# coding=utf-8
# Copyright 2023 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 ALIGN model."""

import inspect
import os
import tempfile
import unittest

import requests

from transformers import AlignConfig, AlignProcessor, AlignTextConfig, AlignVisionConfig
from transformers.testing_utils import (
    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,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        AlignModel,
        AlignTextModel,
        AlignVisionModel,
    )


if is_vision_available():
    from PIL import Image


class AlignVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=32,
        num_channels=3,
        kernel_sizes=[3, 3, 5],
        in_channels=[32, 16, 24],
        out_channels=[16, 24, 30],
        hidden_dim=64,
        strides=[1, 1, 2],
        num_block_repeats=[1, 1, 2],
        expand_ratios=[1, 6, 6],
        is_training=True,
        hidden_act="gelu",
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.hidden_dim = hidden_dim
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.is_training = is_training
        self.hidden_act = hidden_act

    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 AlignVisionConfig(
            num_channels=self.num_channels,
            kernel_sizes=self.kernel_sizes,
            in_channels=self.in_channels,
            out_channels=self.out_channels,
            hidden_dim=self.hidden_dim,
            strides=self.strides,
            num_block_repeats=self.num_block_repeats,
            expand_ratios=self.expand_ratios,
            hidden_act=self.hidden_act,
        )

    def create_and_check_model(self, config, pixel_values):
        model = AlignVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)

        patch_size = self.image_size // 4
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, config.hidden_dim, patch_size, patch_size)
        )
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, config.hidden_dim))

    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 AlignVisionModelTest(ModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as ALIGN does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (AlignVisionModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

    def setUp(self):
        self.model_tester = AlignVisionModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=AlignVisionConfig,
            has_text_modality=False,
            hidden_size=37,
            common_properties=["num_channels", "image_size"],
        )

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

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

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

    @unittest.skip(reason="AlignVisionModel does not support input and output embeddings")
    def test_model_get_set_embeddings(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.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_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                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
            num_blocks = sum(config.num_block_repeats) * 4
            self.assertEqual(len(hidden_states), num_blocks)

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [self.model_tester.image_size // 2, self.model_tester.image_size // 2],
            )

        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)

    @unittest.skip
    def test_training(self):
        pass

    @unittest.skip
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "kakaobrain/align-base"
        model = AlignVisionModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class AlignTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        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_token_type_ids = use_token_type_ids
        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.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        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])

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = self.get_config()

        return config, input_ids, token_type_ids, input_mask

    def get_config(self):
        return AlignTextConfig(
            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,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            type_vocab_size=self.type_vocab_size,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, input_ids, token_type_ids, input_mask):
        model = AlignTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
            result = model(input_ids, token_type_ids=token_type_ids)
            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,
            token_type_ids,
            input_mask,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class AlignTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (AlignTextModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = AlignTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=AlignTextConfig, 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
    def test_training(self):
        pass

    @unittest.skip
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

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

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

    @unittest.skip(reason="AlignTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="AlignTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model_name = "kakaobrain/align-base"
        model = AlignTextModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


class AlignModelTester:
    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 = AlignTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = AlignVisionModelTester(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):
        test_config, input_ids, token_type_ids, input_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, token_type_ids, input_mask, pixel_values

    def get_config(self):
        return AlignConfig.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, token_type_ids, attention_mask, pixel_values):
        model = AlignModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values, attention_mask, token_type_ids)
        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, token_type_ids, input_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
            "pixel_values": pixel_values,
            "return_loss": True,
        }
        return config, inputs_dict


@require_torch
class AlignModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (AlignModel,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": AlignModel} if is_torch_available() else {}
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = AlignModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=AlignConfig,
            has_text_modality=False,
            common_properties=["projection_dim", "temperature_init_value"],
        )

    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()

    @unittest.skip(reason="Start to fail after using torch `cu118`.")
    def test_multi_gpu_data_parallel_forward(self):
        super().test_multi_gpu_data_parallel_forward()

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

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

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

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

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

    # override as the `temperature` parameter initilization is different for ALIGN
    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    # check if `temperature` is initilized as per the original implementation
                    if name == "temperature":
                        self.assertAlmostEqual(
                            param.data.item(),
                            1.0,
                            delta=1e-3,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    elif name == "text_projection.weight":
                        self.assertTrue(
                            -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            self.skipTest(reason="test_torchscript is set to False")

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()

            try:
                input_ids = inputs_dict["input_ids"]
                pixel_values = inputs_dict["pixel_values"]  # ALIGN needs pixel_values
                traced_model = torch.jit.trace(model, (input_ids, pixel_values))
            except RuntimeError:
                self.fail("Couldn't trace module.")

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")

                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")

            model.to(torch_device)
            model.eval()

            loaded_model.to(torch_device)
            loaded_model.eval()

            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()

            non_persistent_buffers = {}
            for key in loaded_model_state_dict.keys():
                if key not in model_state_dict.keys():
                    non_persistent_buffers[key] = loaded_model_state_dict[key]

            loaded_model_state_dict = {
                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
            }

            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

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

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

        # Save AlignConfig and check if we can load AlignTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = AlignTextConfig.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 = "kakaobrain/align-base"
        model = AlignModel.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_torch
class AlignModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "kakaobrain/align-base"
        model = AlignModel.from_pretrained(model_name).to(torch_device)
        processor = AlignProcessor.from_pretrained(model_name)

        image = prepare_img()
        texts = ["a photo of a cat", "a photo of a dog"]
        inputs = processor(images=image, text=texts, return_tensors="pt").to(torch_device)

        # 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([[9.7093, 3.4679]], device=torch_device)
        self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
