# coding=utf-8
# Copyright 2024 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 Aria model."""

import gc
import unittest

import requests

from transformers import (
    AriaConfig,
    AriaForConditionalGeneration,
    AriaTextConfig,
    AutoProcessor,
    AutoTokenizer,
    is_torch_available,
    is_vision_available,
)
from transformers.models.idefics3 import Idefics3VisionConfig
from transformers.testing_utils import (
    require_bitsandbytes,
    require_torch,
    require_vision,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image


class AriaVisionText2TextModelTester:
    def __init__(
        self,
        parent,
        ignore_index=-100,
        image_token_index=9,
        projector_hidden_act="gelu",
        seq_length=7,
        vision_feature_select_strategy="default",
        vision_feature_layer=-1,
        text_config=AriaTextConfig(
            seq_length=7,
            is_training=True,
            use_input_mask=True,
            use_token_type_ids=False,
            use_labels=True,
            hidden_act="gelu",
            hidden_dropout_prob=0.1,
            attention_probs_dropout_prob=0.1,
            type_vocab_size=16,
            type_sequence_label_size=2,
            initializer_range=0.02,
            num_labels=3,
            num_choices=4,
            pad_token_id=1,
            hidden_size=32,
            intermediate_size=64,
            max_position_embeddings=60,
            model_type="aria_moe_lm",
            moe_intermediate_size=4,
            moe_num_experts=4,
            moe_topk=2,
            num_attention_heads=8,
            num_experts_per_tok=3,
            num_hidden_layers=2,
            num_key_value_heads=8,
            rope_theta=5000000,
            vocab_size=99,
            eos_token_id=2,
            head_dim=4,
        ),
        is_training=True,
        vision_config=Idefics3VisionConfig(
            image_size=358,
            patch_size=10,
            num_channels=3,
            is_training=True,
            hidden_size=32,
            projection_dim=20,
            num_hidden_layers=2,
            num_attention_heads=16,
            intermediate_size=10,
            dropout=0.1,
            attention_dropout=0.1,
            initializer_range=0.02,
        ),
    ):
        self.parent = parent
        self.ignore_index = ignore_index
        self.image_token_index = image_token_index
        self.projector_hidden_act = projector_hidden_act
        self.vision_feature_select_strategy = vision_feature_select_strategy
        self.vision_feature_layer = vision_feature_layer
        self.text_config = text_config
        self.vision_config = vision_config
        self.pad_token_id = text_config.pad_token_id
        self.eos_token_id = text_config.eos_token_id
        self.num_hidden_layers = text_config.num_hidden_layers
        self.vocab_size = text_config.vocab_size
        self.hidden_size = text_config.hidden_size
        self.num_attention_heads = text_config.num_attention_heads
        self.is_training = is_training

        self.batch_size = 10
        self.num_channels = 3
        self.image_size = 358
        self.num_image_tokens = 128
        self.seq_length = seq_length + self.num_image_tokens

    def get_config(self):
        return AriaConfig(
            text_config=self.text_config,
            vision_config=self.vision_config,
            ignore_index=self.ignore_index,
            image_token_index=self.image_token_index,
            projector_hidden_act=self.projector_hidden_act,
            vision_feature_select_strategy=self.vision_feature_select_strategy,
            vision_feature_layer=self.vision_feature_layer,
            eos_token_id=self.eos_token_id,
        )

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor(
            [
                self.batch_size,
                self.vision_config.num_channels,
                self.vision_config.image_size,
                self.vision_config.image_size,
            ]
        )
        config = self.get_config()

        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
        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
        attention_mask = input_ids.ne(1).to(torch_device)
        input_ids[input_ids == config.image_token_index] = self.pad_token_id
        input_ids[:, : self.num_image_tokens] = config.image_token_index
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict

    def create_and_check_aria_model_fp16_forward(self, config, input_ids, pixel_values, attention_mask):
        model = AriaForConditionalGeneration(config=config)
        model.to(torch_device)
        model.eval()
        with torch.autocast(device_type="cuda", dtype=torch.float16):
            logits = model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                pixel_values=pixel_values.to(torch.bfloat16),
                return_dict=True,
            )["logits"]
        self.parent.assertFalse(torch.isnan(logits).any().item())


@require_torch
class AriaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    """
    Model tester for `AriaForConditionalGeneration`.
    """

    all_model_classes = (AriaForConditionalGeneration,) if is_torch_available() else ()
    test_pruning = False
    test_head_masking = False
    _is_composite = True

    def setUp(self):
        self.model_tester = AriaVisionText2TextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=AriaConfig, has_text_modality=False)

    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = self._prepare_for_class(inputs_dict, model_class)

            input_ids = inputs["input_ids"]
            del inputs["input_ids"]
            del inputs["pixel_values"]

            wte = model.get_input_embeddings()
            inputs["inputs_embeds"] = wte(input_ids)

            with torch.no_grad():
                model(**inputs)

    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
    # while some other models require pixel_values to be present
    def test_inputs_embeds_matches_input_ids(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = self._prepare_for_class(inputs_dict, model_class)
            input_ids = inputs["input_ids"]
            del inputs["input_ids"]
            del inputs["pixel_values"]

            inputs_embeds = model.get_input_embeddings()(input_ids)

            with torch.no_grad():
                out_ids = model(input_ids=input_ids, **inputs)[0]
                out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
            torch.testing.assert_close(out_embeds, out_ids)

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

    @unittest.skip(
        reason="This architecture 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 architecture 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="Compile not yet supported because in LLava models")
    def test_sdpa_can_compile_dynamic(self):
        pass

    @unittest.skip(reason="Compile not yet supported because in LLava models")
    def test_sdpa_can_dispatch_on_flash(self):
        pass

    @unittest.skip(reason="Feedforward chunking is not yet supported")
    def test_feed_forward_chunking(self):
        pass

    @unittest.skip(reason="Unstable test")
    def test_initialization(self):
        pass

    @unittest.skip(reason="Unstable test")
    def test_dola_decoding_sample(self):
        pass

    @unittest.skip(reason="Unsupported")
    def test_generate_from_inputs_embeds_0_greedy(self):
        pass

    @unittest.skip(reason="Unsupported")
    def test_generate_from_inputs_embeds_1_beam_search(self):
        pass

    @unittest.skip(reason="Dynamic control flow due to MoE")
    def test_generate_with_static_cache(self):
        pass

    @unittest.skip(reason="Dynamic control flow due to MoE")
    def test_generate_from_inputs_embeds_with_static_cache(self):
        pass

    @unittest.skip(reason="Dynamic control flow due to MoE")
    def test_generate_compile_model_forward(self):
        pass


@require_torch
class AriaForConditionalGenerationIntegrationTest(unittest.TestCase):
    def setUp(self):
        self.processor = AutoProcessor.from_pretrained("rhymes-ai/Aria")

    def tearDown(self):
        gc.collect()
        torch.cuda.empty_cache()

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test(self):
        # Let' s make sure we test the preprocessing to replace what is used
        model = AriaForConditionalGeneration.from_pretrained("rhymes-ai/Aria", load_in_4bit=True)

        prompt = "<image>\nUSER: What are the things I should be cautious about when I visit this place?\nASSISTANT:"
        image_file = "https://aria-vl.github.io/static/images/view.jpg"
        raw_image = Image.open(requests.get(image_file, stream=True).raw)
        inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt")

        EXPECTED_INPUT_IDS = torch.tensor([[1, 32000, 28705, 13, 11123, 28747, 1824, 460, 272, 1722,315, 1023, 347, 13831, 925, 684, 739, 315, 3251, 456,1633, 28804, 13, 4816, 8048, 12738, 28747]])  # fmt: skip
        self.assertTrue(torch.equal(inputs["input_ids"], EXPECTED_INPUT_IDS))

        output = model.generate(**inputs, max_new_tokens=20)
        EXPECTED_DECODED_TEXT = "\nUSER: What are the things I should be cautious about when I visit this place?\nASSISTANT: When visiting this place, there are a few things one should be cautious about. Firstly,"  # fmt: skip

        self.assertEqual(
            self.processor.decode(output[0], skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test_llama_single(self):
        # Let' s make sure we test the preprocessing to replace what is used
        model_id = "rhymes-ai/Aria"

        model = AriaForConditionalGeneration.from_pretrained(model_id, load_in_4bit=True)
        processor = AutoProcessor.from_pretrained(model_id)

        prompt = "USER: <image>\nWhat are the things I should be cautious about when I visit this place? ASSISTANT:"
        image_file = "https://aria-vl.github.io/static/images/view.jpg"
        raw_image = Image.open(requests.get(image_file, stream=True).raw)
        inputs = processor(images=raw_image, text=prompt, return_tensors="pt").to(torch_device, torch.float16)

        output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
        EXPECTED_DECODED_TEXT = "USER:  \nWhat are the things I should be cautious about when I visit this place? ASSISTANT: When visiting this place, which is a pier or dock extending over a body of water, there are a few things to be cautious about. First, be aware of the weather conditions, as sudden changes in weather can make the pier unsafe to walk on. Second, be mindful of the water depth and any potential hazards, such as submerged rocks or debris, that could cause accidents or injuries. Additionally, be cautious of the tides and currents, as they can change rapidly and pose a risk to swimmers or those who venture too close to the edge of the pier. Finally, be respectful of the environment and other visitors, and follow any posted rules or guidelines for the area."  # fmt: skip

        self.assertEqual(
            processor.decode(output[0], skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test_llama_batched(self):
        # Let' s make sure we test the preprocessing to replace what is used
        model_id = "rhymes-ai/Aria"

        model = AriaForConditionalGeneration.from_pretrained(model_id, load_in_4bit=True)
        processor = AutoProcessor.from_pretrained(model_id)

        prompts = [
            "USER: <image>\nWhat are the things I should be cautious about when I visit this place? What should I bring with me? ASSISTANT:",
            "USER: <image>\nWhat is this? ASSISTANT:",
        ]
        image1 = Image.open(requests.get("https://aria-vl.github.io/static/images/view.jpg", stream=True).raw)
        image2 = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)

        inputs = processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)

        output = model.generate(**inputs, max_new_tokens=20)

        EXPECTED_DECODED_TEXT = ['USER:  \nWhat are the things I should be cautious about when I visit this place? What should I bring with me? ASSISTANT: When visiting this place, which is a pier or dock extending over a body of water, you', 'USER:  \nWhat is this? ASSISTANT: The image features two cats lying down on a pink couch. One cat is located on']  # fmt: skip

        self.assertEqual(
            processor.batch_decode(output, skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test_batch(self):
        # Let' s make sure we test the preprocessing to replace what is used
        model = AriaForConditionalGeneration.from_pretrained("rhymes-ai/Aria", load_in_4bit=True)
        # The first batch is longer in terms of text, but only has 1 image. The second batch will be padded in text, but the first will be padded because images take more space!.
        prompts = [
            "USER: <image>\nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT:",
            "USER: <image>\nWhat is this?\nASSISTANT:",
        ]
        image1 = Image.open(requests.get("https://aria-vl.github.io/static/images/view.jpg", stream=True).raw)
        image2 = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)

        inputs = self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)

        output = model.generate(**inputs, max_new_tokens=20)

        EXPECTED_DECODED_TEXT = [
            'USER:  \nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT: When visiting this place, there are a few things to be cautious about and items to bring.',
            'USER:  \nWhat is this?\nASSISTANT: Cats'
        ]  # fmt: skip
        self.assertEqual(
            self.processor.batch_decode(output, skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test_llama_batched_regression(self):
        # Let' s make sure we test the preprocessing to replace what is used
        model_id = "rhymes-ai/Aria"

        # Multi-image & multi-prompt (e.g. 3 images and 2 prompts now fails with SDPA, this tests if "eager" works as before)
        model = AriaForConditionalGeneration.from_pretrained(model_id, load_in_4bit=True, attn_implementation="eager")
        processor = AutoProcessor.from_pretrained(model_id, pad_token="<pad>")

        prompts = [
            "USER: <image>\nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT:",
            "USER: <image>\nWhat is this?\nASSISTANT: Two cats lying on a bed!\nUSER: <image>\nAnd this?\nASSISTANT:",
        ]
        image1 = Image.open(requests.get("https://aria-vl.github.io/static/images/view.jpg", stream=True).raw)
        image2 = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)

        inputs = processor(images=[image1, image2, image1], text=prompts, return_tensors="pt", padding=True)

        output = model.generate(**inputs, max_new_tokens=20)

        EXPECTED_DECODED_TEXT = ['USER:  \nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT: When visiting this place, which appears to be a dock or pier extending over a body of water', 'USER:  \nWhat is this?\nASSISTANT: Two cats lying on a bed!\nUSER:  \nAnd this?\nASSISTANT: A cat sleeping on a bed.']  # fmt: skip

        self.assertEqual(
            processor.batch_decode(output, skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )

    @slow
    @require_torch
    @require_vision
    @require_bitsandbytes
    def test_batched_generation(self):
        model = AriaForConditionalGeneration.from_pretrained("rhymes-ai/Aria", load_in_4bit=True)

        processor = AutoProcessor.from_pretrained("rhymes-ai/Aria")

        prompt1 = "<image>\n<image>\nUSER: What's the the difference of two images?\nASSISTANT:"
        prompt2 = "<image>\nUSER: Describe the image.\nASSISTANT:"
        prompt3 = "<image>\nUSER: Describe the image.\nASSISTANT:"
        url1 = "https://images.unsplash.com/photo-1552053831-71594a27632d?q=80&w=3062&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
        url2 = "https://images.unsplash.com/photo-1617258683320-61900b281ced?q=80&w=3087&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
        image1 = Image.open(requests.get(url1, stream=True).raw)
        image2 = Image.open(requests.get(url2, stream=True).raw)

        inputs = processor(
            images=[image1, image2, image1, image2],
            text=[prompt1, prompt2, prompt3],
            return_tensors="pt",
            padding=True,
        ).to(torch_device)

        model = model.eval()

        EXPECTED_OUTPUT = [
            "\n \nUSER: What's the the difference of two images?\nASSISTANT: The difference between the two images is that one shows a dog standing on a grassy field, while",
            "\nUSER: Describe the image.\nASSISTANT: The image features a brown and white dog sitting on a sidewalk. The dog is holding a small",
            "\nUSER: Describe the image.\nASSISTANT: The image features a lone llama standing on a grassy hill. The llama is the",
        ]

        generate_ids = model.generate(**inputs, max_new_tokens=20)
        outputs = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
        self.assertEqual(outputs, EXPECTED_OUTPUT)

    def test_tokenizer_integration(self):
        model_id = "rhymes-ai/Aria"
        slow_tokenizer = AutoTokenizer.from_pretrained(
            model_id, bos_token="<|startoftext|>", eos_token="<|endoftext|>", use_fast=False
        )
        slow_tokenizer.add_tokens("<image>", True)

        fast_tokenizer = AutoTokenizer.from_pretrained(
            model_id,
            bos_token="<|startoftext|>",
            eos_token="<|endoftext|>",
            from_slow=True,
            legacy=False,
        )
        fast_tokenizer.add_tokens("<image>", True)

        prompt = "<|startoftext|><|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|>"
        EXPECTED_OUTPUT = ['<|startoftext|>', '<', '|', 'im', '_', 'start', '|', '>', 'system', '\n', 'Answer', '▁the', '▁questions', '.<', '|', 'im', '_', 'end', '|', '><', '|', 'im', '_', 'start', '|', '>', 'user', '\n', '<image>', '\n', 'What', '▁is', '▁shown', '▁in', '▁this', '▁image', '?', '<', '|', 'im', '_', 'end', '|', '>']  # fmt: skip
        self.assertEqual(slow_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
        self.assertEqual(fast_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)

    @slow
    @require_bitsandbytes
    def test_generation_no_images(self):
        model_id = "rhymes-ai/Aria"
        model = AriaForConditionalGeneration.from_pretrained(model_id, load_in_4bit=True)
        processor = AutoProcessor.from_pretrained(model_id)

        # Prepare inputs with no images
        inputs = processor(text="Hello, I am", return_tensors="pt").to(torch_device)

        # Make sure that `generate` works
        _ = model.generate(**inputs, max_new_tokens=20)
