





'''simple docstring'''



import os
import time

import numpy as np
import onnxruntime as ort


__magic_name__   :						str    =			"""1"""
__magic_name__   :						str    =			"""0"""
__magic_name__   :						Tuple    =			"""1"""

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

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









'''simple docstring'''



from typing import TYPE_CHECKING

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


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						Union[str, Any]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


