# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 deepset GmbH. All Rights Reserved.
#
# 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.

from typing import List, Optional, Any, Dict

import logging
from pathlib import Path
from pipelines.nodes.file_converter import BaseConverter

logger = logging.getLogger(__name__)


class TextConverter(BaseConverter):

    def convert(
        self,
        file_path: Path,
        meta: Optional[Dict[str, str]] = None,
        remove_numeric_tables: Optional[bool] = None,
        valid_languages: Optional[List[str]] = None,
        encoding: Optional[str] = "utf-8",
    ) -> List[Dict[str, Any]]:
        """
        Reads text from a txt file and executes optional preprocessing steps.

        :param file_path: path of the file to convert
        :param meta: dictionary of meta data key-value pairs to append in the returned document.
        :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables.
                                      The tabular structures in documents might be noise for the reader model if it
                                      does not have table parsing capability for finding answers. However, tables
                                      may also have long strings that could possible candidate for searching answers.
                                      The rows containing strings are thus retained in this option.
        :param valid_languages: validate languages from a list of languages specified in the ISO 639-1
                                (https://en.wikipedia.org/wiki/ISO_639-1) format.
                                This option can be used to add test for encoding errors. If the extracted text is
                                not one of the valid languages, then it might likely be encoding error resulting
                                in garbled text.
        :param encoding: Select the file encoding (default is `utf-8`)

        :return: Dict of format {"text": "The text from file", "meta": meta}}

        """
        if remove_numeric_tables is None:
            remove_numeric_tables = self.remove_numeric_tables
        if valid_languages is None:
            valid_languages = self.valid_languages

        with open(file_path, encoding=encoding, errors="ignore") as f:
            text = f.read()
            pages = text.split("\n\n")

        cleaned_pages = []
        for page in pages:
            lines = page.splitlines()
            cleaned_lines = []
            for line in lines:
                words = line.split()
                digits = [
                    word for word in words if any(i.isdigit() for i in word)
                ]

                # remove lines having > 40% of words as digits AND not ending with a period(.)
                if remove_numeric_tables:
                    if words and len(digits) / len(
                            words) > 0.4 and not line.strip().endswith("."):
                        logger.debug(f"Removing line '{line}' from {file_path}")
                        continue

                cleaned_lines.append(line)

            page = "\n".join(cleaned_lines)
            cleaned_pages.append(page)

        if valid_languages:
            document_text = "".join(cleaned_pages)
            if not self.validate_language(document_text, valid_languages):
                logger.warning(
                    f"The language for {file_path} is not one of {valid_languages}. The file may not have "
                    f"been decoded in the correct text format.")
        documents = []
        for page in cleaned_pages:
            document = {"content": page, "content_type": "text", "meta": meta}
            documents.append(document)
        return documents
