# coding=utf-8
# 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.
"""TODO: Add a description here."""


import csv
import json
import os

import datasets
import pickle 


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
languages=['python','javascript','java','go']
_URLs = {lang:f'https://funcdef.s3.amazonaws.com/{lang}.tar.gz' for lang in languages}
_URLs['all']=_URLs.copy()


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class FundDefDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="all", version=VERSION, description="All available data"),
        datasets.BuilderConfig(name="python", version=VERSION, description="Python data"),
        datasets.BuilderConfig(name="javascript", version=VERSION, description="Javascript data"),
        datasets.BuilderConfig(name="java", version=VERSION, description="Java data"),
        datasets.BuilderConfig(name="go", version=VERSION, description="Go data"),
    ]

    DEFAULT_CONFIG_NAME = "all"

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        
        features = datasets.Features(
            {
                "repository_name": datasets.Value("string"),
                "function_path": datasets.Value("string"),
                "function_identifier": datasets.Value("string"),
                "language": datasets.Value("string"),
                "function": datasets.Sequence(datasets.Value("string")),
                "docstring": datasets.Value("string"),
                "function_url": datasets.Value("string"),
                "license":datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,  # Here we define them above because they are different between the two configurations
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        my_urls = _URLs[self.config.name]
        if isinstance(my_urls, str):
            my_urls = {self.config.name:my_urls}
        data_dir = [os.path.join(lang_dir,lang) for lang,lang_dir in dl_manager.download_and_extract(my_urls).items()]
        splitpaths={split:os.path.join(lang_dir,f'{split}'.bin) for lang_dir in data_dir for split in ['train','valid','test']}

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": splitpaths['train'],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": splitpaths['test'],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": splitpaths['valid'],
                    "split": "valid",
                },
            ),
        ]

    def _generate_examples(
        self, filepaths,split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        count=-1
        for i,filepath in enumerate(filepaths):
            loaded_f=pickle.load(open(filepath,'rb'))
            for j, func in enumerate(loaded_f):
                count+=1
                yield count,{
                    "repository_name": func['nwo'],
                    "function_path":func['path'],
                    "function_identifier": func['identifier'],
                    "language": func['language'],
                    "function": func['function'],
                    "docstring": func['docstring'],
                    "function_url": func['url'],
                    "license":func['license'], 
                }