# Copyright 2022 The HuggingFace 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.

import numpy as np

from transformers.testing_utils import TestCasePlus, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        IdeficsImageProcessor,
        IdeficsProcessor,
        LlamaTokenizerFast,
        PreTrainedTokenizerFast,
    )


@require_torch
@require_vision
class IdeficsProcessorTest(TestCasePlus):
    def setUp(self):
        super().setUp()

        self.checkpoint_path = self.get_auto_remove_tmp_dir()

        image_processor = IdeficsImageProcessor()
        tokenizer = LlamaTokenizerFast.from_pretrained("HuggingFaceM4/tiny-random-idefics")

        processor = IdeficsProcessor(image_processor, tokenizer)

        processor.save_pretrained(self.checkpoint_path)

        self.input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"]

    def get_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).tokenizer

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).image_processor

    def prepare_prompts(self):
        """This function prepares a list of PIL images"""

        num_images = 2
        images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)]
        images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images]

        # print([type(x) for x in images])
        # die

        prompts = [
            # text and 1 image
            [
                "User:",
                images[0],
                "Describe this image.\nAssistant:",
            ],
            # text and images
            [
                "User:",
                images[0],
                "Describe this image.\nAssistant: An image of two dogs.\n",
                "User:",
                images[1],
                "Describe this image.\nAssistant:",
            ],
            # only text
            [
                "User:",
                "Describe this image.\nAssistant: An image of two kittens.\n",
                "User:",
                "Describe this image.\nAssistant:",
            ],
            # only images
            [
                images[0],
                images[1],
            ],
        ]

        return prompts

    def test_save_load_pretrained_additional_features(self):
        processor = IdeficsProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
        processor.save_pretrained(self.checkpoint_path)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = IdeficsProcessor.from_pretrained(
            self.checkpoint_path, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, IdeficsImageProcessor)

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)

        prompts = self.prepare_prompts()

        # test that all prompts succeeded
        input_processor = processor(prompts, return_tensors="pt")
        for key in self.input_keys:
            assert torch.is_tensor(input_processor[key])

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
        prompts = self.prepare_prompts()

        inputs = processor(prompts)

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertSetEqual(set(inputs.keys()), set(self.input_keys))