code: # Copyright 2021 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 inspect
  import unittest
  
  import numpy as np
  
  from transformers import ViTConfig, is_flax_available
  from transformers.testing_utils import require_flax, slow
  
  from .test_configuration_common import ConfigTester
  from .test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
  
  
  if is_flax_available():
  
      import jax
      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
  
  
  class FlaxViTModelTester(unittest.TestCase):
      def __init__(
          self,
          parent,
          batch_size=13,
          image_size=30,
          patch_size=2,
          num_channels=3,
          is_training=True,
          use_labels=True,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          type_sequence_label_size=10,
          initializer_range=0.02,
      ):
          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.use_labels = use_labels
          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.hidden_act = hidden_act
          self.hidden_dropout_prob = hidden_dropout_prob
          self.attention_probs_dropout_prob = attention_probs_dropout_prob
          self.type_sequence_label_size = type_sequence_label_size
          self.initializer_range = initializer_range
  
      def prepare_config_and_inputs(self):
          pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
  
          config = ViTConfig(
              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,
              hidden_act=self.hidden_act,
              hidden_dropout_prob=self.hidden_dropout_prob,
              attention_probs_dropout_prob=self.attention_probs_dropout_prob,
              is_decoder=False,
              initializer_range=self.initializer_range,
          )
  
          return config, pixel_values
  
      def create_and_check_model(self, config, pixel_values, labels):
  
          model = FlaxViTModel(config=config)
          result = model(pixel_values)
          # 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))
  
      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_flax
  class FlaxViTModelTest(FlaxModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
  
      def setUp(self) -> None:
          self.model_tester = FlaxViTModelTester(self)
          self.config_tester = ConfigTester(self, config_class=ViTConfig, has_text_modality=False, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      # We need to override this test because in ViT, the seq_len equals the number of patches + 1
      # we compute that here
      def test_attention_outputs(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.return_dict = True
  
          num_patches = (config.image_size // config.patch_size) ** 2
          seq_length = num_patches + 1
  
          for model_class in self.all_model_classes:
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = False
              model = model_class(config)
              outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              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))
              attentions = outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              self.assertListEqual(
                  list(attentions[0].shape[-3:]),
                  [self.model_tester.num_attention_heads, seq_length, seq_length],
              )
              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))
  
              added_hidden_states = 1
              self.assertEqual(out_len + added_hidden_states, len(outputs))
  
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(attentions[0].shape[-3:]),
                  [self.model_tester.num_attention_heads, seq_length, seq_length],
              )
  
      # We neeed to override this test because ViT's forward signature is different than text models.
      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)
  
      # We neeed to override this test because ViT expects pixel_values instead of input_ids
      @slow
      def test_jit_compilation(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                  prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                  model = model_class(config)
  
                  @jax.jit
                  def model_jitted(pixel_values, **kwargs):
                      return model(pixel_values=pixel_values, **kwargs)
  
                  with self.subTest("JIT Enabled"):
                      jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
  
                  with self.subTest("JIT Disabled"):
                      with jax.disable_jit():
                          outputs = model_jitted(**prepared_inputs_dict).to_tuple()
  
                  self.assertEqual(len(outputs), len(jitted_outputs))
                  for jitted_output, output in zip(jitted_outputs, outputs):
                      self.assertEqual(jitted_output.shape, output.shape)
  
      # We need to override this test because in ViT, the seq_len equals the number of patches + 1
      # we compute that here
      def test_hidden_states_output(self):
          def check_hidden_states_output(inputs_dict, config, model_class):
              model = model_class(config)
              num_patches = (config.image_size // config.patch_size) ** 2
              seq_length = num_patches + 1  # we add 1 for the [CLS] token
  
              outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              hidden_states = outputs.hidden_states
  
              self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 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_class_name in self.all_model_classes:
              model = model_class_name.from_pretrained("google/vit-base-patch16-224")
              outputs = model(np.ones((1, 3, 224, 224)))
              self.assertIsNotNone(outputs)
  
apis: numpy.ones