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# Lint as: python3
"""Reuters 21578"""


from textwrap import dedent

import datasets


_HOMEPAGE = "https://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection"

_CITATION = """\
@article{APTE94,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}

@inproceedings{APTE94b,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Toward Language Independent Automated Learning of Text Categorization Models},
booktitle = {sigir94},
year = {1994},
note = {To appear.}
}

@inproceedings{HAYES8},
author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and
Linda M. Schmandt},
title = {{TCS}: A Shell for Content-Based Text Categorization},
booktitle = {IEEE Conference on Artificial Intelligence Applications},
year = {1990}
}

@inproceedings{HAYES90b,
author = {Philip J. Hayes and Steven P. Weinstein},
title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a
Database of News Stories},
booktitle = {Second Annual Conference on Innovative Applications of
Artificial Intelligence},
year = {1990}
}

@incollection{HAYES92 ,
author = {Philip J. Hayes},
title = {Intelligent High-Volume Text Processing using Shallow,
Domain-Specific Techniques},
booktitle = {Text-Based Intelligent Systems},
publisher = {Lawrence Erlbaum},
address =  {Hillsdale, NJ},
year = {1992},
editor = {Paul S. Jacobs}
}

@inproceedings{LEWIS91c ,
author = {David D. Lewis},
title = {Evaluating Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1991},
month = {feb},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {312--318}

}

@phdthesis{LEWIS91d,
author = {David Dolan Lewis},
title = {Representation and Learning in Information Retrieval},
school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003},
year = 1992},
note = {Technical Report 91--93.}
}

@inproceedings{LEWIS91e,
author = {David D. Lewis},
title = {Data Extraction as Text Categorization: An Experiment with
the {MUC-3} Corpus},
booktitle = {Proceedings of the Third Message Understanding Evaluation
and Conference},
year = {1991},
month = {may},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}

}

@inproceedings{LEWIS92b,
author = {David D. Lewis},
title = {An Evaluation of Phrasal and Clustered Representations on a Text
Categorization Task},
booktitle = {Fifteenth Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval},
year = {1992},
pages = {37--50}
}

@inproceedings{LEWIS92d ,
author = {David D. Lewis and Richard M. Tong},
title = {Text Filtering in {MUC-3} and {MUC-4}},
booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})},
year = {1992},
month = {jun},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}

@inproceedings{LEWIS92e,
author = {David D. Lewis},
title = {Feature Selection and Feature Extraction for Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1992},
month = {feb} ,
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {212--217}
}

@inproceedings{LEWIS94b,
author = {David D. Lewis and Marc Ringuette},
title = {A Comparison of Two Learning Algorithms for Text Categorization},
booktitle = {Symposium on Document Analysis and Information Retrieval},
year = {1994},
organization = {ISRI; Univ. of Nevada, Las Vegas},
address = {Las Vegas, NV},
month = {apr},
pages = {81--93}
}

@article{LEWIS94d,
author = {David D. Lewis and Philip J. Hayes},
title = {Guest Editorial},
journal = {ACM Transactions on Information Systems},
year = {1994},
volume  = {12},
number  = {3},
pages = {231},
month = {jul}
}

@article{SPARCKJONES76,
author = {K. {Sparck Jones} and  C. J. {van Rijsbergen}},
title =  {Information Retrieval Test Collections},
journal = {Journal of Documentation},
year = {1976},
volume = {32},
number = {1},
pages = {59--75}
}

@book{WEISS91,
author = {Sholom M. Weiss and Casimir A. Kulikowski},
title = {Computer Systems That Learn},
publisher = {Morgan Kaufmann},
year = {1991},
address = {San Mateo, CA}
}
"""

_DESCRIPTION = """\
The Reuters-21578 dataset  is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987.
"""

_DATA_URL = "data/reuters21578.tar.gz"


class Reuters21578Config(datasets.BuilderConfig):
    """BuilderConfig for reuters-21578."""

    def __init__(self, **kwargs):
        """BuilderConfig for Reuters21578.

        Args:
        **kwargs: keyword arguments forwarded to super.
        """
        super(Reuters21578Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


class Reuters21578(datasets.GeneratorBasedBuilder):
    """Reuters 21578"""

    BUILDER_CONFIGS = [
        Reuters21578Config(
            name="ModHayes",
            description=dedent(
                """Training Set (20856 docs): CGISPLIT="TRAINING-SET"
                                Test Set (722 docs): CGISPLIT="PUBLISHED-TESTSET"
                                Unused (0 docs)"""
            ),
        ),
        Reuters21578Config(
            name="ModLewis",
            description=dedent(
                """Training Set (13,625 docs): LEWISSPLIT="TRAIN";  TOPICS="YES" or "NO"
                                Test Set (6,188 docs):  LEWISSPLIT="TEST"; TOPICS="YES" or "NO"
                                Unused (1,765): LEWISSPLIT="NOT-USED" or TOPICS="BYPASS"""
            ),
        ),
        Reuters21578Config(
            name="ModApte",
            description=dedent(
                """Training Set (9,603 docs): LEWISSPLIT="TRAIN";  TOPICS="YES"
                                Test Set (3,299 docs): LEWISSPLIT="TEST"; TOPICS="YES"
                                Unused (8,676 docs):   LEWISSPLIT="NOT-USED"; TOPICS="YES" or TOPICS="NO" or TOPICS="BYPASS" """
            ),
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "text_type": datasets.Value("string"),
                    "topics": datasets.Sequence(datasets.Value("string")),
                    "lewis_split": datasets.Value("string"),
                    "cgis_split": datasets.Value("string"),
                    "old_id": datasets.Value("string"),
                    "new_id": datasets.Value("string"),
                    "places": datasets.Sequence(datasets.Value("string")),
                    "people": datasets.Sequence(datasets.Value("string")),
                    "orgs": datasets.Sequence(datasets.Value("string")),
                    "exchanges": datasets.Sequence(datasets.Value("string")),
                    "date": datasets.Value("string"),
                    "title": datasets.Value("string"),
                }
            ),
            # No default supervised_keys (as we have to pass both premise
            # and hypothesis as input).
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        archive = dl_manager.download(_DATA_URL)
        filepaths = ["reut2-" + "%03d" % i + ".sgm" for i in range(22)]
        if self.config.name == "ModHayes":
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "filepaths": filepaths,
                        "split": "PUBLISHED-TESTSET",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepaths": filepaths,
                        "split": "TRAINING-SET",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
            ]
        else:
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={"filepaths": filepaths, "split": "TEST", "files": dl_manager.iter_archive(archive)},
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"filepaths": filepaths, "split": "TRAIN", "files": dl_manager.iter_archive(archive)},
                ),
                datasets.SplitGenerator(
                    name="unused",
                    gen_kwargs={
                        "filepaths": filepaths,
                        "split": "NOT-USED",
                        "files": dl_manager.iter_archive(archive),
                    },
                ),
            ]

    def _generate_examples(self, filepaths, split, files):
        """This function returns the examples in the raw (text) form."""
        for path, f in files:
            if path in filepaths:
                # only the file reut2-017 has one line non UTF-8 encoded so we can ignore it
                line = f.readline().decode("utf-8", errors="ignore")
                while line:
                    if line.startswith("<REUTERS"):
                        lewis_split = ""
                        cgis_split = ""
                        old_id = ""
                        new_id = ""
                        topics = []
                        places = []
                        people = []
                        orgs = []
                        exchanges = []
                        date = ""
                        title = ""
                        text = ""
                        text_type = ""
                        line = line.split()
                        lewis_split = line[2].split("=")[1]
                        cgis_split = line[3].split("=")[1]
                        old_id = line[4].split("=")[1]
                        new_id = line[5].split("=")[1][:-1]
                        has_topic = line[1].split("=")[1]
                        line = f.readline().decode("utf-8", errors="ignore")
                        if (
                            (self.config.name == "ModHayes" and split not in cgis_split)
                            or (
                                self.config.name == "ModLewis"
                                and (
                                    (split not in lewis_split)
                                    or (split == "TRAIN" and has_topic not in ['"YES"', '"NO"'])
                                    or (split == "TEST" and has_topic not in ['"YES"', '"NO"'])
                                    or (split == "NOT-USED" and has_topic not in ['"YES"', '"NO"', '"BYPASS"'])
                                )
                            )
                            or (
                                self.config.name == "ModApte"
                                and (
                                    split not in lewis_split
                                    or (split == "TRAIN" and has_topic != '"YES"')
                                    or (split == "TEST" and has_topic != '"YES"')
                                    or (split == "NOT-USED" and has_topic not in ['"YES"', '"NO"', '"BYPASS"'])
                                )
                            )
                        ):  # skip example that are not in the current split
                            li = line
                            while li and not li.startswith("<REUTERS"):
                                li = f.readline().decode("utf-8", errors="ignore")
                            if li:
                                line = li
                    elif line.startswith("<TOPICS>"):
                        if line.replace("\n", "") != "<TOPICS></TOPICS>":
                            line = line.split("<D>")
                            topics = [topic.replace("</D>", "") for topic in line[1:]]
                            topics = [topic.replace("</TOPICS>\n", "") for topic in topics]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<PLACES>"):
                        if line.replace("\n", "") != "<PLACES></PLACES>":
                            line = line.split("<D>")
                            places = [place.replace("</D>", "") for place in line[1:]]
                            places = [place.replace("</PLACES>\n", "") for place in places]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<PEOPLE>"):
                        if line.replace("\n", "") != "<PEOPLE></PEOPLE>":
                            line = line.split("<D>")
                            people = [p.replace("</D>", "") for p in line[1:]]
                            people = [p.replace("</PEOPLE>\n", "") for p in people]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<ORGS>"):
                        if line.replace("\n", "") != "<ORGS></ORGS>":
                            line = line.split("<D>")
                            orgs = [org.replace("</D>", "") for org in line[1:]]
                            orgs = [org.replace("</ORGS>\n", "") for org in orgs]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<EXCHANGES>"):
                        if line.replace("\n", "") != "<EXCHANGES></EXCHANGES>":
                            line = line.split("<D>")
                            exchanges = [ex.replace("</D>", "") for ex in line[1:]]
                            exchanges = [ex.replace("</EXCHANGES>\n", "") for ex in exchanges]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<DATE>"):
                        date = line.replace("\n", "")
                        date = line[6:-8]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<TITLE>"):
                        title = line[7:-9]
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif "*<TITLE>" in line:
                        # These lines start with a variable number of * chars
                        title = line.split("*<TITLE>")[1][:-1]
                        line = f.readline().decode("utf-8", errors="ignore")
                        while "</TITLE>" not in line:
                            # Convert any \n in TYPE="BRIEF" text to spaces to match other titles
                            title += " " + line[:-1]
                            line = f.readline().decode("utf-8", errors="ignore")
                    elif "<BODY>" in line:
                        text = line.split("<BODY>")[1]
                        line = f.readline().decode("utf-8", errors="ignore")
                        while "</BODY>" not in line:
                            text += line
                            line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith('<TEXT TYPE="UNPROC">'):
                        text_type = '"UNPROC"'
                        text = line[20:]
                        line = f.readline().decode("utf-8", errors="ignore")
                        while "</TEXT>" not in line:
                            text += line
                            line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith('<TEXT TYPE="BRIEF">'):
                        text_type = '"BRIEF"'
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("<TEXT>"):
                        text_type = '"NORM"'
                        line = f.readline().decode("utf-8", errors="ignore")
                    elif line.startswith("</REUTERS>"):
                        yield new_id, {
                            "lewis_split": lewis_split,
                            "cgis_split": cgis_split,
                            "old_id": old_id,
                            "new_id": new_id,
                            "topics": topics,
                            "places": places,
                            "people": people,
                            "orgs": orgs,
                            "exchanges": exchanges,
                            "date": date,
                            "title": title,
                            "text": text,
                            "text_type": text_type,
                        }
                        line = f.readline().decode("utf-8", errors="ignore")
                    else:
                        line = f.readline().decode("utf-8", errors="ignore")