# Copyright (c) 2021 PaddlePaddle Authors. 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 paddle

from paddlenlp.utils.log import logger


def create_dataloader(dataset,
                      mode='train',
                      batch_size=1,
                      batchify_fn=None,
                      trans_fn=None):
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == 'train' else False
    if mode == 'train':
        batch_sampler = paddle.io.DistributedBatchSampler(
            dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        batch_sampler = paddle.io.BatchSampler(
            dataset, batch_size=batch_size, shuffle=shuffle)

    return paddle.io.DataLoader(
        dataset=dataset,
        batch_sampler=batch_sampler,
        collate_fn=batchify_fn,
        return_list=True)


def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False):
    """
    Builds model inputs from a sequence.
        
    A BERT sequence has the following format:

    - single sequence: ``[CLS] X [SEP]``

    Args:
        example(obj:`list(str)`): The list of text to be converted to ids.
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` 
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        max_seq_len(obj:`int`): The maximum total input sequence length after tokenization. 
            Sequences longer than this will be truncated, sequences shorter will be padded.
        is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.

    Returns:
        input_ids(obj:`list[int]`): The list of query token ids.
        token_type_ids(obj: `list[int]`): List of query sequence pair mask.
    """

    result = []

    for key, text in example.items():
        if 'label' in key:
            # do_evaluate
            result += [example['label']]
        else:
            # do_train
            encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length)
            input_ids = encoded_inputs["input_ids"]
            token_type_ids = encoded_inputs["token_type_ids"]
            result += [input_ids, token_type_ids]

    return result


def read_simcse_text(data_path):
    """Reads data."""
    with open(data_path, 'r', encoding='utf-8') as f:
        for line in f:
            data = line.rstrip()
            yield {'text_a': data, 'text_b': data}


def read_text_pair(data_path, is_test=False):
    """Reads data."""
    with open(data_path, 'r', encoding='utf-8') as f:
        for line in f:
            data = line.rstrip().split("\t")
            if is_test == False:
                if len(data) != 3:
                    continue
                yield {'text_a': data[0], 'text_b': data[1], 'label': data[2]}
            else:
                if len(data) != 2:
                    continue
                yield {'text_a': data[0], 'text_b': data[1]}