# 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Testing suite for the PyTorch chameleon model."""

import unittest

import requests
from parameterized import parameterized

from transformers import ChameleonConfig, is_torch_available, is_vision_available, set_seed
from transformers.testing_utils import (
    require_bitsandbytes,
    require_read_token,
    require_torch,
    slow,
    torch_device,
)

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


if is_vision_available():
    from PIL import Image

if is_torch_available():
    import torch

    from transformers import (
        ChameleonForConditionalGeneration,
        ChameleonModel,
        ChameleonProcessor,
    )


class ChameleonModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=False,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        image_token_id=98,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=2,
        num_key_value_heads=2,
        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,
        num_labels=3,
        num_choices=4,
        pad_token_id=0,
        vq_num_embeds=12,
        vq_embed_dim=12,
        vq_channel_multiplier=[1, 2],
        vq_img_token_start_id=10,  # has to be less than vocab size when added with vq_num_embeds
        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.image_token_id = image_token_id
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_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.num_labels = num_labels
        self.num_choices = num_choices
        self.pad_token_id = pad_token_id
        self.scope = scope
        self.vq_num_embeds = vq_num_embeds
        self.vq_embed_dim = vq_embed_dim
        self.vq_channel_multiplier = vq_channel_multiplier
        self.vq_img_token_start_id = vq_img_token_start_id

    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 = torch.tril(torch.ones_like(input_ids).to(torch_device))

        sequence_labels = None
        token_labels = None
        choice_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        # create dummy vocab map for image2bpe mapping if it needs remapping
        # we assume that vocab size is big enough to accoun for image tokens somewhere in the beginning
        # same way as in real ckpt, when img tokens are in first half of embeds
        # we will need "vq_num_embeds" amount of tokens

        vocab_map = {i: chr(i) for i in range(self.vocab_size)}
        vocab_map[self.image_token_id] = "<image>"
        start = self.vq_img_token_start_id
        end = self.vq_img_token_start_id + self.vq_num_embeds
        for i in range(start, end):
            vocab_map[i] = f"IMGIMGBS{i}"  # dummy str for each token, anything starting with IMGIMG

        return ChameleonConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            num_key_value_heads=self.num_key_value_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,
            pad_token_id=self.pad_token_id,
            vocabulary_map={v: k for k, v in vocab_map.items()},
            vq_config=self.get_vq_config(),
        )

    def get_vq_config(self):
        return {
            "embed_dim": self.vq_embed_dim,
            "num_embeddings": self.vq_num_embeds,
            "latent_channels": self.vq_embed_dim,
            "in_channels": 3,
            "base_channels": 32,  # we have a GroupNorm of 32 groups, so can't do less
            "channel_multiplier": self.vq_channel_multiplier,
        }

    def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
        model = ChameleonModel(config=config)
        model.to(torch_device)
        model.eval()
        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))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = ChameleonForConditionalGeneration(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        model = ChameleonForConditionalGeneration(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        outputs = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

        output_from_no_past = model(
            next_input_ids,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["hidden_states"][0]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class ChameleonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (ChameleonModel, ChameleonForConditionalGeneration) if is_torch_available() else ()
    all_generative_model_classes = (ChameleonForConditionalGeneration,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": ChameleonModel,
            "text-generation": ChameleonForConditionalGeneration,
            "image-text-to-text": ChameleonForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False
    fx_compatible = False

    def setUp(self):
        self.model_tester = ChameleonModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ChameleonConfig, 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)

    @parameterized.expand([("linear",), ("dynamic",)])
    def test_model_rope_scaling(self, scaling_type):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        short_input = ids_tensor([1, 10], config.vocab_size)
        long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        original_model = ChameleonModel(config)
        original_model.to(torch_device)
        original_model.eval()
        original_short_output = original_model(short_input).last_hidden_state
        original_long_output = original_model(long_input).last_hidden_state

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
        scaled_model = ChameleonModel(config)
        scaled_model.to(torch_device)
        scaled_model.eval()
        scaled_short_output = scaled_model(short_input).last_hidden_state
        scaled_long_output = scaled_model(long_input).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
        else:
            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

    @unittest.skip("Chameleon forces some token ids to be -inf!")
    def test_batching_equivalence(self):
        pass

    # TODO (joao, raushan): fix me -- the problem is in `cache_position[0] == 0`, i.e. dynamic control flow
    @unittest.skip("Chameleon is not compatible with end-to-end generation compilation")
    def test_generate_compile_fullgraph(self):
        pass


@require_torch
class ChameleonIntegrationTest(unittest.TestCase):
    @slow
    @require_bitsandbytes
    @require_read_token
    def test_model_7b(self):
        model = ChameleonForConditionalGeneration.from_pretrained(
            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
        )
        processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")

        image = Image.open(
            requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
        )
        prompt = "<image>Describe what do you see here and tell me about the history behind it?"

        inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device, torch.float16)

        # greedy generation outputs
        EXPECTED_TEXT_COMPLETION = ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue line extending across the center of the image. The line is labeled "390 light years" and is accompanied by a small black and']  # fmt: skip
        generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
        text = processor.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @slow
    @require_bitsandbytes
    @require_read_token
    def test_model_7b_batched(self):
        model = ChameleonForConditionalGeneration.from_pretrained(
            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
        )
        processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")

        image = Image.open(
            requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
        )
        image_2 = Image.open(
            requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
        )
        prompts = [
            "<image>Describe what do you see here and tell me about the history behind it?",
            "What constellation is this image showing?<image>",
        ]

        inputs = processor(images=[image, image_2], text=prompts, padding=True, return_tensors="pt").to(
            model.device, torch.float16
        )

        # greedy generation outputs
        EXPECTED_TEXT_COMPLETION = [
            'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Alpha Centauri. The star map is a representation of the night sky, showing the positions of stars in',
            'What constellation is this image showing?The image is showing the constellation of Orion.'
            ]  # fmt: skip
        generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
        text = processor.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @slow
    @require_bitsandbytes
    @require_read_token
    def test_model_7b_multi_image(self):
        model = ChameleonForConditionalGeneration.from_pretrained(
            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
        )
        processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")

        image = Image.open(
            requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
        )
        image_2 = Image.open(
            requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
        )
        prompt = "What do these two images have in common?<image><image>"

        inputs = processor(images=[image, image_2], text=prompt, return_tensors="pt").to(model.device, torch.float16)

        # greedy generation outputs
        EXPECTED_TEXT_COMPLETION = ['What do these two images have in common?The two images show a connection between two things that are not necessarily related. The first image shows a group of stars, while the second image shows a network of lines connecting two points. The connection between']  # fmt: skip
        generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
        text = processor.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
