# Copyright 2021 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 os
import shutil
import tempfile
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import is_torchvision_available, is_vision_available

from ...test_processing_common import ProcessorTesterMixin


if is_vision_available():
    from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor, ViTImageProcessorFast


@require_tokenizers
@require_vision
class VisionTextDualEncoderProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = VisionTextDualEncoderProcessor

    @classmethod
    def setUpClass(cls):
        cls.tmpdirname = tempfile.mkdtemp()

        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]  # fmt: skip
        cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        with open(cls.vocab_file, "w", encoding="utf-8") as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

        image_processor_map = {
            "do_resize": True,
            "size": {"height": 18, "width": 18},
            "do_normalize": True,
            "image_mean": [0.5, 0.5, 0.5],
            "image_std": [0.5, 0.5, 0.5],
        }
        image_processor = ViTImageProcessor(**image_processor_map)
        tokenizer = cls.get_tokenizer()
        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
        processor.save_pretrained(cls.tmpdirname)

    @classmethod
    def get_tokenizer(cls, **kwargs):
        return BertTokenizer.from_pretrained(cls.tmpdirname, **kwargs)

    @classmethod
    def get_image_processor(cls, **kwargs):
        if is_torchvision_available():
            return ViTImageProcessorFast.from_pretrained(cls.tmpdirname, **kwargs)
        return ViTImageProcessor.from_pretrained(cls.tmpdirname, **kwargs)

    @classmethod
    def tearDownClass(cls):
        shutil.rmtree(cls.tmpdirname, ignore_errors=True)

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

        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)
        with tempfile.TemporaryDirectory() as tmpdir:
            processor.save_pretrained(tmpdir)
            processor = VisionTextDualEncoderProcessor.from_pretrained(tmpdir)

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
        self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))

        self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
        self.assertIsInstance(processor.image_processor, (ViTImageProcessor, ViTImageProcessorFast))

    def test_save_load_pretrained_additional_features(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            processor = VisionTextDualEncoderProcessor(
                tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()
            )
            processor.save_pretrained(tmpdir)

            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 = VisionTextDualEncoderProcessor.from_pretrained(
                tmpdir, 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, (BertTokenizer, BertTokenizerFast))

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, (ViTImageProcessor, ViTImageProcessorFast))

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

        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="pt")
        input_processor = processor(images=image_input, return_tensors="pt")

        for key in input_feat_extract:
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

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

        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str)

        for key in encoded_tok:
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

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

        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "token_type_ids", "attention_mask"])

        # test if it raises when no input is passed
        with self.assertRaises(ValueError):
            processor()

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

        processor = VisionTextDualEncoderProcessor(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)
