'''simple docstring'''


import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
 from transformers import (
     FlaxBertModel,
     FlaxCLIPVisionModel,
     FlaxVisionTextDualEncoderModel,
     FlaxViTModel,
     VisionTextDualEncoderConfig,
     VisionTextDualEncoderProcessor,
 )
 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )


if is_torch_available():
 import torch

 from transformers import VisionTextDualEncoderModel

if is_vision_available():
 from PIL import Image


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     if isinstance(lowerCamelCase_     ,					collections.abc.Iterable       ):
          return x
     return (x, x)






@require_flax
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				np.abs((a - b)						).max()
       self.assertLessEqual(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		F'''Difference between torch and flax is {diff} (>= {tol}).'''						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       List[Any]       =				FlaxVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Optional[Any]       =				model(input_ids=__SCREAMING_SNAKE_CASE				,		pixel_values=__SCREAMING_SNAKE_CASE				,		attention_mask=__SCREAMING_SNAKE_CASE						)

       self.assertEqual(output['text_embeds'].shape				,		(input_ids.shape[0], config.projection_dim)						)
       self.assertEqual(output['image_embeds'].shape				,		(pixel_values.shape[0], config.projection_dim)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       int       =				{'vision_model': vision_model, 'text_model': text_model}
       UpperCAmelCase_							:       str       =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Any       =				model(input_ids=__SCREAMING_SNAKE_CASE				,		pixel_values=__SCREAMING_SNAKE_CASE				,		attention_mask=__SCREAMING_SNAKE_CASE						)

       self.assertEqual(output['text_embeds'].shape				,		(input_ids.shape[0], model.config.projection_dim)						)
       self.assertEqual(output['image_embeds'].shape				,		(pixel_values.shape[0], model.config.projection_dim)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Optional[int]       =				{'vision_model': vision_model, 'text_model': text_model}
       UpperCAmelCase_							:       Optional[Any]       =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Optional[Any]       =				model(input_ids=__SCREAMING_SNAKE_CASE				,		pixel_values=__SCREAMING_SNAKE_CASE				,		attention_mask=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Optional[int]       =				output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(__SCREAMING_SNAKE_CASE						)
            UpperCAmelCase_							:       Dict       =				FlaxVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE						)

            UpperCAmelCase_							:       Dict       =				model(input_ids=__SCREAMING_SNAKE_CASE				,		pixel_values=__SCREAMING_SNAKE_CASE				,		attention_mask=__SCREAMING_SNAKE_CASE						)
            UpperCAmelCase_							:       Optional[Any]       =				after_output[0]
            UpperCAmelCase_							:       str       =				np.amax(np.abs(out_a - out_a						)						)
            self.assertLessEqual(__SCREAMING_SNAKE_CASE				,		1E-3						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Union[str, Any]       =				{'vision_model': vision_model, 'text_model': text_model}
       UpperCAmelCase_							:       str       =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       str       =				model(
           input_ids=__SCREAMING_SNAKE_CASE				,		pixel_values=__SCREAMING_SNAKE_CASE				,		attention_mask=__SCREAMING_SNAKE_CASE				,		output_attentions=__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Any       =				output.vision_model_output.attentions
       self.assertEqual(len(__SCREAMING_SNAKE_CASE						)				,		vision_config.num_hidden_layers						)

       # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCAmelCase_							:       int       =				to_atuple(vision_model.config.image_size						)
       UpperCAmelCase_							:       Dict       =				to_atuple(vision_model.config.patch_size						)
       UpperCAmelCase_							:       str       =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       UpperCAmelCase_							:       Optional[Any]       =				num_patches + 1
       self.assertEqual(vision_attentions[0].shape[-3:]				,		(vision_config.num_attention_heads, seq_len, seq_len)						)

       UpperCAmelCase_							:       Any       =				output.text_model_output.attentions
       self.assertEqual(len(__SCREAMING_SNAKE_CASE						)				,		text_config.num_hidden_layers						)

       self.assertEqual(
           text_attentions[0].shape[-3:]				,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])				,		)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       pt_model.to(__SCREAMING_SNAKE_CASE						)
       pt_model.eval()

       # prepare inputs
       UpperCAmelCase_							:       Any       =				inputs_dict
       UpperCAmelCase_							:       Any       =				{k: torch.tensor(v.tolist()						) for k, v in flax_inputs.items()}

       with torch.no_grad():
            UpperCAmelCase_							:       Union[str, Any]       =				pt_model(**__SCREAMING_SNAKE_CASE						).to_tuple()

       UpperCAmelCase_							:       int       =				fx_model(**__SCREAMING_SNAKE_CASE						).to_tuple()
       self.assertEqual(len(__SCREAMING_SNAKE_CASE						)				,		len(__SCREAMING_SNAKE_CASE						)				,		'Output lengths differ between Flax and PyTorch'						)
       for fx_output, pt_output in zip(fx_outputs[:4]				,		pt_outputs[:4]						):
            self.assert_almost_equals(__SCREAMING_SNAKE_CASE				,		pt_output.numpy()				,		4E-2						)

       # PT -> Flax
       with tempfile.TemporaryDirectory() as tmpdirname:
            pt_model.save_pretrained(__SCREAMING_SNAKE_CASE						)
            UpperCAmelCase_							:       Any       =				FlaxVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE				,		from_pt=__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Tuple       =				fx_model_loaded(**__SCREAMING_SNAKE_CASE						).to_tuple()
       self.assertEqual(len(__SCREAMING_SNAKE_CASE						)				,		len(__SCREAMING_SNAKE_CASE						)				,		'Output lengths differ between Flax and PyTorch'						)
       for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]				,		pt_outputs[:4]						):
            self.assert_almost_equals(__SCREAMING_SNAKE_CASE				,		pt_output.numpy()				,		4E-2						)

       # Flax -> PT
       with tempfile.TemporaryDirectory() as tmpdirname:
            fx_model.save_pretrained(__SCREAMING_SNAKE_CASE						)
            UpperCAmelCase_							:       Tuple       =				VisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE				,		from_flax=__SCREAMING_SNAKE_CASE						)

       pt_model_loaded.to(__SCREAMING_SNAKE_CASE						)
       pt_model_loaded.eval()

       with torch.no_grad():
            UpperCAmelCase_							:       Union[str, Any]       =				pt_model_loaded(**__SCREAMING_SNAKE_CASE						).to_tuple()

       self.assertEqual(len(__SCREAMING_SNAKE_CASE						)				,		len(__SCREAMING_SNAKE_CASE						)				,		'Output lengths differ between Flax and PyTorch'						)
       for fx_output, pt_output_loaded in zip(fx_outputs[:4]				,		pt_outputs_loaded[:4]						):
            self.assert_almost_equals(__SCREAMING_SNAKE_CASE				,		pt_output_loaded.numpy()				,		4E-2						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       int       =				VisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Union[str, Any]       =				FlaxVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Optional[Any]       =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,		__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       str       =				fx_state

       self.check_pt_flax_equivalence(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       Dict       =				VisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Optional[int]       =				FlaxVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       int       =				load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE				,		fx_model.params						)

       self.check_pt_flax_equivalence(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				self.prepare_config_and_inputs()
       self.check_save_load(**__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**__SCREAMING_SNAKE_CASE						)


  @is_pt_flax_cross_test
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.prepare_config_and_inputs()
       UpperCAmelCase_							:       Tuple       =				config_inputs_dict.pop('vision_config'						)
       UpperCAmelCase_							:       int       =				config_inputs_dict.pop('text_config'						)

       UpperCAmelCase_							:       Any       =				config_inputs_dict

       self.check_equivalence_pt_to_flax(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)
       self.check_equivalence_flax_to_pt(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				self.get_pretrained_model_and_inputs()

       UpperCAmelCase_							:       str       =				model_a(**__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       str       =				outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(__SCREAMING_SNAKE_CASE						)
            UpperCAmelCase_							:       List[Any]       =				FlaxVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE						)

            UpperCAmelCase_							:       int       =				model_a(**__SCREAMING_SNAKE_CASE						)
            UpperCAmelCase_							:       Dict       =				after_outputs[0]
            UpperCAmelCase_							:       Dict       =				np.amax(np.abs(out_a - out_a						)						)
            self.assertLessEqual(__SCREAMING_SNAKE_CASE				,		1E-5						)







@require_flax
class 					__SCREAMING_SNAKE_CASE						(	lowerCAmelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           'hf-internal-testing/tiny-random-vit'				,		'hf-internal-testing/tiny-bert'				,		vision_from_pt=__SCREAMING_SNAKE_CASE				,		text_from_pt=__SCREAMING_SNAKE_CASE				,		)
       UpperCAmelCase_							:       str       =				1_3
       UpperCAmelCase_							:       str       =				floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]						)
       UpperCAmelCase_							:       Optional[int]       =				ids_tensor([batch_size, 4]				,		model.config.text_config.vocab_size						)
       UpperCAmelCase_							:       Dict       =				random_attention_mask([batch_size, 4]						)
       UpperCAmelCase_							:       Optional[Any]       =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

       return model, inputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				FlaxViTModel(__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Optional[Any]       =				FlaxBertModel(__SCREAMING_SNAKE_CASE						)
       return vision_model, text_model






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				FlaxViTModelTester(self						)
       UpperCAmelCase_							:       int       =				FlaxBertModelTester(self						)
       UpperCAmelCase_							:       Optional[Any]       =				vit_model_tester.prepare_config_and_inputs()
       UpperCAmelCase_							:       Union[str, Any]       =				bert_model_tester.prepare_config_and_inputs()

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Union[str, Any]       =				vision_config_and_inputs

       UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCAmelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           'hf-internal-testing/tiny-random-clip'				,		'hf-internal-testing/tiny-bert'				,		vision_from_pt=__SCREAMING_SNAKE_CASE				,		text_from_pt=__SCREAMING_SNAKE_CASE				,		)
       UpperCAmelCase_							:       int       =				1_3
       UpperCAmelCase_							:       Any       =				floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]						)
       UpperCAmelCase_							:       int       =				ids_tensor([batch_size, 4]				,		model.config.text_config.vocab_size						)
       UpperCAmelCase_							:       int       =				random_attention_mask([batch_size, 4]						)
       UpperCAmelCase_							:       Optional[Any]       =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

       return model, inputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				FlaxCLIPVisionModel(__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Union[str, Any]       =				FlaxBertModel(__SCREAMING_SNAKE_CASE						)
       return vision_model, text_model






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				FlaxCLIPVisionModelTester(self						)
       UpperCAmelCase_							:       int       =				FlaxBertModelTester(self						)
       UpperCAmelCase_							:       Tuple       =				clip_model_tester.prepare_config_and_inputs()
       UpperCAmelCase_							:       Tuple       =				bert_model_tester.prepare_config_and_inputs()

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				vision_config_and_inputs

       UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }







@require_flax
@require_vision
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'				,		logit_scale_init_value=1.0						)
       UpperCAmelCase_							:       Union[str, Any]       =				VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'						)

       UpperCAmelCase_							:       str       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       UpperCAmelCase_							:       Dict       =				processor(
           text=['una foto di un gatto', 'una foto di un cane']				,		images=__SCREAMING_SNAKE_CASE				,		padding=__SCREAMING_SNAKE_CASE				,		return_tensors='np'						)

       UpperCAmelCase_							:       Optional[Any]       =				model(**__SCREAMING_SNAKE_CASE						)

       # verify the logits
       self.assertEqual(outputs.logits_per_image.shape				,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])						)
       self.assertEqual(
           outputs.logits_per_text.shape				,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])				,		)

       UpperCAmelCase_							:       Optional[Any]       =				np.array([[1.2_28_47_27, 0.3_10_41_22]]						)

       self.assertTrue(np.allclose(outputs.logits_per_image				,		__SCREAMING_SNAKE_CASE				,		atol=1E-3						)						)


'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

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

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, 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...''')
snake_case__					:      Union[str, Any]				      =  time.time()
snake_case__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 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) * 1000 / max_iters))






