text: # coding=utf-8
  # Copyright 2022 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 ConvNext model. """
  
  import inspect
  import unittest
  from typing import List, Tuple
  
  from transformers import ConvNextConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ..test_configuration_common import ConfigTester
  from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers import TFConvNextForImageClassification, TFConvNextModel
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import ConvNextFeatureExtractor
  
  
  class TFConvNextModelTester:
      def __init__(
          self,
          parent,
          batch_size=13,
          image_size=32,
          num_channels=3,
          num_stages=4,
          hidden_sizes=[10, 20, 30, 40],
          depths=[2, 2, 3, 2],
          is_training=True,
          use_labels=True,
          intermediate_size=37,
          hidden_act="gelu",
          type_sequence_label_size=10,
          initializer_range=0.02,
          num_labels=3,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.image_size = image_size
          self.num_channels = num_channels
          self.num_stages = num_stages
          self.hidden_sizes = hidden_sizes
          self.depths = depths
          self.is_training = is_training
          self.use_labels = use_labels
          self.intermediate_size = intermediate_size
          self.hidden_act = hidden_act
          self.type_sequence_label_size = type_sequence_label_size
          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])
  
          labels = None
          if self.use_labels:
              labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
  
          config = self.get_config()
  
          return config, pixel_values, labels
  
      def get_config(self):
          return ConvNextConfig(
              num_channels=self.num_channels,
              hidden_sizes=self.hidden_sizes,
              depths=self.depths,
              num_stages=self.num_stages,
              hidden_act=self.hidden_act,
              is_decoder=False,
              initializer_range=self.initializer_range,
          )
  
      def create_and_check_model(self, config, pixel_values, labels):
          model = TFConvNextModel(config=config)
          result = model(pixel_values, training=False)
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape,
              (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
          )
  
      def create_and_check_for_image_classification(self, config, pixel_values, labels):
          config.num_labels = self.type_sequence_label_size
          model = TFConvNextForImageClassification(config)
          result = model(pixel_values, labels=labels, training=False)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          config, pixel_values, labels = config_and_inputs
          inputs_dict = {"pixel_values": pixel_values}
          return config, inputs_dict
  
  
  @require_tf
  class TFConvNextModelTest(TFModelTesterMixin, unittest.TestCase):
      """
      Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds,
      attention_mask and seq_length.
      """
  
      all_model_classes = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  
      test_pruning = False
      test_onnx = False
      test_resize_embeddings = False
      test_head_masking = False
  
      def setUp(self):
          self.model_tester = TFConvNextModelTester(self)
          self.config_tester = ConfigTester(
              self,
              config_class=ConvNextConfig,
              has_text_modality=False,
              hidden_size=37,
          )
  
      @unittest.skip(reason="ConvNext does not use inputs_embeds")
      def test_inputs_embeds(self):
          pass
  
      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
          reason="TF (<=2.8) does not support backprop for grouped convolutions on CPU.",
      )
      def test_keras_fit(self):
          pass
  
      @unittest.skip(reason="ConvNext does not support input and output embeddings")
      def test_model_common_attributes(self):
          pass
  
      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)
  
      @unittest.skip(reason="Model doesn't have attention layers")
      def test_attention_outputs(self):
          pass
  
      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))
              hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
              expected_num_stages = self.model_tester.num_stages
              self.assertEqual(len(hidden_states), expected_num_stages + 1)
  
              # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]),
                  [self.model_tester.image_size // 4, self.model_tester.image_size // 4],
              )
  
          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)
  
      # Since ConvNext does not have any attention we need to rewrite this test.
      def test_model_outputs_equivalence(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
              tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
              dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
  
              def recursive_check(tuple_object, dict_object):
                  if isinstance(tuple_object, (List, Tuple)):
                      for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                          recursive_check(tuple_iterable_value, dict_iterable_value)
                  elif tuple_object is None:
                      return
                  else:
                      self.assertTrue(
                          all(tf.equal(tuple_object, dict_object)),
                          msg=f"Tuple and dict output are not equal. Difference: {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}",
                      )
  
                  recursive_check(tuple_output, dict_output)
  
          for model_class in self.all_model_classes:
              model = model_class(config)
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class)
              check_equivalence(model, tuple_inputs, dict_inputs)
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              check_equivalence(model, tuple_inputs, dict_inputs)
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class)
              check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
  
              tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
              check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
  
      def test_for_image_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
          self.assertIsNotNone(model)
  
  
  # We will verify our results on an image of cute cats
  def prepare_img():
      image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
      return image
  
  
  @require_tf
  @require_vision
  class TFConvNextModelIntegrationTest(unittest.TestCase):
      @cached_property
      def default_feature_extractor(self):
          return (
              ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
          )
  
      @slow
      def test_inference_image_classification_head(self):
          model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
  
          feature_extractor = self.default_feature_extractor
          image = prepare_img()
          inputs = feature_extractor(images=image, return_tensors="tf")
  
          # forward pass
          outputs = model(**inputs)
  
          # verify the logits
          expected_shape = tf.TensorShape((1, 1000))
          self.assertEqual(outputs.logits.shape, expected_shape)
  
          expected_slice = tf.constant([-0.0260, -0.4739, 0.1911])
  
          tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)
  