# 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 PyTorch GroupViT model."""

import inspect
import random
import tempfile
import unittest

import numpy as np
import requests

from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
from transformers.testing_utils import is_flaky, require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import GroupViTModel, GroupViTTextModel, GroupViTVisionModel


if is_vision_available():
    from PIL import Image

    from transformers import CLIPProcessor


class GroupViTVisionModelTester:
    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 = GroupViTVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        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_torch
class GroupViTVisionModelTest(ModelTesterMixin, 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 = (GroupViTVisionModel,) if is_torch_available() else ()

    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = GroupViTVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=GroupViTVisionConfig, has_text_modality=False, hidden_size=32
        )

    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

    @is_flaky(description="The `index` computed with `max()` in `hard_softmax` is not stable.")
    def test_batching_equivalence(self):
        super().test_batching_equivalence()

    def test_model_get_set_embeddings(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(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    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.forward)
            # 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._from_config(config, attn_implementation="eager")
            config = model.config
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            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)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            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)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            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_attn.shape[-2:]),
                    [
                        self.model_tester.num_output_groups[i],
                        self.model_tester.num_output_groups[i - 1] if i > 0 else seq_len,
                    ],
                )

    @unittest.skip(reason="This module does not support standalone training")
    def test_training(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        pass

    # override since the attention mask from GroupViT is not used to compute loss, thus no grad
    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = self.has_attentions

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)

        output = outputs[0]

        if config.is_encoder_decoder:
            # Seq2Seq models
            encoder_hidden_states = outputs.encoder_hidden_states[0]
            encoder_hidden_states.retain_grad()

            decoder_hidden_states = outputs.decoder_hidden_states[0]
            decoder_hidden_states.retain_grad()

            if self.has_attentions:
                encoder_attentions = outputs.encoder_attentions[0]
                encoder_attentions.retain_grad()

                decoder_attentions = outputs.decoder_attentions[0]
                decoder_attentions.retain_grad()

                cross_attentions = outputs.cross_attentions[0]
                cross_attentions.retain_grad()

            output.flatten()[0].backward(retain_graph=True)

            self.assertIsNotNone(encoder_hidden_states.grad)
            self.assertIsNotNone(decoder_hidden_states.grad)

            if self.has_attentions:
                self.assertIsNotNone(encoder_attentions.grad)
                self.assertIsNotNone(decoder_attentions.grad)
                self.assertIsNotNone(cross_attentions.grad)
        else:
            # Encoder-/Decoder-only models
            hidden_states = outputs.hidden_states[0]
            hidden_states.retain_grad()

            if self.has_attentions:
                attentions = outputs.attentions[0]
                attentions.retain_grad()

            output.flatten()[0].backward(retain_graph=True)

            self.assertIsNotNone(hidden_states.grad)

            if self.has_attentions:
                self.assertIsNone(attentions.grad)

    @slow
    def test_model_from_pretrained(self):
        model_name = "nvidia/groupvit-gcc-yfcc"
        model = GroupViTVisionModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class GroupViTTextModelTester:
    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=2,
        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])

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        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 = GroupViTTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, attention_mask=input_mask)
            result = model(input_ids)
        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_torch
class GroupViTTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (GroupViTTextModel,) if is_torch_available() else ()

    def setUp(self):
        self.model_tester = GroupViTTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=GroupViTTextConfig, hidden_size=32)

    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="This module does not support standalone training")
    def test_training(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="This module does not support standalone training")
    def test_training_gradient_checkpointing_use_reentrant_true(self):
        pass

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

    @slow
    def test_model_from_pretrained(self):
        model_name = "nvidia/groupvit-gcc-yfcc"
        model = GroupViTTextModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class GroupViTModelTester:
    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
        if text_kwargs is None:
            text_kwargs = {}
        if vision_kwargs is None:
            vision_kwargs = {}

        self.parent = parent
        self.text_model_tester = GroupViTTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = GroupViTVisionModelTester(parent, **vision_kwargs)
        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
        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(
            text_config=self.text_model_tester.get_config().to_dict(),
            vision_config=self.vision_model_tester.get_config().to_dict(),
            projection_dim=64,
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = GroupViTModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values, attention_mask)
        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_torch
class GroupViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (GroupViTModel,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": GroupViTModel} if is_torch_available() else {}

    test_resize_embeddings = False
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = GroupViTModelTester(self)
        common_properties = ["projection_dim", "projection_intermediate_dim", "logit_scale_init_value"]
        self.config_tester = ConfigTester(
            self, config_class=GroupViTConfig, has_text_modality=False, common_properties=common_properties
        )

    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_config(self):
        self.config_tester.run_common_tests()

    @is_flaky(description="The `index` computed with `max()` in `hard_softmax` is not stable.")
    def test_batching_equivalence(self):
        super().test_batching_equivalence()

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

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

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

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

    def test_load_vision_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save GroupViTConfig and check if we can load GroupViTVisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = GroupViTVisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        # Save GroupViTConfig and check if we can load GroupViTTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = GroupViTTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        model_name = "nvidia/groupvit-gcc-yfcc"
        model = GroupViTModel.from_pretrained(model_name)
        self.assertIsNotNone(model)

    def _image_features_get_expected_num_attentions(self, model_tester=None):
        if model_tester is None:
            model_tester = self.model_tester.vision_model_tester
        # GroupViT returns attention grouping of each stage
        return sum(g > 0 for g in self.model_tester.vision_model_tester.num_group_tokens)

    def _image_features_get_expected_num_hidden_states(self, model_tester=None):
        if model_tester is None:
            model_tester = self.model_tester.vision_model_tester
        return model_tester.expected_num_hidden_layers


# 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_torch
class GroupViTModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "nvidia/groupvit-gcc-yfcc"
        model = GroupViTModel.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="pt"
        )

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # verify the logits
        self.assertEqual(
            outputs.logits_per_image.shape,
            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
        )
        self.assertEqual(
            outputs.logits_per_text.shape,
            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
        )

        expected_logits = torch.tensor([[13.3523, 6.3629]])

        torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
