# 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 GroupViT model. """


from __future__ import annotations

import inspect
import os
import random
import tempfile
import unittest
from importlib import import_module

import numpy as np
import requests

from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tensorflow_probability,
    require_tf,
    require_vision,
    slow,
)
from transformers.utils import is_tf_available, is_vision_available

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


if is_tf_available():
    import tensorflow as tf

    from transformers import TFGroupViTModel, TFGroupViTTextModel, TFGroupViTVisionModel, TFSharedEmbeddings
    from transformers.models.groupvit.modeling_tf_groupvit import TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import CLIPProcessor


class TFGroupViTVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        depths=[6, 3, 3],
        num_group_tokens=[64, 8, 0],
        num_output_groups=[64, 8, 8],
        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.depths = depths
        self.num_hidden_layers = sum(depths)
        self.expected_num_hidden_layers = len(depths) + 1
        self.num_group_tokens = num_group_tokens
        self.num_output_groups = num_output_groups
        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

        num_patches = (image_size // patch_size) ** 2
        # no [CLS] token for GroupViT
        self.seq_length = num_patches

    def prepare_config_and_inputs(self):
        rng = random.Random(0)
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng)
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return GroupViTVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            depths=self.depths,
            num_group_tokens=self.num_group_tokens,
            num_output_groups=self.num_output_groups,
            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 = TFGroupViTVisionModel(config=config)
        result = model(pixel_values, training=False)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.num_output_groups[-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 TFGroupViTVisionModelTest(TFModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as GroupViT does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (TFGroupViTVisionModel,) 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 = TFGroupViTVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=GroupViTVisionConfig, has_text_modality=False, hidden_size=37
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="GroupViT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    """
    During saving, TensorFlow will also run with `training=True` which trigger `gumbel_softmax` that requires
    `tensorflow-probability`.
    """

    @require_tensorflow_probability
    @slow
    def test_saved_model_creation(self):
        super().test_saved_model_creation()

    @unittest.skip(reason="GroupViT does not use inputs_embeds")
    def test_graph_mode_with_inputs_embeds(self):
        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

        seq_len = getattr(self.model_tester, "seq_length", None)

        expected_num_attention_outputs = sum(g > 0 for g in self.model_tester.num_group_tokens)

        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
            # GroupViT returns attention grouping of each stage
            self.assertEqual(len(attentions), sum(g > 0 for g in self.model_tester.num_group_tokens))

            # 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
            # GroupViT returns attention grouping of each stage
            self.assertEqual(len(attentions), expected_num_attention_outputs)

            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

            # GroupViT returns attention grouping of each stage
            self.assertEqual(len(self_attentions), expected_num_attention_outputs)
            for i, self_attn in enumerate(self_attentions):
                if self_attn is None:
                    continue

                self.assertListEqual(
                    list(self_attentions[i].shape[-2:]),
                    [
                        self.model_tester.num_output_groups[i],
                        self.model_tester.num_output_groups[i - 1] if i > 0 else 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)

            seq_length = getattr(self.model_tester, "seq_length", None)

            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)

    @is_pt_tf_cross_test
    def test_pt_tf_model_equivalence(self):
        # `GroupViT` computes some indices using argmax, uses them as
        # one-hot encoding for further computation. The problem is
        # while PT/TF have very small difference in `y_soft` (~ 1e-9),
        # the argmax could be totally different, if there are at least
        # 2 indices with almost identical values. This leads to very
        # large difference in the outputs. We need specific seeds to
        # avoid almost identical values happening in `y_soft`.
        import torch

        seed = 338
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        tf.random.set_seed(seed)
        return super().test_pt_tf_model_equivalence()

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFGroupViTVisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @unittest.skip(
        "TFGroupViTVisionModel does not convert `hidden_states` and `attentions` to tensors as they are all of"
        " different dimensions, and we get `Got a non-Tensor value` error when saving the model."
    )
    @slow
    def test_saved_model_creation_extended(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        if hasattr(config, "use_cache"):
            config.use_cache = True

        seq_len = getattr(self.model_tester, "seq_length", None)

        for model_class in self.all_model_classes:
            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)
            num_out = len(model(class_inputs_dict))

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=True)
                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
                model = tf.keras.models.load_model(saved_model_dir)
                outputs = model(class_inputs_dict)
                output_hidden_states = outputs["hidden_states"]
                output_attentions = outputs["attentions"]

                # Check num outputs
                self.assertEqual(len(outputs), num_out)

                # Check num layers
                expected_num_layers = getattr(
                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
                )

                self.assertEqual(len(output_hidden_states), expected_num_layers)
                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)

                # Check attention outputs
                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

                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, seq_len, seq_len],
                )

                # Check hidden states
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]),
                    [seq_len, self.model_tester.hidden_size],
                )


class TFGroupViTTextModelTester:
    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):
        rng = random.Random(0)
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])
            # make sure the first token has attention mask `1` to ensure that, after combining the causal mask, there
            # is still at least one token being attended to for each batch.
            # TODO: Change `random_attention_mask` in PT/TF/Flax common test file, after a discussion with the team.
            input_mask = tf.concat(
                [tf.ones_like(input_mask[:, :1], dtype=input_mask.dtype), input_mask[:, 1:]], axis=-1
            )

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return GroupViTTextConfig(
            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 = TFGroupViTTextModel(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 TFGroupViTTextModelTest(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFGroupViTTextModel,) if is_tf_available() else ()
    test_pruning = False
    test_head_masking = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFGroupViTTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=GroupViTTextConfig, 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)

    @unittest.skip(reason="GroupViTTextModel does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFGroupViTTextModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @slow
    def test_saved_model_creation_extended(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        if hasattr(config, "use_cache"):
            config.use_cache = True

        for model_class in self.all_model_classes:
            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)
            num_out = len(model(class_inputs_dict))

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=True)
                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
                model = tf.keras.models.load_model(saved_model_dir)
                outputs = model(class_inputs_dict)
                output_hidden_states = outputs["hidden_states"]
                output_attentions = outputs["attentions"]

                # Check number of outputs
                self.assertEqual(len(outputs), num_out)

                # Check number of layers
                expected_num_layers = getattr(
                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
                )

                # Check hidden states
                self.assertEqual(len(output_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )

                # Check attention outputs
                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)

                seq_length = self.model_tester.seq_length
                key_length = getattr(self.model_tester, "key_length", seq_length)

                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, seq_length, key_length],
                )


class TFGroupViTModelTester:
    def __init__(self, parent, is_training=True):
        self.parent = parent
        self.text_model_tester = TFGroupViTTextModelTester(parent)
        self.vision_model_tester = TFGroupViTVisionModelTester(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 GroupViTConfig.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 = TFGroupViTModel(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 TFGroupViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (TFGroupViTModel,) if is_tf_available() else ()
    pipeline_model_mapping = {"feature-extraction": TFGroupViTModel} 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 = TFGroupViTModelTester(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)

    @unittest.skip(reason="hidden_states are tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="input_embeds are tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="CLIPModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @require_tensorflow_probability
    @slow
    def test_keras_fit(self):
        super().test_keras_fit()

    @is_pt_tf_cross_test
    def test_pt_tf_model_equivalence(self):
        # `GroupViT` computes some indices using argmax, uses them as
        # one-hot encoding for further computation. The problem is
        # while PT/TF have very small difference in `y_soft` (~ 1e-9),
        # the argmax could be totally different, if there are at least
        # 2 indices with almost identical values. This leads to very
        # large difference in the outputs. We need specific seeds to
        # avoid almost identical values happening in `y_soft`.
        import torch

        seed = 158
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        tf.random.set_seed(seed)
        return super().test_pt_tf_model_equivalence()

    # overwrite from common since `TFGroupViTModelTester` 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__ == "TFGroupViTModelTest":
            inputs_dict.pop("return_loss", None)

        tf_main_layer_classes = {
            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)

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFGroupViTModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
    @slow
    def test_saved_model_creation(self):
        pass

    @unittest.skip(reason="`saved_model` doesn't work with nested outputs so no preparation happens.")
    @slow
    def test_prepare_serving_output(self):
        pass


# 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 TFGroupViTModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "nvidia/groupvit-gcc-yfcc"
        model = TFGroupViTModel.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([[13.3523, 6.3629]])

        tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)
