

'''simple docstring'''
import os
import time

import numpy as np
import onnxruntime as ort


_lowerCamelCase      					=     "1"
_lowerCamelCase      					=     "0"
_lowerCamelCase      					=     "1"

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

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







"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


