




import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  _lowerCamelCase						(			__lowerCAmelCase							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					LxmertTokenizer
      lowerCAmelCase__			: Union[str, Any]    =					LxmertTokenizerFast
      lowerCAmelCase__			: List[str]    =					True
      lowerCAmelCase__			: Any    =					True







      def       snake_case	(     self					: Any				):
          super().setUp()

          __UpperCamelCase  		=						[
              """[UNK]""",
              """[CLS]""",
              """[SEP]""",
              """want""",
              """##want""",
              """##ed""",
              """wa""",
              """un""",
              """runn""",
              """##ing""",
              """,""",
              """low""",
              """lowest""",
          ]
          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''vocab_file''']				)
          with open(self.vocab_file ,       '''w''' ,       encoding='''utf-8'''				) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)







      def       snake_case	(     self					: str ,       snake_case					: List[str]				):
          __UpperCamelCase  		=						"""UNwant\u00E9d,running"""
          __UpperCamelCase  		=						"""unwanted, running"""
          return input_text, output_text







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.tokenizer_class(self.vocab_file				)

          __UpperCamelCase  		=						tokenizer.tokenize('''UNwant\u00E9d,running'''				)
          self.assertListEqual(_UpperCamelCase ,       ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase				) ,       [7, 4, 5, 10, 8, 9]				)







      def       snake_case	(     self					: Optional[int]				):


          if not self.test_rust_tokenizer:
              return

          __UpperCamelCase  		=						self.get_tokenizer()
          __UpperCamelCase  		=						self.get_rust_tokenizer()

          __UpperCamelCase  		=						"""I was born in 92000, and this is falsé."""

          __UpperCamelCase  		=						tokenizer.tokenize(_UpperCamelCase				)
          __UpperCamelCase  		=						rust_tokenizer.tokenize(_UpperCamelCase				)
          self.assertListEqual(_UpperCamelCase ,       _UpperCamelCase				)

          __UpperCamelCase  		=						tokenizer.encode(_UpperCamelCase ,       add_special_tokens=_UpperCamelCase				)
          __UpperCamelCase  		=						rust_tokenizer.encode(_UpperCamelCase ,       add_special_tokens=_UpperCamelCase				)
          self.assertListEqual(_UpperCamelCase ,       _UpperCamelCase				)

          __UpperCamelCase  		=						self.get_rust_tokenizer()
          __UpperCamelCase  		=						tokenizer.encode(_UpperCamelCase				)
          __UpperCamelCase  		=						rust_tokenizer.encode(_UpperCamelCase				)
          self.assertListEqual(_UpperCamelCase ,       _UpperCamelCase				)












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

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

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






