import os

import pyarrow as pa
import pyarrow.parquet as pq
import datasets


# Meta infomation
_REPO_NAME = 'Fsoft-AIC/the-vault-inline'

_DESCRIPTION = """The Vault is a multilingual code-text dataset with over 34 million pairs covering 10 popular programming languages. 
It is the largest corpus containing parallel code-text data. By building upon The Stack, a massive raw code sample collection, 
the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a 
high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level. 
The Vault can serve many purposes at multiple levels."""

_HOMEPAGE = "https://huggingface.co/Fsoft-AIC"
_LICENSE = "MIT License"
_CITATION = """
@article{manh2023vault,
  title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
  author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
  journal={arXiv preprint arXiv:2305.06156},
  year={2023}
}
"""
################################################################################################

# Config metadata
_LANG_TO_TEXT = {
    "python": "python",
    "c": "c",
    "c#": "c_sharp",
    "c++": "cpp",
    "go": "go",
    "java": "java",
    "javascript": "javascript",
    "php": "php",
    "ruby": "ruby",
    "rust": "rust",
}       
_LANG_CONFIGS = ["all"] + list(_LANG_TO_TEXT.keys())

_TEXT_TO_LANG = {}
for lang in _LANG_TO_TEXT:
    _TEXT_TO_LANG[_LANG_TO_TEXT[lang]] = lang

num_shard_split = {
    "ruby": 3,
    "c": 29,
    "c_sharp": 25,
    "cpp": 39,
    "go": 15,
    "java": 75,
    "javascript": 6,
    "php": 21,
    "python": 48,
    "rust": 10,
}

################################################################################################

class TheVaultFunctionConfig(datasets.BuilderConfig):
    """BuilderConfig for The Vault dataset."""

    def __init__(self, *args, languages=["all"], **kwargs):
        """BuilderConfig for the The Vault dataset.
        Args:
            languages (:obj:`List[str]`): List of languages to load.
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            name= "+".join([_LANG_TO_TEXT[lang] if lang in _LANG_TO_TEXT else lang for lang in languages]),
            **kwargs,
        )
        
        languages = set([lang.lower() for lang in languages])
        
        assert all([language in _LANG_CONFIGS for language in languages]), f"languages {languages} contains language not in {_LANG_CONFIGS}."

        if "all" in languages:
            assert len(languages)==1, f"Passed 'all' together with other languages. {languages}"
        else:
            languages = [_LANG_TO_TEXT[lang] for lang in languages] # Convert to text name
        
        self.languages = list(languages)


class TheVaultFunction(datasets.GeneratorBasedBuilder):
    """The Vault dataset."""

    VERSION = datasets.Version("1.0.0")
    
    BUILDER_CONFIG_CLASS = TheVaultFunctionConfig
    BUILDER_CONFIGS = [TheVaultFunctionConfig(languages=[lang]) for lang in _LANG_CONFIGS]
    DEFAULT_CONFIG_NAME = "all"

    
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                                            "hexsha": datasets.Value("string"),
                                            "repo": datasets.Value("string"),
                                            "path": datasets.Value("string"), 
                                            "license": datasets.Sequence(datasets.Value("string")),
                                            "language": datasets.Value("string"),
                                            "identifier": datasets.Value("string"),
                                            "code": datasets.Value("string"),
                                            "code_tokens": datasets.Sequence(datasets.Value("string")),
                                            "original_comment": datasets.Value("string"),
                                            "comment": datasets.Value("string"),
                                            "comment_tokens": datasets.Sequence(datasets.Value("string")),
                                            "start_point": datasets.Sequence(datasets.Value("int32")),
                                            "end_point": datasets.Sequence(datasets.Value("int32")),
                                            "prev_context":
                                                {
                                                    "code": datasets.Value("string"),
                                                    "start_point": datasets.Sequence(datasets.Value("int32")),
                                                    "end_point": datasets.Sequence(datasets.Value("int32")),
                                                },
                                            "next_context":
                                                {
                                                    "code": datasets.Value("string"),
                                                    "start_point": datasets.Sequence(datasets.Value("int32")),
                                                    "end_point": datasets.Sequence(datasets.Value("int32")),
                                                },
                                        }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            
        )

    def _split_generators(self, dl_manager):
        generators = []
        languages = self.config.languages

        if "all" in languages:
            languages = list(_LANG_TO_TEXT.values())

        
        split_files = []
        for language in languages:
            num_shards = num_shard_split[language]
            data_files = [
                f"data/train/{language}-{_index:05d}-of-{num_shards:05d}.parquet"
                for _index in range(num_shards)
            ]
            files = dl_manager.download(data_files)
            split_files.extend(files)

        generators.append(
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "files": split_files,
                },
            ),
        )
                
    
        return generators

    def _generate_examples(self, files):
        key = 0
        for file_idx, file in enumerate(files):
            with open(file, "rb") as f:
                parquet_file = pq.ParquetFile(f)
                for batch_idx, record_batch in enumerate(parquet_file.iter_batches(batch_size=10_000)):
                    pa_table = pa.Table.from_batches([record_batch])
                    for row_index in range(pa_table.num_rows):
                        row = pa_table.slice(row_index, 1).to_pydict()
                        
                        yield key, {
                                        "hexsha": row['hexsha'][0],
                                        "repo": row['repo'][0],
                                        "path": row['path'][0], 
                                        "license": row['license'][0], 
                                        "language": row['language'][0],
                                        "identifier": row['identifier'][0],
                                        "code": row['code'][0],
                                        "code_tokens": row['code_tokens'][0],
                                        "original_comment": row['original_comment'][0],
                                        "comment": row['comment'][0],
                                        "comment_tokens": row['comment_tokens'][0],
                                        "start_point": row['start_point'][0],
                                        "end_point": row['end_point'][0],
                                        "prev_context": row['prev_context'][0],
                                        "next_context": row['next_context'][0]
                                    } 
                        key += 1