# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""personalized_passkey_retrieval: a synthetic dataset to evaluate long-context embeddings"""


import json
import gzip
import datasets

logger = datasets.logging.get_logger(__name__)

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{Wang2023ImprovingTE,
  title={Improving Text Embeddings with Large Language Models},
  author={Liang Wang and Nan Yang and Xiaolong Huang and Linjun Yang and Rangan Majumder and Furu Wei},
  year={2023},
}
"""

# You can copy an official description
_DESCRIPTION = """\
This dataset contains synthetic data for personalized passkey retrieval.
It is only intended for evaluation purposes, you should not use it for training.
"""

_URLS = {
    "train": "personalized_passkey_retrieval.jsonl.gz"
}


class Query2docMsmarco(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.1.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name='plain_text', version=VERSION, description='plain text')
    ]

    def _info(self):
        features = datasets.Features(
            {
                "query": datasets.Value("string"),
                "candidates": datasets.features.Sequence(datasets.Value("string")),
                "label": datasets.Value("int32"),
                "context_length": datasets.Value("int32"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download(_URLS)
        print(downloaded_files)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files["train"],
                    "split": "train",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        id_ = 0
        with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
            for line in f:
                if line:
                    example = json.loads(line)
                    yield id_, {
                        "query": example["query"],
                        "candidates": example["candidates"],
                        "label": example["label"],
                        "context_length": example["context_length"],
                    }
                    id_ += 1