





'''simple docstring'''



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   =      1_2_8
_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   =      2_0_0_0
_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) * 1_0_0_0 / max_iters))












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''MvpTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MvpForCausalLM''',
	    '''MvpForConditionalGeneration''',
	    '''MvpForQuestionAnswering''',
	    '''MvpForSequenceClassification''',
	    '''MvpModel''',
	    '''MvpPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





