





'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict







@pytest.mark.parametrize(
    "split_dict"					,  [
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"					,  num_bytes=1337					,  num_examples=42					,  dataset_name="my_dataset"     )}     ),
        SplitDict({"train": SplitInfo(name="train"					,  num_bytes=1337					,  num_examples=42     )}     ),
        SplitDict({"train": SplitInfo()}     ),
    ]					,  )
def    _lowerCamelCase  (__lowerCamelCase  :     SplitDict     )   ->    str:
   a__     = split_dict._to_yaml_list()
   assert len(__lowerCamelCase     ) == len(__lowerCamelCase     )
   a__     = SplitDict._from_yaml_list(__lowerCamelCase     )
   for split_name, split_info in split_dict.items():
      # dataset_name field is deprecated, and is therefore not part of the YAML dump
      a__     = None
      # the split name of split_dict takes over the name of the split info object
      a__     = split_name
   assert split_dict == reloaded







@pytest.mark.parametrize(
    "split_info"					,  [SplitInfo(), SplitInfo(dataset_name=__lowerCamelCase     ), SplitInfo(dataset_name="my_dataset"     )]     )
def    _lowerCamelCase  (__lowerCamelCase  :     Dict     )   ->    Optional[Any]:
   # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
   # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
   a__     = asdict(SplitDict({"train": split_info}     )     )
   assert "dataset_name" in split_dict_asdict["train"]
   assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name










'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

lowerCAmelCase_   :   str       =					ort.SessionOptions()
lowerCAmelCase_   :   Tuple       =					ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCAmelCase_   :   Any       =					["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCAmelCase_   :   str       =					ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCAmelCase_   :   Tuple       =					ort.RunOptions()

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   str       =					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...")
lowerCAmelCase_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[Any]       =					{}
for iter in range(max_iters):
		lowerCAmelCase_   :   List[str]       =					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) * 1000 / max_iters))



