



import os
import time

import numpy as np
import onnxruntime as ort


A_		:     List[Any]        =     '''1'''
A_		:     Tuple        =     '''0'''
A_		:     Optional[int]        =     '''1'''

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

A_		:     List[str]        =     1_28
A_		:     Tuple        =     1
A_		:     str        =     np.ones((batch, sequence), dtype=np.intaa)
A_		:     Optional[Any]        =     np.ones((batch, sequence), dtype=np.intaa)
A_		:     int        =     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...''')
A_		:     int        =     time.time()
A_		:     Dict        =     20_00
A_		:     Union[str, Any]        =     {}
for iter in range(max_iters):
     A_		:     Optional[int]        =     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) * 10_00 / max_iters))






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_		:     Optional[int]        =     {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:     List[Any]        =     [
         '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''InstructBlipQFormerModel''',
         '''InstructBlipPreTrainedModel''',
         '''InstructBlipForConditionalGeneration''',
         '''InstructBlipVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_instructblip import (
         INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         InstructBlipConfig,
         InstructBlipQFormerConfig,
         InstructBlipVisionConfig,
     )
     from .processing_instructblip import InstructBlipProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_instructblip import (
              INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              InstructBlipForConditionalGeneration,
              InstructBlipPreTrainedModel,
              InstructBlipQFormerModel,
              InstructBlipVisionModel,
          )

else:
     import sys

     A_		:     Tuple        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

