





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_28
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_           =	20_00
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) * 10_00 / max_iters))

"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_       :    Dict						      =			{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    int						      =			['''PLBartTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Optional[int]						      =			[
		    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PLBartForCausalLM''',
		    '''PLBartForConditionalGeneration''',
		    '''PLBartForSequenceClassification''',
		    '''PLBartModel''',
		    '''PLBartPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_plbart import PLBartTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_plbart import (
				    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PLBartForCausalLM,
				    PLBartForConditionalGeneration,
				    PLBartForSequenceClassification,
				    PLBartModel,
				    PLBartPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    Optional[int]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




