




import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase__					:  int      							=				'1'
lowerCamelCase__					:  Optional[int]      							=				'0'
lowerCamelCase__					:  Optional[Any]      							=				'1'

lowerCamelCase__					:  int      							=				ort.SessionOptions()
lowerCamelCase__					:  List[Any]      							=				ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
lowerCamelCase__					:  List[str]      							=				['TensorrtExecutionProvider', 'CUDAExecutionProvider']
lowerCamelCase__					:  List[str]      							=				ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
lowerCamelCase__					:  Union[str, Any]      							=				ort.RunOptions()

lowerCamelCase__					:  int      							=				128
lowerCamelCase__					:  Dict      							=				1
lowerCamelCase__					:  Tuple      							=				np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase__					:  Union[str, Any]      							=				np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase__					:  Any      							=				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__					:  str      							=				time.time()
lowerCamelCase__					:  int      							=				2_000
lowerCamelCase__					:  Any      							=				{}
for iter in range(max_iters):
   lowerCamelCase__					:  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) * 1_000 / max_iters))





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__					:  Optional[int]      							=				{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Any      							=				['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   lowerCamelCase__					:  str      							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)