# coding=utf-8
# Copyright 2020 the 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.
from transformers import (
    AutoModelForSeq2SeqLM,
    BertTokenizer,
    DataCollatorForSeq2Seq,
    EncoderDecoderModel,
    GenerationConfig,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    T5Tokenizer,
)
from transformers.testing_utils import TestCasePlus, require_sentencepiece, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets


@require_sentencepiece
class Seq2seqTrainerTester(TestCasePlus):
    @slow
    @require_torch
    def test_finetune_bert2bert(self):
        bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny", "prajjwal1/bert-tiny")
        tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")

        bert2bert.config.vocab_size = bert2bert.config.encoder.vocab_size
        bert2bert.config.eos_token_id = tokenizer.sep_token_id
        bert2bert.config.decoder_start_token_id = tokenizer.cls_token_id
        bert2bert.config.max_length = 128

        train_dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train[:1%]")
        val_dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="validation[:1%]")

        train_dataset = train_dataset.select(range(32))
        val_dataset = val_dataset.select(range(16))

        batch_size = 4

        def _map_to_encoder_decoder_inputs(batch):
            # Tokenizer will automatically set [BOS] <text> [EOS]
            inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512)
            outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=128)
            batch["input_ids"] = inputs.input_ids
            batch["attention_mask"] = inputs.attention_mask

            batch["decoder_input_ids"] = outputs.input_ids
            batch["labels"] = outputs.input_ids.copy()
            batch["labels"] = [
                [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
            ]
            batch["decoder_attention_mask"] = outputs.attention_mask

            assert all(len(x) == 512 for x in inputs.input_ids)
            assert all(len(x) == 128 for x in outputs.input_ids)

            return batch

        def _compute_metrics(pred):
            labels_ids = pred.label_ids
            pred_ids = pred.predictions

            # all unnecessary tokens are removed
            pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
            label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)

            accuracy = sum([int(pred_str[i] == label_str[i]) for i in range(len(pred_str))]) / len(pred_str)

            return {"accuracy": accuracy}

        # map train dataset
        train_dataset = train_dataset.map(
            _map_to_encoder_decoder_inputs,
            batched=True,
            batch_size=batch_size,
            remove_columns=["article", "highlights"],
        )
        train_dataset.set_format(
            type="torch",
            columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
        )

        # same for validation dataset
        val_dataset = val_dataset.map(
            _map_to_encoder_decoder_inputs,
            batched=True,
            batch_size=batch_size,
            remove_columns=["article", "highlights"],
        )
        val_dataset.set_format(
            type="torch",
            columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
        )

        output_dir = self.get_auto_remove_tmp_dir()

        training_args = Seq2SeqTrainingArguments(
            output_dir=output_dir,
            per_device_train_batch_size=batch_size,
            per_device_eval_batch_size=batch_size,
            predict_with_generate=True,
            eval_strategy="steps",
            do_train=True,
            do_eval=True,
            warmup_steps=0,
            eval_steps=2,
            logging_steps=2,
        )

        # instantiate trainer
        trainer = Seq2SeqTrainer(
            model=bert2bert,
            args=training_args,
            compute_metrics=_compute_metrics,
            train_dataset=train_dataset,
            eval_dataset=val_dataset,
            tokenizer=tokenizer,
        )

        # start training
        trainer.train()

    @slow
    @require_torch
    def test_return_sequences(self):
        # Tests that the number of generated sequences is correct when num_return_sequences > 1
        # and essentially ensuring that `accelerator.gather()` is used instead of `gather_for_metrics`
        INPUT_COLUMN = "question"
        TARGET_COLUMN = "answer"
        MAX_INPUT_LENGTH = 256
        MAX_TARGET_LENGTH = 256

        dataset = datasets.load_dataset("gsm8k", "main", split="train[:38]")
        model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
        tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
        data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors="pt", padding="longest")
        gen_config = GenerationConfig.from_pretrained(
            "google-t5/t5-small", max_length=None, min_length=None, max_new_tokens=256, min_new_tokens=1, num_beams=5
        )

        training_args = Seq2SeqTrainingArguments(".", predict_with_generate=True)

        trainer = Seq2SeqTrainer(
            model=model,
            args=training_args,
            tokenizer=tokenizer,
            data_collator=data_collator,
            compute_metrics=lambda x: {"samples": x[0].shape[0]},
        )

        def prepare_data(examples):
            # Remove pairs where at least one record is none
            inputs = examples[INPUT_COLUMN]
            targets = examples[TARGET_COLUMN]

            model_inputs = tokenizer(inputs, max_length=MAX_INPUT_LENGTH, truncation=True)
            labels = tokenizer(text_target=targets, max_length=MAX_TARGET_LENGTH, truncation=True)
            model_inputs["labels"] = labels["input_ids"]

            return model_inputs

        prepared_dataset = dataset.map(prepare_data, batched=True, remove_columns=[INPUT_COLUMN, TARGET_COLUMN])
        dataset_len = len(prepared_dataset)  # 38

        for num_return_sequences in range(3, 0, -1):
            gen_config.num_return_sequences = num_return_sequences
            metrics = trainer.evaluate(eval_dataset=prepared_dataset, generation_config=gen_config)
            assert (
                metrics["eval_samples"] == dataset_len * num_return_sequences
            ), f"Got {metrics['eval_samples']}, expected: {dataset_len * num_return_sequences}"

    @require_torch
    def test_bad_generation_config_fail_early(self):
        # Tests that a bad geneartion config causes the trainer to fail early
        model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
        tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
        data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors="pt", padding="longest")
        gen_config = GenerationConfig(do_sample=False, top_p=0.9)  # bad: top_p is not compatible with do_sample=False

        training_args = Seq2SeqTrainingArguments(".", predict_with_generate=True, generation_config=gen_config)
        with self.assertRaises(ValueError) as exc:
            _ = Seq2SeqTrainer(
                model=model,
                args=training_args,
                tokenizer=tokenizer,
                data_collator=data_collator,
                compute_metrics=lambda x: {"samples": x[0].shape[0]},
            )
        self.assertIn("The loaded generation config instance is invalid", str(exc.exception))
