# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# 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 unittest

from transformers import is_torch_available
from transformers.file_utils import cached_property

from .utils import require_torch, slow, torch_device


if is_torch_available():
    import torch
    from transformers import MarianMTModel, MarianSentencePieceTokenizer


@require_torch
class IntegrationTests(unittest.TestCase):
    @classmethod
    def setUpClass(cls) -> None:
        cls.model_name = "Helsinki-NLP/opus-mt-en-de"
        cls.tokenizer = MarianSentencePieceTokenizer.from_pretrained(cls.model_name)
        cls.eos_token_id = cls.tokenizer.eos_token_id
        return cls

    @cached_property
    def model(self):
        model = MarianMTModel.from_pretrained(self.model_name).to(torch_device)
        if torch_device == "cuda":
            return model.half()
        else:
            return model

    @slow
    def test_forward(self):
        src, tgt = ["I am a small frog"], ["▁Ich ▁bin ▁ein ▁kleiner ▁Fro sch"]
        expected = [38, 121, 14, 697, 38848, 0]

        model_inputs: dict = self.tokenizer.prepare_translation_batch(src, tgt_texts=tgt).to(torch_device)
        self.assertListEqual(expected, model_inputs["input_ids"][0].tolist())

        desired_keys = {
            "input_ids",
            "attention_mask",
            "decoder_input_ids",
            "decoder_attention_mask",
        }
        self.assertSetEqual(desired_keys, set(model_inputs.keys()))
        with torch.no_grad():
            logits, *enc_features = self.model(**model_inputs)
        max_indices = logits.argmax(-1)
        self.tokenizer.decode_batch(max_indices)

    @slow
    def test_repl_generate_one(self):
        src = ["I am a small frog.", "Hello"]
        model_inputs: dict = self.tokenizer.prepare_translation_batch(src).to(torch_device)
        self.assertEqual(self.model.device, model_inputs["input_ids"].device)
        generated_ids = self.model.generate(model_inputs["input_ids"], num_beams=6,)
        generated_words = self.tokenizer.decode_batch(generated_ids)[0]
        expected_words = "Ich bin ein kleiner Frosch."
        self.assertEqual(expected_words, generated_words)

    @slow
    def test_repl_generate_batch(self):
        src = [
            "I am a small frog.",
            "Now I can forget the 100 words of german that I know.",
            "O",
            "Tom asked his teacher for advice.",
            "That's how I would do it.",
            "Tom really admired Mary's courage.",
            "Turn around and close your eyes.",
        ]
        model_inputs: dict = self.tokenizer.prepare_translation_batch(src).to(torch_device)
        self.assertEqual(self.model.device, model_inputs["input_ids"].device)
        generated_ids = self.model.generate(
            model_inputs["input_ids"],
            length_penalty=1.0,
            num_beams=2,  # 6 is the default
            bad_words_ids=[[self.tokenizer.pad_token_id]],
        )
        expected = [
            "Ich bin ein kleiner Frosch.",
            "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
            "",
            "Tom bat seinen Lehrer um Rat.",
            "So würde ich das tun.",
            "Tom bewunderte Marias Mut wirklich.",
            "Umdrehen und die Augen schließen.",
        ]
        # actual C++ output differences: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
        generated_words = self.tokenizer.decode_batch(generated_ids, skip_special_tokens=True)
        self.assertListEqual(expected, generated_words)

    def test_marian_equivalence(self):
        batch = self.tokenizer.prepare_translation_batch(["I am a small frog"]).to(torch_device)
        input_ids = batch["input_ids"][0]
        expected = [38, 121, 14, 697, 38848, 0]
        self.assertListEqual(expected, input_ids.tolist())

    def test_pad_not_split(self):
        input_ids_w_pad = self.tokenizer.prepare_translation_batch(["I am a small frog <pad>"])["input_ids"][0]
        expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0]  # pad
        self.assertListEqual(expected_w_pad, input_ids_w_pad.tolist())