# 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.
"""Metadata information of all the models available on HuggingFace's modelhub"""


import ast
import csv

import datasets
# Some readme files on modelhub are large in size
csv.field_size_limit(100000000)

_CITATION = """\

"""

_DESCRIPTION = """\
Metadata information of all the models available on HuggingFace's modelhub
"""

_HOMEPAGE = "https://huggingface.co/models"

_LICENSE = ""

_URL = "huggingface-modelhub.csv"



class HuggingfaceModelhub(datasets.GeneratorBasedBuilder):
    """Metadata information of all the models available on HuggingFace's modelhub"""

    VERSION = datasets.Version("1.0.2")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "modelId": datasets.Value("string"),
                    "lastModified": datasets.Value("string"),
                    "tags": datasets.features.Sequence(datasets.Value("string")),
                    "pipeline_tag": datasets.Value("string"),
                    "files": datasets.features.Sequence(datasets.Value("string")),
                    "publishedBy": datasets.Value("string"),
                    "downloads_last_month": datasets.Value("int32"),
                    "library": datasets.Value("string"),
                    "modelCard": datasets.Value("large_string"),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        data_file = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_file,
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""

        with open(filepath, encoding="utf-8") as f:
            reader = csv.reader(f)
            for id_, row in enumerate(reader):
                if id_ == 0:
                    continue
                yield id_, {
                    "modelId": row[0],
                    "lastModified": row[1],
                    "tags": ast.literal_eval(row[2]),
                    "pipeline_tag": row[3],
                    "files": ast.literal_eval(row[4]),
                    "publishedBy": row[5],
                    "downloads_last_month": float(row[6]) if row[6] else 0,
                    "library": row[7],
                    "modelCard": row[8]
                }