



import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE     				=						"1"
SCREAMING_SNAKE_CASE     				=						"0"
SCREAMING_SNAKE_CASE     				=						"1"

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

SCREAMING_SNAKE_CASE     				=						128
SCREAMING_SNAKE_CASE     				=						1
SCREAMING_SNAKE_CASE     				=						np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE     				=						np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE     				=						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...")
SCREAMING_SNAKE_CASE     				=						time.time()
SCREAMING_SNAKE_CASE     				=						2000
SCREAMING_SNAKE_CASE     				=						{}
for iter in range(max_iters):
			SCREAMING_SNAKE_CASE     				=						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))






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

