowerCAmelCase_     (		lowercase:							List[str]		)    ->				int:







 '''simple docstring'''
 for shard in shards:
  for i in range(lowercase		):
   yield {"i": i, "shard": shard}




def       lowerCAmelCase_     (		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     str							= int(os.environ['''RANK''']		)
 _UpperCamelCase:     str							= int(os.environ['''WORLD_SIZE''']		)

 _UpperCamelCase:     List[Any]							= ArgumentParser()
 parser.add_argument('''--streaming'''  ,					type=lowercase		)
 parser.add_argument('''--local_rank'''  ,					type=lowercase		)
 parser.add_argument('''--num_workers'''  ,					type=lowercase  ,					default=0		)
 _UpperCamelCase:     List[Any]							= parser.parse_args()
 _UpperCamelCase:     List[Any]							= args.streaming
 _UpperCamelCase:     List[str]							= args.num_workers

 _UpperCamelCase:     int							= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(lowercase		)]}
 _UpperCamelCase:     Union[str, Any]							= IterableDataset.from_generator(lowercase  ,					gen_kwargs=lowercase		)
 if not streaming:
  _UpperCamelCase:     Optional[Any]							= Dataset.from_list(list(lowercase		)		)

 _UpperCamelCase:     Dict							= split_dataset_by_node(lowercase  ,					rank=lowercase  ,					world_size=lowercase		)
 _UpperCamelCase:     Any							= torch.utils.data.DataLoader(lowercase  ,					num_workers=lowercase		)

 _UpperCamelCase:     Tuple							= NUM_SHARDS * NUM_ITEMS_PER_SHARD
 _UpperCamelCase:     Tuple							= full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)		)

 _UpperCamelCase:     Dict							= sum(1 for _ in dataloader		)
 if local_size != expected_local_size:
  raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""		)


if __name__ == "__main__":
  main()
import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase_			    =							'''1'''
UpperCAmelCase_			    =							'''0'''
UpperCAmelCase_			    =							'''1'''

UpperCAmelCase_			    =							ort.SessionOptions()
UpperCAmelCase_			    =							ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
UpperCAmelCase_			    =							['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
UpperCAmelCase_			    =							ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase_			    =							ort.RunOptions()

UpperCAmelCase_			    =							1_2_8
UpperCAmelCase_			    =							1
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
UpperCAmelCase_			    =							time.time()
UpperCAmelCase_			    =							2_0_0_0
UpperCAmelCase_			    =							{}
for iter in range(max_iters):
  UpperCAmelCase_			    =							sess.run(
      None,
      {
          sess.get_inputs()[0].name: input_ids,
          sess.get_inputs()[1].name: attention_mask,
          sess.get_inputs()[2].name: token_type_ids,
      },
      run_options=run_opt,
  )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1_0_0_0 / max_iters))