# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Data operations, will be used in run_pretrain.py
"""
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
from mindspore import log as logger
from .config import cfg


def create_bert_dataset(device_num=1, rank=0, do_shuffle="true", data_dir=None, schema_dir=None):
    """create train dataset"""
    # apply repeat operations
    files = os.listdir(data_dir)
    data_files = []
    for file_name in files:
        if "tfrecord" in file_name:
            data_files.append(os.path.join(data_dir, file_name))
    data_set = ds.TFRecordDataset(data_files, schema_dir if schema_dir != "" else None,
                                  columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
                                                "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"],
                                  shuffle=ds.Shuffle.FILES if do_shuffle == "true" else False,
                                  num_shards=device_num, shard_id=rank, shard_equal_rows=True)
    ori_dataset_size = data_set.get_dataset_size()
    print('origin dataset size: ', ori_dataset_size)
    type_cast_op = C.TypeCast(mstype.int32)
    data_set = data_set.map(operations=type_cast_op, input_columns="masked_lm_ids")
    data_set = data_set.map(operations=type_cast_op, input_columns="masked_lm_positions")
    data_set = data_set.map(operations=type_cast_op, input_columns="next_sentence_labels")
    data_set = data_set.map(operations=type_cast_op, input_columns="segment_ids")
    data_set = data_set.map(operations=type_cast_op, input_columns="input_mask")
    data_set = data_set.map(operations=type_cast_op, input_columns="input_ids")
    # apply batch operations
    data_set = data_set.batch(cfg.batch_size, drop_remainder=True)
    logger.info("data size: {}".format(data_set.get_dataset_size()))
    logger.info("repeat count: {}".format(data_set.get_repeat_count()))
    return data_set


def create_ner_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy", data_file_path=None,
                       dataset_format="mindrecord", schema_file_path=None, do_shuffle=True):
    """create finetune or evaluation dataset"""
    type_cast_op = C.TypeCast(mstype.int32)
    if dataset_format == "mindrecord":
        dataset = ds.MindDataset([data_file_path],
                                 columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"],
                                 shuffle=do_shuffle)
    else:
        dataset = ds.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
                                     columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"],
                                     shuffle=do_shuffle)
    if assessment_method == "Spearman_correlation":
        type_cast_op_float = C.TypeCast(mstype.float32)
        dataset = dataset.map(operations=type_cast_op_float, input_columns="label_ids")
    else:
        dataset = dataset.map(operations=type_cast_op, input_columns="label_ids")
    dataset = dataset.map(operations=type_cast_op, input_columns="segment_ids")
    dataset = dataset.map(operations=type_cast_op, input_columns="input_mask")
    dataset = dataset.map(operations=type_cast_op, input_columns="input_ids")
    dataset = dataset.repeat(repeat_count)
    # apply batch operations
    dataset = dataset.batch(batch_size, drop_remainder=True)
    return dataset


def create_classification_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy",
                                  data_file_path=None, schema_file_path=None, do_shuffle=True):
    """create finetune or evaluation dataset"""
    type_cast_op = C.TypeCast(mstype.int32)
    data_set = ds.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
                                  columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"],
                                  shuffle=do_shuffle)
    if assessment_method == "Spearman_correlation":
        type_cast_op_float = C.TypeCast(mstype.float32)
        data_set = data_set.map(operations=type_cast_op_float, input_columns="label_ids")
    else:
        data_set = data_set.map(operations=type_cast_op, input_columns="label_ids")
    data_set = data_set.map(operations=type_cast_op, input_columns="segment_ids")
    data_set = data_set.map(operations=type_cast_op, input_columns="input_mask")
    data_set = data_set.map(operations=type_cast_op, input_columns="input_ids")
    data_set = data_set.repeat(repeat_count)
    # apply batch operations
    data_set = data_set.batch(batch_size, drop_remainder=True)
    return data_set


def generator_squad(data_features):
    for feature in data_features:
        yield (feature.input_ids, feature.input_mask, feature.segment_ids, feature.unique_id)


def create_squad_dataset(batch_size=1, repeat_count=1, data_file_path=None, schema_file_path=None,
                         is_training=True, do_shuffle=True):
    """create finetune or evaluation dataset"""
    type_cast_op = C.TypeCast(mstype.int32)
    if is_training:
        data_set = ds.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
                                      columns_list=["input_ids", "input_mask", "segment_ids", "start_positions",
                                                    "end_positions", "unique_ids", "is_impossible"],
                                      shuffle=do_shuffle)
        data_set = data_set.map(operations=type_cast_op, input_columns="start_positions")
        data_set = data_set.map(operations=type_cast_op, input_columns="end_positions")
    else:
        data_set = ds.GeneratorDataset(generator_squad(data_file_path), shuffle=do_shuffle,
                                       column_names=["input_ids", "input_mask", "segment_ids", "unique_ids"])
    data_set = data_set.map(operations=type_cast_op, input_columns="segment_ids")
    data_set = data_set.map(operations=type_cast_op, input_columns="input_mask")
    data_set = data_set.map(operations=type_cast_op, input_columns="input_ids")
    data_set = data_set.map(operations=type_cast_op, input_columns="unique_ids")
    data_set = data_set.repeat(repeat_count)
    # apply batch operations
    data_set = data_set.batch(batch_size, drop_remainder=True)
    return data_set