import os
import time

import numpy as np
import onnxruntime as ort


__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			'1'
__SCREAMING_SNAKE_CASE						:   str         =			'0'
__SCREAMING_SNAKE_CASE						:   int         =			'1'

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

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			1_2_8
__SCREAMING_SNAKE_CASE						:   str         =			1
__SCREAMING_SNAKE_CASE						:   Dict         =			np.ones((batch, sequence), dtype=np.intaa)
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			np.ones((batch, sequence), dtype=np.intaa)
__SCREAMING_SNAKE_CASE						:   Dict         =			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						:   List[Any]         =			time.time()
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			2_0_0_0
__SCREAMING_SNAKE_CASE						:   List[str]         =			{}
for iter in range(max_iters):
				__SCREAMING_SNAKE_CASE						:   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_0_0_0 / max_iters))
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)