




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase    :		str			   =       logging.get_logger(__name__)

lowerCamelCase    :		int			   =       {
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''xlm-roberta'''

 def __init__(    self :  Tuple						,							A_ :  Optional[Any]=30522						,							A_ :  str=768						,							A_ :  List[str]=12						,							A_ :  Any=12						,							A_ :  Tuple=3072						,							A_ :  Any="gelu"						,							A_ :  List[Any]=0.1						,							A_ :  Any=0.1						,							A_ :  Optional[int]=512						,							A_ :  Dict=2						,							A_ :  str=0.02						,							A_ :  Optional[int]=1E-12						,							A_ :  List[str]=1						,							A_ :  int=0						,							A_ :  str=2						,							A_ :  int="absolute"						,							A_ :  Optional[int]=True						,							A_ :  List[str]=None						,							**A_ :  List[str]						,							)       ->     Optional[Any]:

       """simple docstring"""






       super().__init__(pad_token_id=A_						,							bos_token_id=A_						,							eos_token_id=A_						,							**A_  )

       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   hidden_dropout_prob
       lowerCamelCase_							=   attention_probs_dropout_prob
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   type_vocab_size
       lowerCamelCase_							=   initializer_range
       lowerCamelCase_							=   layer_norm_eps
       lowerCamelCase_							=   position_embedding_type
       lowerCamelCase_							=   use_cache
       lowerCamelCase_							=   classifier_dropout







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 @property
 def     a__			(    self :  Optional[Any]  )       ->     Mapping[str, Mapping[int, str]]:

       """simple docstring"""






       if self.task == "multiple-choice":
             lowerCamelCase_							=   {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             lowerCamelCase_							=   {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]  )









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

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

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       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    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       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_000 / max_iters))



