"""
 Copyright (c) 2024, Idiap Research Institute.
 All rights reserved.
 SPDX-License-Identifier: MIT License
 For full license text, see the LICENSE file in the repo root
"""

#!/usr/bin/env python3
import os
import csv
import json
import datasets
from datasets import (GeneratorBasedBuilder,
                      BuilderConfig,
                      SplitGenerator, 
                      DatasetInfo, 
                      Features,
                      Value,
                      Version)

logger = datasets.logging.get_logger(__name__)
datasets.logging.disable_progress_bar()

_VERSION = Version("1.0.0")
_CITATION = """
@inproceedings{burdisso-etal-2024-dialog2flow,
    title = "Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction",
    author = "Burdisso, Sergio  and
      Madikeri, Srikanth  and
      Motlicek, Petr",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami",
    publisher = "Association for Computational Linguistics",
}
"""

DATASETS_PRETRAIN = ["dialog-acts", "slots", "dialog-acts+slots"]
DATASETS_DS = {
    'ABCD': ['test', 'train', 'val'],
    'BiTOD': ['test', 'train', 'val'],
    'DSTC2-Clean': ['test', 'train', 'val'],
    'Disambiguation': ['test', 'train', 'val'],
    'FRAMES': ['test', 'train'],
    'HDSA-Dialog': ['test', 'train', 'val'],
    'GECOR': ['train'],
    'KETOD': ['test', 'train', 'val'],
    'MS-DC': ['train'],
    'MULTIWOZ2_2': ['test', 'train', 'val'],
    'MulDoGO': ['test', 'train', 'val'],
    'MultiWOZ_2.1': ['test', 'train', 'val'],
    'SGD': ['test', 'train', 'val'],
    'SimJointMovie': ['test', 'train', 'val'],
    'SimJointRestaurant': ['test', 'train', 'val'],
    'Taskmaster1': ['test', 'train', 'val'],
    'Taskmaster2': ['train'],
    'Taskmaster3': ['test', 'train', 'val'],
    'WOZ2_0': ['test', 'train', 'val'],
    'SimJointGEN': ['test', 'train', 'val'],
}
DATASETS = list(DATASETS_DS.keys()) + DATASETS_PRETRAIN
SPLIT2NAME = {
    "train": datasets.Split.TRAIN,
    "val": datasets.Split.VALIDATION,
    "test": datasets.Split.TEST,
}


class Dialog2FlowConfig(BuilderConfig):
    """BuilderConfig for Dialog2Flow."""

    def __init__(self, name, citation, url, **kwargs):
        """BuilderConfig for Dialog2Flow.

        Args:
          extra_features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
          data_url: `string`, url to download the zip file from.
          citation: `string`, citation for the data set.
          url: `string`, url for information about the data set.
          label_classes: `list[string]`, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            'False' or 'True'.
          **kwargs: keyword arguments forwarded to super.
        """
        super(Dialog2FlowConfig, self).__init__(version=_VERSION, **kwargs)
        self.name = name
        self.citation = citation
        self.url = url


class Dialog2FlowBuilder(GeneratorBasedBuilder):
    BUILDER_CONFIG_CLASS = Dialog2FlowConfig
    BUILDER_CONFIGS = []
    for dataset in DATASETS:
        BUILDER_CONFIGS.append(
            Dialog2FlowConfig(
                name=dataset,
                description="",
                citation=_CITATION,
                url="https://github.com/idiap/dialog2flow",
        ))

    DEFAULT_CONFIG_NAME = "dialog-acts+slots"

    def _info(self):
        if self.config.name in DATASETS_PRETRAIN:
            features = {"utterance": Value("string"), "label": Value("string")}
        else:
            features = {"dialog": [
                {
                    "speaker": Value("string"),
                    "text": Value("string"),
                    "domains": [
                        Value("string")
                    ],
                    "labels": {
                        "dialog_acts": {
                            "acts" : [Value("string")],
                            "main_acts" : [Value("string")],
                            "original_acts" : [Value("string")],
                        },
                        "slots": [Value("string")],
                        "intents": [Value("string")]
                    }
                }
            ]}
        return DatasetInfo(
            description="",
            features=Features(features),
            homepage=self.config.url,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        if self.config.name in DATASETS_PRETRAIN:
            file_path = dl_manager.download({
                "train": f"{self.config.name}.csv",  # full
                "val": "val.csv",  # few shot subset
                # "test": "test.csv",  # TODO: use SpokenWOZ
            })
            splits = [
                SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "file_path": file_path["train"],
                        "split": datasets.Split.TRAIN,
                    },
                ),
                SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "file_path": file_path["val"],
                        "split": datasets.Split.VALIDATION,
                    },
                ),
                # SplitGenerator(  # TODO: use SpokenWOZ
                #     name=datasets.Split.TEST,
                #     gen_kwargs={
                #         "file_path": file_path["test"],
                #         "split": datasets.Split.TEST,
                #     },
                # )
            ]
        else:
            splits = []
            file_path = dl_manager.download({
                "train": os.path.join(self.config.name, "data.json")
            })
            split_names = DATASETS_DS[self.config.name]
            for split_name in split_names:
                splits.append(
                    SplitGenerator(
                        name=SPLIT2NAME[split_name],
                        gen_kwargs={
                            "file_path": file_path["train"],
                            "split": SPLIT2NAME[split_name],
                            "split_name": split_name
                        },
                    )
                )
        return splits

    def _load_json(self, file_path):
        with open(file_path, encoding="utf-8") as f:
            data = json.load(f)
        return data

    def _generate_examples(self, file_path, split, split_name=None):
        if split_name is not None:
            data = self._load_json(file_path)
            data = [(dial_id, dial) for dial_id, dial in data["dialogs"].items() if split_name in dial_id]
            logger.info(f"generating {len(data)} examples from = {split}")
            for dial_id, dial in data:
                yield dial_id, {"dialog": dial}
        else:
            with open(file_path, newline='') as csvfile:
                csvreader = csv.reader(csvfile)
                for ix, row in enumerate(csvreader):
                    yield ix, {"utterance": row[0], "label": row[1]}