# Copyright 2021 The HuggingFace 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
# limitations under the License.

import json
import os
from os.path import exists
from os.path import join as pjoin

from datasets import Dataset, load_dataset, load_from_disk
from tqdm import tqdm

_CACHE_DIR = "cache_dir"


# grab first N rows of a dataset from the hub
def load_truncated_dataset(
    dataset_name,
    config_name=None,
    split_name=None,
    num_rows=0,
    use_streaming=True,
    use_auth_token=None,
    use_dataset=None,
):
    """
    This function loads the first `num_rows` items of a dataset for a
    given `config_name` and `split_name`.
    When the dataset is streamable, we iterate through the first
    `num_rows` examples in streaming mode, write them to a jsonl file,
    then create a new dataset from the json.
    This is the most direct way to make a Dataset from an IterableDataset
    as of datasets version 1.6.1.
    Otherwise, we download the full dataset and select the first
    `num_rows` items
    Args:
        dataset_name (string):
            dataset id in the dataset library
        config_name (string):
            dataset configuration
        split_name (string):
            optional split name, defaults to `train`
        num_rows (int):
            number of rows to truncate the dataset to, <= 0  means no truncation
        use_streaming (bool):
            whether to use streaming when the dataset supports it
        use_auth_token (string):
            HF authentication token to access private datasets
        use_dataset (Dataset):
            use existing dataset instead of getting one from the hub
    Returns:
        Dataset:
            the truncated dataset as a Dataset object
    """
    split_name = "train" if split_name is None else split_name
    cache_name = f"{dataset_name.replace('/', '---')}_{'default' if config_name is None else config_name}_{split_name}_{num_rows}"
    if use_streaming:
        if not exists(pjoin(_CACHE_DIR, "tmp", f"{cache_name}.jsonl")):
            iterable_dataset = (
                load_dataset(
                    dataset_name,
                    name=config_name,
                    split=split_name,
                    cache_dir=pjoin(_CACHE_DIR, "tmp", cache_name + "_temp"),
                    streaming=True,
                    use_auth_token=use_auth_token,
                )
                if use_dataset is None
                else use_dataset
            )
            if num_rows > 0:
                iterable_dataset = iterable_dataset.take(num_rows)
            f = open(
                pjoin(_CACHE_DIR, "tmp", f"{cache_name}.jsonl"), "w", encoding="utf-8"
            )
            for row in tqdm(iterable_dataset):
                _ = f.write(json.dumps(row) + "\n")
            f.close()
        dataset = Dataset.from_json(
            pjoin(_CACHE_DIR, "tmp", f"{cache_name}.jsonl"),
            cache_dir=pjoin(_CACHE_DIR, "tmp", cache_name + "_jsonl"),
        )
    else:
        full_dataset = (
            load_dataset(
                dataset_name,
                name=config_name,
                split=split_name,
                use_auth_token=use_auth_token,
                cache_dir=pjoin(_CACHE_DIR, "tmp", cache_name + "_temp"),
            )
            if use_dataset is None
            else use_dataset
        )
        if num_rows > 0:
            dataset = full_dataset.select(range(num_rows))
        else:
            dataset = full_dataset
    return dataset


# get all instances of a specific field in a dataset with indices and labels
def extract_features(examples, indices, input_field_path, label_name=None):
    """
    This function prepares examples for further processing by:
        - returning an "unrolled" list of all the fields denoted by input_field_path
        - with the indices corresponding to the example the field item came from
        - optionally, the corresponding label is also returned with each field item
    Args:
        examples (dict):
            a dictionary of lists, provided dataset.map with batched=True
        indices (list):
            a list of indices, provided dataset.map with with_indices=True
        input_field_path (tuple):
            a tuple indicating the field we want to extract. Can be a singleton
            for top-level features (e.g. `("text",)`) or a full path for nested
            features (e.g. `("answers", "text")`) to get all answer strings in
            SQuAD
        label_name (string):
            optionally used to align the field items with labels. Currently,
            returns the top-most field that has this name, which may fail in some
            edge cases
            TODO: make it so the label is specified through a full path
    Returns:
        Dict:
            a dictionary of lists, used by dataset.map with batched=True.
            labels are all None if label_name!=None but label_name is not found
            TODO: raised an error if label_name is specified but not found
    """
    top_name = input_field_path[0]
    if label_name is not None and label_name in examples:
        item_list = [
            {"index": i, "label": label, "items": items}
            for i, items, label in zip(
                indices, examples[top_name], examples[label_name]
            )
        ]
    else:
        item_list = [
            {"index": i, "label": None, "items": items}
            for i, items in zip(indices, examples[top_name])
        ]
    for field_name in input_field_path[1:]:
        new_item_list = []
        for dct in item_list:
            if label_name is not None and label_name in dct["items"]:
                if isinstance(dct["items"][field_name], list):
                    new_item_list += [
                        {"index": dct["index"], "label": label, "items": next_item}
                        for next_item, label in zip(
                            dct["items"][field_name], dct["items"][label_name]
                        )
                    ]
                else:
                    new_item_list += [
                        {
                            "index": dct["index"],
                            "label": dct["items"][label_name],
                            "items": dct["items"][field_name],
                        }
                    ]
            else:
                if isinstance(dct["items"][field_name], list):
                    new_item_list += [
                        {
                            "index": dct["index"],
                            "label": dct["label"],
                            "items": next_item,
                        }
                        for next_item in dct["items"][field_name]
                    ]
                else:
                    new_item_list += [
                        {
                            "index": dct["index"],
                            "label": dct["label"],
                            "items": dct["items"][field_name],
                        }
                    ]
        item_list = new_item_list
    res = (
        {
            "ids": [dct["index"] for dct in item_list],
            "field": [dct["items"] for dct in item_list],
        }
        if label_name is None
        else {
            "ids": [dct["index"] for dct in item_list],
            "field": [dct["items"] for dct in item_list],
            "label": [dct["label"] for dct in item_list],
        }
    )
    return res


# grab some examples and extract interesting fields
def prepare_clustering_dataset(
    dataset_name,
    input_field_path,
    label_name=None,
    config_name=None,
    split_name=None,
    num_rows=0,
    use_streaming=True,
    use_auth_token=None,
    cache_dir=_CACHE_DIR,
    use_dataset=None,
):
    """
    This function loads the first `num_rows` items of a dataset for a
    given `config_name` and `split_name`, and extracts all instances of a field
    of interest denoted by `input_field_path` along with the indices of the
    examples the instances came from and optionall their labels (`label_name`)
    in the original dataset
    Args:
        dataset_name (string):
            dataset id in the dataset library
        input_field_path (tuple):
            a tuple indicating the field we want to extract. Can be a singleton
            for top-level features (e.g. `("text",)`) or a full path for nested
            features (e.g. `("answers", "text")`) to get all answer strings in
            SQuAD
        label_name (string):
            optionally used to align the field items with labels. Currently,
            returns the top-most field that has this name, which fails in edge cases
        config_name (string):
            dataset configuration
        split_name (string):
            optional split name, defaults to `train`
        num_rows (int):
            number of rows to truncate the dataset to, <= 0  means no truncation
        use_streaming (bool):
            whether to use streaming when the dataset supports it
        use_auth_token (string):
            HF authentication token to access private datasets
        use_dataset (Dataset):
            use existing dataset instead of getting one from the hub
    Returns:
        Dataset:
            the extracted dataset as a Dataset object. Note that if there is more
            than one instance of the field per example in the original dataset
            (e.g. multiple answers per QA example), the returned dataset will
            have more than `num_rows` rows
        string:
            the path to the newsly created dataset directory
    """
    cache_path = [
        cache_dir,
        dataset_name.replace("/", "---"),
        f"{'default' if config_name is None else config_name}",
        f"{'train' if split_name is None else split_name}",
        f"field-{'->'.join(input_field_path)}-label-{label_name}",
        f"{num_rows}_rows",
        "features_dset",
    ]
    if exists(pjoin(*cache_path)):
        pre_clustering_dset = load_from_disk(pjoin(*cache_path))
    else:
        truncated_dset = load_truncated_dataset(
            dataset_name,
            config_name,
            split_name,
            num_rows,
            use_streaming,
            use_auth_token,
            use_dataset,
        )

        def batch_func(examples, indices):
            return extract_features(examples, indices, input_field_path, label_name)

        pre_clustering_dset = truncated_dset.map(
            batch_func,
            remove_columns=truncated_dset.features,
            batched=True,
            with_indices=True,
        )
        for i in range(1, len(cache_path) - 1):
            if not exists(pjoin(*cache_path[:i])):
                os.mkdir(pjoin(*cache_path[:i]))
        pre_clustering_dset.save_to_disk(pjoin(*cache_path))
    return pre_clustering_dset, pjoin(*cache_path)