# 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.
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# limitations under the License.

"""RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems"""

import gzip
import pickle
import textwrap
import datasets

_CITATION = """\
@misc{liu2023repobench,
      title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems}, 
      author={Tianyang Liu and Canwen Xu and Julian McAuley},
      year={2023},
      eprint={2306.03091},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
RepoBench is a dataset that benchmarks repository-level code auto-completion systems.

RepoBench-P denotes RepoBench for pipeline, 
which is subtask of RepoBench including both relevant code retrieval and next-line code prediction.
"""

_HOMEPAGE = "https://github.com/Leolty/repobench"

_LICENSE = "Apache License 2.0"

_URLs = {
    "python": "https://raw.githubusercontent.com/Leolty/repobench/main/data/pipeline/python/",
    "java": "https://raw.githubusercontent.com/Leolty/repobench/main/data/pipeline/java/"
}

class RepoBenchP(datasets.GeneratorBasedBuilder):
    """RepoBench"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="python",
            description=textwrap.dedent(
                """
                This part of RepoBench-P is for python language.
                """
            )
        ),
        datasets.BuilderConfig(
            name="java",
            description=textwrap.dedent(
                """
                This part of RepoBench-P is for java language.
                """
            )
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "repo_name": datasets.Value("string"),
                "file_path": datasets.Value("string"),
                "context": [{
                    "path": datasets.Value("string"),
                    "identifier": datasets.Value("string"),
                    "snippet": datasets.Value("string")
                }],
                "import_statement": datasets.Value("string"),
                "code": datasets.Value("string"),
                "next_line": datasets.Value("string"),
                "gold_snippet_index": datasets.Value("int32"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        config_urls = _URLs[self.config.name]
        data_dir = dl_manager.download({
            "cff": config_urls + "cross_file_first.gz",
            "cfr": config_urls + "cross_file_random.gz",
            "if": config_urls + "in_file.gz"
        }
        )

        return [
            datasets.SplitGenerator(
                name=datasets.Split("cff"),
                gen_kwargs={"data_dir": data_dir, "split": "cff"}
            ),
            datasets.SplitGenerator(
                name=datasets.Split("cfr"),
                gen_kwargs={"data_dir": data_dir, "split": "cfr"}
            ),
            datasets.SplitGenerator(
                name=datasets.Split("if"),
                gen_kwargs={"data_dir": data_dir, "split": "if"}
            )
        ]

    def _generate_examples(self, data_dir, split):
        """ Yields examples. """
        with gzip.open(data_dir[split], "rb") as f:
            data = pickle.load(f)

        if split == "cff" or split == "cfr":
            for i, example in enumerate(data):

                yield i, {
                    "repo_name": example["repo_name"],
                    "file_path": example["file_path"],
                    "context": example["context"],
                    "import_statement": example["import_statement"],
                    "code": example["code"],
                    "next_line": example["next_line"],
                    "gold_snippet_index": example["gold_snippet_index"]
                }
        
        elif split == "if":
            for i, example in enumerate(data):

                yield i, {
                    "repo_name": example["repo_name"],
                    "file_path": example["file_path"],
                    "context": example["context"],
                    "import_statement": example["import_statement"],
                    "code": example["code"],
                    "next_line": example["next_line"],
                    # in-file data has no gold_snippet_index
                    "gold_snippet_index": -1
                }