



"""simple docstring"""
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))







from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


