code: # coding=utf-8
  # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  
  import unittest
  
  from transformers import ElectraConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ..test_configuration_common import ConfigTester
  from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers.models.electra.modeling_tf_electra import (
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
      )
  
  
  class TFElectraModelTester:
      def __init__(
          self,
          parent,
      ):
          self.parent = parent
          self.batch_size = 13
          self.seq_length = 7
          self.is_training = True
          self.use_input_mask = True
          self.use_token_type_ids = True
          self.use_labels = True
          self.vocab_size = 99
          self.hidden_size = 32
          self.num_hidden_layers = 5
          self.num_attention_heads = 4
          self.intermediate_size = 37
          self.hidden_act = "gelu"
          self.hidden_dropout_prob = 0.1
          self.attention_probs_dropout_prob = 0.1
          self.max_position_embeddings = 512
          self.type_vocab_size = 16
          self.type_sequence_label_size = 2
          self.initializer_range = 0.02
          self.num_labels = 3
          self.num_choices = 4
          self.scope = None
          self.embedding_size = 128
  
      def prepare_config_and_inputs(self):
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          input_mask = None
          if self.use_input_mask:
              input_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          token_type_ids = None
          if self.use_token_type_ids:
              token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
  
          sequence_labels = None
          token_labels = None
          choice_labels = None
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
              token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
              choice_labels = ids_tensor([self.batch_size], self.num_choices)
  
          config = ElectraConfig(
              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,
          )
  
          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
      def prepare_config_and_inputs_for_decoder(self):
          (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = self.prepare_config_and_inputs()
  
          config.is_decoder = True
          encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )
  
      def create_and_check_model(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs)
  
          result = model(input_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_causal_lm_base_model(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.is_decoder = True
  
          model = TFElectraModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs)
  
          result = model(input_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_model_as_decoder(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      ):
          config.add_cross_attention = True
  
          model = TFElectraModel(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
              "encoder_hidden_states": encoder_hidden_states,
              "encoder_attention_mask": encoder_attention_mask,
          }
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
  
          # Also check the case where encoder outputs are not passed
          result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_causal_lm_base_model_past(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          config.is_decoder = True
  
          model = TFElectraModel(config=config)
  
          # first forward pass
          outputs = model(input_ids, use_cache=True)
          outputs_use_cache_conf = model(input_ids)
          outputs_no_past = model(input_ids, use_cache=False)
  
          self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
          self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
  
          past_key_values = outputs.past_key_values
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
          # append to next input_ids and attn_mask
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
  
          output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
          output_from_past = model(
              next_tokens, past_key_values=past_key_values, output_hidden_states=True
          ).hidden_states[0]
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
          output_from_past_slice = output_from_past[:, 0, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
  
      def create_and_check_causal_lm_base_model_past_with_attn_mask(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          config.is_decoder = True
  
          model = TFElectraModel(config=config)
  
          # create attention mask
          half_seq_length = self.seq_length // 2
          attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
          attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
          attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
  
          # first forward pass
          outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
          past_key_values = outputs.past_key_values
  
          # change a random masked slice from input_ids
          random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
          random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
          vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
          condition = tf.transpose(
              tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
          )
          input_ids = tf.where(condition, random_other_next_tokens, input_ids)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          attn_mask = tf.concat(
              [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
              axis=1,
          )
  
          output_from_no_past = model(
              next_input_ids,
              attention_mask=attn_mask,
              output_hidden_states=True,
          ).hidden_states[0]
          output_from_past = model(
              next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
          ).hidden_states[0]
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
          output_from_past_slice = output_from_past[:, 0, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
  
      def create_and_check_causal_lm_base_model_past_large_inputs(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          config.is_decoder = True
  
          model = TFElectraModel(config=config)
  
          input_ids = input_ids[:1, :]
          input_mask = input_mask[:1, :]
          self.batch_size = 1
  
          # first forward pass
          outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
          past_key_values = outputs.past_key_values
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_attn_mask = ids_tensor((self.batch_size, 3), 2)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
  
          output_from_no_past = model(
              next_input_ids,
              attention_mask=next_attention_mask,
              output_hidden_states=True,
          ).hidden_states[0]
          output_from_past = model(
              next_tokens,
              attention_mask=next_attention_mask,
              past_key_values=past_key_values,
              output_hidden_states=True,
          ).hidden_states[0]
  
          self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
          output_from_past_slice = output_from_past[:, :, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
  
      def create_and_check_decoder_model_past_large_inputs(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      ):
          config.add_cross_attention = True
  
          model = TFElectraModel(config=config)
  
          input_ids = input_ids[:1, :]
          input_mask = input_mask[:1, :]
          encoder_hidden_states = encoder_hidden_states[:1, :, :]
          encoder_attention_mask = encoder_attention_mask[:1, :]
          self.batch_size = 1
  
          # first forward pass
          outputs = model(
              input_ids,
              attention_mask=input_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              use_cache=True,
          )
          past_key_values = outputs.past_key_values
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_attn_mask = ids_tensor((self.batch_size, 3), 2)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
  
          output_from_no_past = model(
              next_input_ids,
              attention_mask=next_attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              output_hidden_states=True,
          ).hidden_states[0]
          output_from_past = model(
              next_tokens,
              attention_mask=next_attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              past_key_values=past_key_values,
              output_hidden_states=True,
          ).hidden_states[0]
  
          self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
          output_from_past_slice = output_from_past[:, :, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
  
      def create_and_check_for_masked_lm(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraForMaskedLM(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_for_pretraining(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraForPreTraining(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
  
      def create_and_check_for_sequence_classification(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = TFElectraForSequenceClassification(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def create_and_check_for_multiple_choice(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_choices = self.num_choices
          model = TFElectraForMultipleChoice(config=config)
          multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
          multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
          multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
          inputs = {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
  
      def create_and_check_for_question_answering(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraForQuestionAnswering(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          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 create_and_check_for_token_classification(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = TFElectraForTokenClassification(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = config_and_inputs
          inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  @require_tf
  class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              TFElectraModel,
              TFElectraForMaskedLM,
              TFElectraForPreTraining,
              TFElectraForTokenClassification,
              TFElectraForMultipleChoice,
              TFElectraForSequenceClassification,
              TFElectraForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      test_head_masking = False
      test_onnx = False
  
      def setUp(self):
          self.model_tester = TFElectraModelTester(self)
          self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_model(self):
          """Test the base model"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      def test_causal_lm_base_model(self):
          """Test the base model of the causal LM model
  
          is_deocder=True, no cross_attention, no encoder outputs
          """
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
  
      def test_model_as_decoder(self):
          """Test the base model as a decoder (of an encoder-decoder architecture)
  
          is_deocder=True + cross_attention + pass encoder outputs
          """
          config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
  
      def test_causal_lm_base_model_past(self):
          """Test causal LM base model with `past_key_values`"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_base_model_past(*config_and_inputs)
  
      def test_causal_lm_base_model_past_with_attn_mask(self):
          """Test the causal LM base model with `past_key_values` and `attention_mask`"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_base_model_past_with_attn_mask(*config_and_inputs)
  
      def test_causal_lm_base_model_past_with_large_inputs(self):
          """Test the causal LM base model with `past_key_values` and a longer decoder sequence length"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_base_model_past_large_inputs(*config_and_inputs)
  
      def test_decoder_model_past_with_large_inputs(self):
          """Similar to `test_causal_lm_base_model_past_with_large_inputs` but with cross-attention"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
  
      def test_for_masked_lm(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
  
      def test_for_pretraining(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
  
      def test_for_question_answering(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
  
      def test_for_sequence_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
  
      def test_for_multiple_choice(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
  
      def test_for_token_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          # for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["google/electra-small-discriminator"]:
              model = TFElectraModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  @require_tf
  class TFElectraModelIntegrationTest(unittest.TestCase):
      @slow
      def test_inference_masked_lm(self):
          model = TFElectraForPreTraining.from_pretrained("lysandre/tiny-electra-random")
          input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
          output = model(input_ids)[0]
  
          expected_shape = [1, 6]
          self.assertEqual(output.shape, expected_shape)
  
          print(output[:, :3])
  
          expected_slice = tf.constant([[-0.24651965, 0.8835437, 1.823782]])
          tf.debugging.assert_near(output[:, :3], expected_slice, atol=1e-4)
  
apis: tensorflow.concat tensorflow.constant tensorflow.range tensorflow.zeros tensorflow.ones tensorflow.expand_dims tensorflow.debugging.assert_near tensorflow.where
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