
"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TFMobileBertForMaskedLM,
       TFMobileBertForMultipleChoice,
       TFMobileBertForNextSentencePrediction,
       TFMobileBertForPreTraining,
       TFMobileBertForQuestionAnswering,
       TFMobileBertForSequenceClassification,
       TFMobileBertForTokenClassification,
       TFMobileBertModel,
   )
@require_tf
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				List[str]				   =						(
          (
              TFMobileBertModel,
              TFMobileBertForMaskedLM,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE :				Tuple				   =						(
          {
              "feature-extraction": TFMobileBertModel,
              "fill-mask": TFMobileBertForMaskedLM,
              "question-answering": TFMobileBertForQuestionAnswering,
              "text-classification": TFMobileBertForSequenceClassification,
              "token-classification": TFMobileBertForTokenClassification,
              "zero-shot": TFMobileBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						False
      __SCREAMING_SNAKE_CASE :				str				   =						False




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=False				)    ->       Any:
          UpperCAmelCase          =       super()._prepare_for_class(lowercase_ ,							lowercase_ ,							return_labels=lowercase_				)

          if return_labels:
              if model_class in get_values(lowercase_				):
                  UpperCAmelCase          =       tf.zeros(self.model_tester.batch_size ,							dtype=tf.intaa				)

          return inputs_dict
      class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




            def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=9_9 ,							lowercase_=3_2 ,							lowercase_=3_2 ,							lowercase_=2 ,							lowercase_=4 ,							lowercase_=3_7 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=1_6 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_=None ,							)    ->       Any:
                UpperCAmelCase          =       parent
                UpperCAmelCase          =       batch_size
                UpperCAmelCase          =       seq_length
                UpperCAmelCase          =       is_training
                UpperCAmelCase          =       use_input_mask
                UpperCAmelCase          =       use_token_type_ids
                UpperCAmelCase          =       use_labels
                UpperCAmelCase          =       vocab_size
                UpperCAmelCase          =       hidden_size
                UpperCAmelCase          =       num_hidden_layers
                UpperCAmelCase          =       num_attention_heads
                UpperCAmelCase          =       intermediate_size
                UpperCAmelCase          =       hidden_act
                UpperCAmelCase          =       hidden_dropout_prob
                UpperCAmelCase          =       attention_probs_dropout_prob
                UpperCAmelCase          =       max_position_embeddings
                UpperCAmelCase          =       type_vocab_size
                UpperCAmelCase          =       type_sequence_label_size
                UpperCAmelCase          =       initializer_range
                UpperCAmelCase          =       num_labels
                UpperCAmelCase          =       num_choices
                UpperCAmelCase          =       scope
                UpperCAmelCase          =       embedding_size




            def      a_   (				self				)    ->       Optional[int]:
                UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size				)

                UpperCAmelCase          =       None
                if self.use_input_mask:
                    UpperCAmelCase          =       random_attention_mask([self.batch_size, self.seq_length]				)

                UpperCAmelCase          =       None
                if self.use_token_type_ids:
                    UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.type_vocab_size				)

                UpperCAmelCase          =       None
                UpperCAmelCase          =       None
                UpperCAmelCase          =       None
                if self.use_labels:
                    UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.type_sequence_label_size				)
                    UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels				)
                    UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.num_choices				)

                UpperCAmelCase          =       MobileBertConfig(
                    vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_size=self.intermediate_size ,							hidden_act=self.hidden_act ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							max_position_embeddings=self.max_position_embeddings ,							type_vocab_size=self.type_vocab_size ,							initializer_range=self.initializer_range ,							embedding_size=self.embedding_size ,							)

                return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Optional[int]:
                UpperCAmelCase          =       TFMobileBertModel(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)

                UpperCAmelCase          =       [input_ids, input_mask]
                UpperCAmelCase          =       model(lowercase_				)

                UpperCAmelCase          =       model(lowercase_				)

                self.parent.assertEqual(
                    result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)
                self.parent.assertEqual(result.pooler_output.shape ,							(self.batch_size, self.hidden_size)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Tuple:
                UpperCAmelCase          =       TFMobileBertForMaskedLM(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Optional[int]:
                UpperCAmelCase          =       TFMobileBertForNextSentencePrediction(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(result.logits.shape ,							(self.batch_size, 2)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       str:
                UpperCAmelCase          =       TFMobileBertForPreTraining(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(
                    result.prediction_logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)
                self.parent.assertEqual(result.seq_relationship_logits.shape ,							(self.batch_size, 2)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Optional[Any]:
                UpperCAmelCase          =       self.num_labels
                UpperCAmelCase          =       TFMobileBertForSequenceClassification(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Optional[Any]:
                UpperCAmelCase          =       self.num_choices
                UpperCAmelCase          =       TFMobileBertForMultipleChoice(config=lowercase_				)
                UpperCAmelCase          =       tf.tile(tf.expand_dims(lowercase_ ,							1				) ,							(1, self.num_choices, 1)				)
                UpperCAmelCase          =       tf.tile(tf.expand_dims(lowercase_ ,							1				) ,							(1, self.num_choices, 1)				)
                UpperCAmelCase          =       tf.tile(tf.expand_dims(lowercase_ ,							1				) ,							(1, self.num_choices, 1)				)
                UpperCAmelCase          =       {
                    'input_ids': multiple_choice_inputs_ids,
                    'attention_mask': multiple_choice_input_mask,
                    'token_type_ids': multiple_choice_token_type_ids,
                }
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
                UpperCAmelCase          =       self.num_labels
                UpperCAmelCase          =       TFMobileBertForTokenClassification(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)				)




            def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
                UpperCAmelCase          =       TFMobileBertForQuestionAnswering(config=lowercase_				)
                UpperCAmelCase          =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
                UpperCAmelCase          =       model(lowercase_				)
                self.parent.assertEqual(result.start_logits.shape ,							(self.batch_size, self.seq_length)				)
                self.parent.assertEqual(result.end_logits.shape ,							(self.batch_size, self.seq_length)				)




            def      a_   (				self				)    ->       List[Any]:
                UpperCAmelCase          =       self.prepare_config_and_inputs()
                (
                    (
                    UpperCAmelCase
                )      ,				(
                    UpperCAmelCase
                )      ,				(
                    UpperCAmelCase
                )      ,				(
                    UpperCAmelCase
                )      ,				(
                    UpperCAmelCase
                )      ,				(
                    UpperCAmelCase
                )      ,				(
                    UpperCAmelCase
                )      ,				
                )          =       config_and_inputs
                UpperCAmelCase          =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
                return config, inputs_dict




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       TFMobileBertModelTest.TFMobileBertModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_ ,							hidden_size=3_7				)




      def      a_   (				self				)    ->       Union[str, Any]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*lowercase_				)




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowercase_				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowercase_				)




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*lowercase_				)




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowercase_				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*lowercase_				)




      @slow
      def      a_   (				self				)    ->       Any:
          # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["google/mobilebert-uncased"]:
              UpperCAmelCase          =       TFMobileBertModel.from_pretrained(lowercase_				)
              self.assertIsNotNone(lowercase_				)

@require_tf
class   _UpperCAmelCase				(		unittest.TestCase				):




      @slow
      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'				)
          UpperCAmelCase          =       tf.constant([[0, 1, 2, 3, 4, 5]]				)
          UpperCAmelCase          =       model(lowercase_				)[0]

          UpperCAmelCase          =       [1, 6, 3_0_5_2_2]
          self.assertEqual(output.shape ,							lowercase_				)

          UpperCAmelCase          =       tf.constant(
              [
                  [
                      [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
                      [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
                      [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
                  ]
              ]				)
          tf.debugging.assert_near(output[:, :3, :3] ,							lowercase_ ,							atol=1E-4				)





"""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_            =    128
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_            =    2000
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) * 1000 / max_iters))



