import inspect
import tempfile
import unittest

import numpy as np

import transformers
from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from .test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.clip.modeling_flax_clip import FlaxCLIPModel, FlaxCLIPTextModel, FlaxCLIPVisionModel

if is_torch_available():
    import torch


class FlaxCLIPVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        num_hidden_layers=5,
        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.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.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])
        config = CLIPVisionConfig(
            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,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )

        return config, pixel_values

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

    all_model_classes = (FlaxCLIPVisionModel,) if is_flax_available() else ()

    def setUp(self):
        self.model_tester = FlaxCLIPVisionModelTester(self)

    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_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).to_tuple()

                with self.subTest("JIT Enabled"):
                    jitted_outputs = model_jitted(**prepared_inputs_dict)

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = model_jitted(**prepared_inputs_dict)

                self.assertEqual(len(outputs), len(jitted_outputs))
                for jitted_output, output in zip(jitted_outputs, outputs):
                    self.assertEqual(jitted_output.shape, output.shape)

    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.hidden_states

            self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)

            # CLIP has a different seq_length
            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_length = num_patches + 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)

    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        # in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
        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_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_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)

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

    # FlaxCLIPVisionModel does not have any base model
    def test_save_load_from_base(self):
        pass

    def test_save_load_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            model = model_class_name.from_pretrained("openai/clip-vit-base-patch32", from_pt=True)
            outputs = model(np.ones((1, 3, 224, 224)))
            self.assertIsNotNone(outputs)


class FlaxCLIPTextModelTester:
    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):
        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])

        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 = CLIPTextConfig(
            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,
        )

        return config, input_ids, input_mask

    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_flax
class FlaxCLIPTextModelTest(FlaxModelTesterMixin, unittest.TestCase):
    all_model_classes = (FlaxCLIPTextModel,) if is_flax_available() else ()

    def setUp(self):
        self.model_tester = FlaxCLIPTextModelTester(self)

    # FlaxCLIPTextModel does not have any base model
    def test_save_load_from_base(self):
        pass

    def test_save_load_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            model = model_class_name.from_pretrained("openai/clip-vit-base-patch32", from_pt=True)
            outputs = model(np.ones((1, 1)))
            self.assertIsNotNone(outputs)


class FlaxCLIPModelTester:
    def __init__(self, parent, is_training=True):
        self.parent = parent
        self.text_model_tester = FlaxCLIPTextModelTester(parent)
        self.vision_model_tester = FlaxCLIPVisionModelTester(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 = CLIPConfig.from_text_vision_configs(text_config, vision_config, projection_dim=64)

        return config, input_ids, attention_mask, pixel_values

    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 config, inputs_dict


@require_flax
class FlaxCLIPModelTest(FlaxModelTesterMixin, unittest.TestCase):
    all_model_classes = (FlaxCLIPModel,) if is_flax_available() else ()
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = FlaxCLIPModelTester(self)

    # hidden_states are tested in individual model tests
    def test_hidden_states_output(self):
        pass

    @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(input_ids, pixel_values, **kwargs):
                    return model(input_ids=input_ids, pixel_values=pixel_values, **kwargs).to_tuple()

                with self.subTest("JIT Enabled"):
                    jitted_outputs = model_jitted(**prepared_inputs_dict)

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = model_jitted(**prepared_inputs_dict)

                self.assertEqual(len(outputs), len(jitted_outputs))
                for jitted_output, output in zip(jitted_outputs[:4], outputs[:4]):
                    self.assertEqual(jitted_output.shape, output.shape)

    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 = ["input_ids", "pixel_values", "attention_mask", "position_ids"]
            self.assertListEqual(arg_names[:4], expected_arg_names)

    def test_get_image_features(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        model = FlaxCLIPModel(config)

        @jax.jit
        def model_jitted(pixel_values):
            return model.get_image_features(pixel_values=pixel_values)

        with self.subTest("JIT Enabled"):
            jitted_output = model_jitted(inputs_dict["pixel_values"])

        with self.subTest("JIT Disabled"):
            with jax.disable_jit():
                output = model_jitted(inputs_dict["pixel_values"])

        self.assertEqual(jitted_output.shape, output.shape)
        self.assertTrue(np.allclose(jitted_output, output, atol=1e-3))

    def test_get_text_features(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        model = FlaxCLIPModel(config)

        @jax.jit
        def model_jitted(input_ids, attention_mask, **kwargs):
            return model.get_text_features(input_ids=input_ids, attention_mask=attention_mask)

        with self.subTest("JIT Enabled"):
            jitted_output = model_jitted(**inputs_dict)

        with self.subTest("JIT Disabled"):
            with jax.disable_jit():
                output = model_jitted(**inputs_dict)

        self.assertEqual(jitted_output.shape, output.shape)
        self.assertTrue(np.allclose(jitted_output, output, atol=1e-3))

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            model = model_class_name.from_pretrained("openai/clip-vit-base-patch32", from_pt=True)
            outputs = model(input_ids=np.ones((1, 1)), pixel_values=np.ones((1, 3, 224, 224)))
            self.assertIsNotNone(outputs)

    # overwrite from common since FlaxCLIPModel returns nested output
    # which is not supported in the common test
    @is_pt_flax_cross_test
    def test_equivalence_pt_to_flax(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__):
                # prepare inputs
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                pt_model_class_name = model_class.__name__[4:]  # Skip the "Flax" at the beginning
                pt_model_class = getattr(transformers, pt_model_class_name)

                pt_model = pt_model_class(config).eval()
                fx_model = model_class(config, dtype=jnp.float32)

                fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
                fx_model.params = fx_state

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs).to_tuple()
                # PyTorch CLIPModel returns loss, we skip it here as we don't return loss in JAX/Flax models
                pt_outputs = pt_outputs[1:]

                fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
                self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
                for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    pt_model.save_pretrained(tmpdirname)
                    fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)

                fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple()
                self.assertEqual(
                    len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
                )
                for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
                    self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)

    # overwrite from common since FlaxCLIPModel returns nested output
    # which is not supported in the common test
    @is_pt_flax_cross_test
    def test_equivalence_flax_to_pt(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__):
                # prepare inputs
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                pt_model_class_name = model_class.__name__[4:]  # Skip the "Flax" at the beginning
                pt_model_class = getattr(transformers, pt_model_class_name)

                pt_model = pt_model_class(config).eval()
                fx_model = model_class(config, dtype=jnp.float32)

                pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs).to_tuple()
                # PyTorch CLIPModel returns loss, we skip it here as we don't return loss in JAX/Flax models
                pt_outputs = pt_outputs[1:]

                fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
                self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
                for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    fx_model.save_pretrained(tmpdirname)
                    pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)

                with torch.no_grad():
                    pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
                pt_outputs_loaded = pt_outputs_loaded[1:]

                self.assertEqual(
                    len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
                )
                for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)

    # overwrite from common since FlaxCLIPModel returns nested output
    # which is not supported in the common test
    def test_from_pretrained_save_pretrained(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            if model_class.__name__ != "FlaxBertModel":
                continue

            with self.subTest(model_class.__name__):
                model = model_class(config)

                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                outputs = model(**prepared_inputs_dict).to_tuple()

                # verify that normal save_pretrained works as expected
                with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(tmpdirname)
                    model_loaded = model_class.from_pretrained(tmpdirname)

                outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()[:4]
                for output_loaded, output in zip(outputs_loaded, outputs):
                    self.assert_almost_equals(output_loaded, output, 1e-3)

                # verify that save_pretrained for distributed training
                # with `params=params` works as expected
                with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(tmpdirname, params=model.params)
                    model_loaded = model_class.from_pretrained(tmpdirname)

                outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()[:4]
                for output_loaded, output in zip(outputs_loaded, outputs):
                    self.assert_almost_equals(output_loaded, output, 1e-3)
