# coding=utf-8
# Copyright 2023 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
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"""Testing suite for the MgpstrProcessor."""

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class MgpstrProcessorTest(unittest.TestCase):
    image_processing_class = ViTImageProcessor if is_vision_available() else None

    @property
    def image_processor_dict(self):
        return self.prepare_image_processor_dict()

    def setUp(self):
        self.image_size = (3, 32, 128)
        self.tmpdirname = tempfile.mkdtemp()

        vocab = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']  # fmt: skip
        vocab_tokens = dict(zip(vocab, range(len(vocab))))

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")

        image_processor_map = {
            "do_normalize": False,
            "do_resize": True,
            "image_processor_type": "ViTImageProcessor",
            "resample": 3,
            "size": {"height": 32, "width": 128},
        }
        self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
        with open(self.image_processor_file, "w", encoding="utf-8") as fp:
            json.dump(image_processor_map, fp)

    def get_tokenizer(self, **kwargs):
        return MgpstrTokenizer.from_pretrained(self.tmpdirname, **kwargs)

    def get_image_processor(self, **kwargs):
        return ViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

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

        image_input = np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)

        image_input = Image.fromarray(np.moveaxis(image_input, 0, -1))

        return image_input

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
        processor.save_pretrained(self.tmpdirname)
        processor = MgpstrProcessor.from_pretrained(self.tmpdirname, use_fast=False)

        self.assertEqual(processor.char_tokenizer.get_vocab(), tokenizer.get_vocab())
        self.assertIsInstance(processor.char_tokenizer, MgpstrTokenizer)

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

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)
        processor.save_pretrained(self.tmpdirname)

        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 = MgpstrProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.char_tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.char_tokenizer, MgpstrTokenizer)

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

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_image_proc = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

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

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "test"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str)
        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "test"
        image_input = self.prepare_image_inputs()

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

        self.assertListEqual(list(inputs.keys()), ["pixel_values", "labels"])

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

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)

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

        decoded_processor = processor.char_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)
        decode_strs = [seq.replace(" ", "") for seq in decoded_tok]

        self.assertListEqual(decode_strs, decoded_processor)

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = None
        image_input = self.prepare_image_inputs()

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

        self.assertListEqual(list(inputs.keys()), processor.model_input_names)

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

        processor = MgpstrProcessor(tokenizer=tokenizer, image_processor=image_processor)

        char_input = torch.randn(1, 27, 38)
        bpe_input = torch.randn(1, 27, 50257)
        wp_input = torch.randn(1, 27, 30522)

        results = processor.batch_decode([char_input, bpe_input, wp_input])

        self.assertListEqual(list(results.keys()), ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"])
