text: # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. 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.
  """ Testing suite for the TensorFlow CLIP model. """
  
  
  import inspect
  import os
  import tempfile
  import unittest
  from importlib import import_module
  
  import requests
  from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_vision, 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 numpy as np
      import tensorflow as tf
  
      from transformers import TFCLIPModel, TFCLIPTextModel, TFCLIPVisionModel, TFSharedEmbeddings
      from transformers.models.clip.modeling_tf_clip import TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import CLIPProcessor
  
  
  class TFCLIPVisionModelTester:
      def __init__(
          self,
          parent,
          batch_size=12,
          image_size=30,
          patch_size=2,
          num_channels=3,
          is_training=True,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          dropout=0.1,
          attention_dropout=0.1,
          initializer_range=0.02,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.image_size = image_size
          self.patch_size = patch_size
          self.num_channels = num_channels
          self.is_training = is_training
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.intermediate_size = intermediate_size
          self.dropout = dropout
          self.attention_dropout = attention_dropout
          self.initializer_range = initializer_range
          self.scope = scope
  
      def prepare_config_and_inputs(self):
          pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
          config = self.get_config()
  
          return config, pixel_values
  
      def get_config(self):
          return CLIPVisionConfig(
              image_size=self.image_size,
              patch_size=self.patch_size,
              num_channels=self.num_channels,
              hidden_size=self.hidden_size,
              num_hidden_layers=self.num_hidden_layers,
              num_attention_heads=self.num_attention_heads,
              intermediate_size=self.intermediate_size,
              dropout=self.dropout,
              attention_dropout=self.attention_dropout,
              initializer_range=self.initializer_range,
          )
  
      def create_and_check_model(self, config, pixel_values):
          model = TFCLIPVisionModel(config=config)
          result = model(pixel_values, training=False)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          image_size = (self.image_size, self.image_size)
          patch_size = (self.patch_size, self.patch_size)
          num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
          self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          config, pixel_values = config_and_inputs
          inputs_dict = {"pixel_values": pixel_values}
          return config, inputs_dict
  
  
  @require_tf
  class TFCLIPVisionModelTest(TFModelTesterMixin, unittest.TestCase):
      """
      Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds,
      attention_mask and seq_length.
      """
  
      all_model_classes = (TFCLIPVisionModel,) if is_tf_available() else ()
  
      test_pruning = False
      test_resize_embeddings = False
      test_head_masking = False
      test_onnx = False
  
      def setUp(self):
          self.model_tester = TFCLIPVisionModelTester(self)
          self.config_tester = ConfigTester(self, config_class=CLIPVisionConfig, has_text_modality=False, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_inputs_embeds(self):
          # CLIP does not use inputs_embeds
          pass
  
      def test_graph_mode_with_inputs_embeds(self):
          # CLIP does not use inputs_embeds
          pass
  
      def test_model_common_attributes(self):
          config, _ = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
              x = model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))
  
      def test_forward_signature(self):
          config, _ = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              signature = inspect.signature(model.call)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              arg_names = [*signature.parameters.keys()]
  
              expected_arg_names = ["pixel_values"]
              self.assertListEqual(arg_names[:1], expected_arg_names)
  
      def test_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      def test_attention_outputs(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.return_dict = True
  
          # in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
          image_size = (self.model_tester.image_size, self.model_tester.image_size)
          patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
          num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          seq_len = num_patches + 1
  
          for model_class in self.all_model_classes:
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = False
              config.return_dict = True
              model = model_class(config)
              outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
              attentions = outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              config.output_attentions = True
              model = model_class(config)
              outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
              attentions = outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              out_len = len(outputs)
  
              # Check attention is always last and order is fine
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = True
              model = model_class(config)
              outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
  
              added_hidden_states = 1
              self.assertEqual(out_len + added_hidden_states, len(outputs))
  
              self_attentions = outputs.attentions
  
              self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
  
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]),
                  [self.model_tester.num_attention_heads, seq_len, seq_len],
              )
  
      def test_hidden_states_output(self):
          def check_hidden_states_output(inputs_dict, config, model_class):
              model = model_class(config)
  
              outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
  
              hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
              expected_num_layers = getattr(
                  self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
              )
              self.assertEqual(len(hidden_states), expected_num_layers)
  
              # CLIP has a different seq_length
              image_size = (self.model_tester.image_size, self.model_tester.image_size)
              patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
              num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
              seq_length = num_patches + 1
  
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]),
                  [seq_length, self.model_tester.hidden_size],
              )
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              inputs_dict["output_hidden_states"] = True
              check_hidden_states_output(inputs_dict, config, model_class)
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              config.output_hidden_states = True
  
              check_hidden_states_output(inputs_dict, config, model_class)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = TFCLIPVisionModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  class TFCLIPTextModelTester:
      def __init__(
          self,
          parent,
          batch_size=12,
          seq_length=7,
          is_training=True,
          use_input_mask=True,
          use_labels=True,
          vocab_size=99,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          dropout=0.1,
          attention_dropout=0.1,
          max_position_embeddings=512,
          initializer_range=0.02,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.seq_length = seq_length
          self.is_training = is_training
          self.use_input_mask = use_input_mask
          self.use_labels = use_labels
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.intermediate_size = intermediate_size
          self.dropout = dropout
          self.attention_dropout = attention_dropout
          self.max_position_embeddings = max_position_embeddings
          self.initializer_range = initializer_range
          self.scope = scope
  
      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])
  
          config = self.get_config()
  
          return config, input_ids, input_mask
  
      def get_config(self):
          return CLIPTextConfig(
              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,
              dropout=self.dropout,
              attention_dropout=self.attention_dropout,
              max_position_embeddings=self.max_position_embeddings,
              initializer_range=self.initializer_range,
          )
  
      def create_and_check_model(self, config, input_ids, input_mask):
          model = TFCLIPTextModel(config=config)
          result = model(input_ids, attention_mask=input_mask, training=False)
          result = model(input_ids, training=False)
          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 prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          config, input_ids, input_mask = config_and_inputs
          inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  @require_tf
  class TFCLIPTextModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (TFCLIPTextModel,) if is_tf_available() else ()
      test_pruning = False
      test_head_masking = False
      test_onnx = False
  
      def setUp(self):
          self.model_tester = TFCLIPTextModelTester(self)
          self.config_tester = ConfigTester(self, config_class=CLIPTextConfig, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      def test_inputs_embeds(self):
          # CLIP does not use inputs_embeds
          pass
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = TFCLIPTextModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  class TFCLIPModelTester:
      def __init__(self, parent, is_training=True):
          self.parent = parent
          self.text_model_tester = TFCLIPTextModelTester(parent)
          self.vision_model_tester = TFCLIPVisionModelTester(parent)
          self.is_training = is_training
  
      def prepare_config_and_inputs(self):
          text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
          vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
  
          config = self.get_config()
  
          return config, input_ids, attention_mask, pixel_values
  
      def get_config(self):
          return CLIPConfig.from_text_vision_configs(
              self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
          )
  
      def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
          model = TFCLIPModel(config)
          result = model(input_ids, pixel_values, attention_mask, training=False)
          self.parent.assertEqual(
              result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
          )
          self.parent.assertEqual(
              result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
          )
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          config, input_ids, attention_mask, pixel_values = config_and_inputs
          inputs_dict = {
              "input_ids": input_ids,
              "attention_mask": attention_mask,
              "pixel_values": pixel_values,
              "return_loss": True,
          }
          return config, inputs_dict
  
  
  @require_tf
  class TFCLIPModelTest(TFModelTesterMixin, unittest.TestCase):
      all_model_classes = (TFCLIPModel,) if is_tf_available() else ()
      test_head_masking = False
      test_pruning = False
      test_resize_embeddings = False
      test_attention_outputs = False
      test_onnx = False
  
      def setUp(self):
          self.model_tester = TFCLIPModelTester(self)
  
      def test_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      # hidden_states are tested in individual model tests
      def test_hidden_states_output(self):
          pass
  
      # input_embeds are tested in individual model tests
      def test_inputs_embeds(self):
          pass
  
      # CLIPModel does not have input/output embeddings
      def test_model_common_attributes(self):
          pass
  
      # overwrite from common since `TFCLIPModelTester` set `return_loss` to `True` and causes the preparation of
      # `symbolic_inputs` failed.
      def test_keras_save_load(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          # remove `return_loss` to make code work
          if self.__class__.__name__ == "TFCLIPModelTest":
              inputs_dict.pop("return_loss", None)
  
          tf_main_layer_classes = set(
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__),)
              for module_member_name in dir(module)
              if module_member_name.endswith("MainLayer")
              # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
              and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
              for module_member in (getattr(module, module_member_name),)
              if isinstance(module_member, type)
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(module_member, "_keras_serializable", False)
          )
          for main_layer_class in tf_main_layer_classes:
              # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
              if "T5" in main_layer_class.__name__:
                  # Take the same values than in TFT5ModelTester for this shared layer
                  shared = TFSharedEmbeddings(99, 32, name="shared")
                  config.use_cache = inputs_dict.pop("use_cache", None)
                  main_layer = main_layer_class(config, embed_tokens=shared)
              else:
                  main_layer = main_layer_class(config)
  
              symbolic_inputs = {
                  name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
              }
  
              model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
              outputs = model(inputs_dict)
  
              with tempfile.TemporaryDirectory() as tmpdirname:
                  filepath = os.path.join(tmpdirname, "keras_model.h5")
                  model.save(filepath)
                  if "T5" in main_layer_class.__name__:
                      model = tf.keras.models.load_model(
                          filepath,
                          custom_objects={
                              main_layer_class.__name__: main_layer_class,
                              "TFSharedEmbeddings": TFSharedEmbeddings,
                          },
                      )
                  else:
                      model = tf.keras.models.load_model(
                          filepath, custom_objects={main_layer_class.__name__: main_layer_class}
                      )
                  assert isinstance(model, tf.keras.Model)
                  after_outputs = model(inputs_dict)
                  self.assert_outputs_same(after_outputs, outputs)
  
      # overwrite from common since CLIPModel/TFCLIPModel return CLIPOutput/TFCLIPOutput
      @is_pt_tf_cross_test
      def test_pt_tf_model_equivalence(self):
          import torch
  
          import transformers
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
              pt_model_class = getattr(transformers, pt_model_class_name)
  
              config.output_hidden_states = True
  
              tf_model = model_class(config)
              pt_model = pt_model_class(config)
  
              # Check we can load pt model in tf and vice-versa with model => model functions
  
              tf_model = transformers.load_pytorch_model_in_tf2_model(
                  tf_model, pt_model, tf_inputs=self._prepare_for_class(inputs_dict, model_class)
              )
              pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
  
              # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
              pt_model.eval()
              pt_inputs_dict = {}
              for name, key in self._prepare_for_class(inputs_dict, model_class).items():
                  if type(key) == bool:
                      pt_inputs_dict[name] = key
                  elif name == "input_values":
                      pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                  elif name == "pixel_values":
                      pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                  else:
                      pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
  
              # need to rename encoder-decoder "inputs" for PyTorch
              if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
                  pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
  
              with torch.no_grad():
                  pto = pt_model(**pt_inputs_dict)
              tfo = tf_model(self._prepare_for_class(inputs_dict, model_class), training=False)
  
              self.assertEqual(len(tfo), len(pto), "Output lengths differ between TF and PyTorch")
              for tf_output, pt_output in zip(tfo.to_tuple(), pto.to_tuple()):
  
                  if not (isinstance(tf_output, tf.Tensor) and isinstance(pt_output, torch.Tensor)):
                      continue
  
                  tf_out = tf_output.numpy()
                  pt_out = pt_output.numpy()
  
                  self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
  
                  if len(tf_out.shape) > 0:
  
                      tf_nans = np.copy(np.isnan(tf_out))
                      pt_nans = np.copy(np.isnan(pt_out))
  
                      pt_out[tf_nans] = 0
                      tf_out[tf_nans] = 0
                      pt_out[pt_nans] = 0
                      tf_out[pt_nans] = 0
  
                  max_diff = np.amax(np.abs(tf_out - pt_out))
                  self.assertLessEqual(max_diff, 4e-2)
  
              # Check we can load pt model in tf and vice-versa with checkpoint => model functions
              with tempfile.TemporaryDirectory() as tmpdirname:
                  pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
                  torch.save(pt_model.state_dict(), pt_checkpoint_path)
                  tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
  
                  tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
                  tf_model.save_weights(tf_checkpoint_path)
                  pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
  
              # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
              pt_model.eval()
              pt_inputs_dict = {}
              for name, key in self._prepare_for_class(inputs_dict, model_class).items():
                  if type(key) == bool:
                      key = np.array(key, dtype=bool)
                      pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
                  elif name == "input_values":
                      pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                  elif name == "pixel_values":
                      pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                  else:
                      pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
              # need to rename encoder-decoder "inputs" for PyTorch
              if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
                  pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
  
              with torch.no_grad():
                  pto = pt_model(**pt_inputs_dict)
              tfo = tf_model(self._prepare_for_class(inputs_dict, model_class))
  
              self.assertEqual(len(tfo), len(pto), "Output lengths differ between TF and PyTorch")
              for tf_output, pt_output in zip(tfo.to_tuple(), pto.to_tuple()):
  
                  if not (isinstance(tf_output, tf.Tensor) and isinstance(pt_output, torch.Tensor)):
                      continue
  
                  tf_out = tf_output.numpy()
                  pt_out = pt_output.numpy()
  
                  self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
  
                  if len(tf_out.shape) > 0:
                      tf_nans = np.copy(np.isnan(tf_out))
                      pt_nans = np.copy(np.isnan(pt_out))
  
                      pt_out[tf_nans] = 0
                      tf_out[tf_nans] = 0
                      pt_out[pt_nans] = 0
                      tf_out[pt_nans] = 0
  
                  max_diff = np.amax(np.abs(tf_out - pt_out))
                  self.assertLessEqual(max_diff, 4e-2)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = TFCLIPModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  # We will verify our results on an image of cute cats
  def prepare_img():
      url = "http://images.cocodataset.org/val2017/000000039769.jpg"
      im = Image.open(requests.get(url, stream=True).raw)
      return im
  
  
  @require_vision
  @require_tf
  class TFCLIPModelIntegrationTest(unittest.TestCase):
      @slow
      def test_inference(self):
          model_name = "openai/clip-vit-base-patch32"
          model = TFCLIPModel.from_pretrained(model_name)
          processor = CLIPProcessor.from_pretrained(model_name)
  
          image = prepare_img()
          inputs = processor(
              text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="tf"
          )
  
          outputs = model(**inputs, training=False)
  
          # verify the logits
          self.assertEqual(
              outputs.logits_per_image.shape,
              tf.TensorShape((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
          )
          self.assertEqual(
              outputs.logits_per_text.shape,
              tf.TensorShape((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
          )
  
          expected_logits = tf.constant([[24.5701, 19.3049]])
  
          tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)
  