# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import os
import json
import datasets




_CITATION = """\
"""

_DESCRIPTION = """\
"""

_URL = "https://huggingface.co/datasets/Red-8/NER_Gujarati_data/resolve/main/data/datas.zip"
_TRAINING_FILE = "train_data.json"
_DEV_FILE = "val_data.json"
_TEST_FILE = "test_data.json"


class RedConfig(datasets.BuilderConfig):
    """BuilderConfig for Red"""

    def __init__(self, **kwargs):
        """BuilderConfig forRed.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(RedConfig, self).__init__(**kwargs)


class Red(datasets.GeneratorBasedBuilder):
    """Red dataset."""

    BUILDER_CONFIGS = [
        RedConfig(name="NER_Gujarati_data", version=datasets.Version("1.0.0"), description="Red dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-PERIOD",
                                "I-PERIOD",
                                "B-DURATION",
                                "I-DURATION",
                                "B-WEATHER",
                                "I-WEATHER",
                                "B-DIGIT",
                                "I-DIGIT",
                                "B-NUMINAL",
                                "I-NUMINAL",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract(_URL)
        data_files = {
            "train": os.path.join(downloaded_file, _TRAINING_FILE),
            "dev": os.path.join(downloaded_file, _DEV_FILE),
            "test": os.path.join(downloaded_file, _TEST_FILE),
        }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        """Yields examples as (key, example) tuples."""
        with open(filepath,encoding="utf-8") as f:
            for idx_, row in enumerate(f):
                data = json.loads(row)
                yield idx_, {"tokens": data["text"], "ner_tags": data["label"]}