# 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 evaluate
import datasets
import re
import dateutil.parser
import numpy as np

import time


# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""


# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predictions to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Returns:
    accuracy: description of the first score,
    another_score: description of the second score,
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("my_new_module")
    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
    >>> print(results)
    {'accuracy': 1.0}
"""

# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class LogMetric(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    # Constant regex to get timestrings
    timestamp_regex = r'(^\d{4}[-/.]\d{2}[-/.]\d{2}(?:[ T]\d{2}[:]\d{2}(?:[:]\d{2}(?:[.,]\d+)?)?(?:Z|[+-]\d{2}[:]\d{2})?)?)'
    timestamp_pattern = re.compile(timestamp_regex, re.MULTILINE)

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            # Both prediction and reference are strings
            features=datasets.Features({
                "predictions": datasets.Value("string", id="sequence"),
                "references": datasets.Value("string", id="sequence"),
            }),
            # Homepage of the module for documentation
            homepage="http://module.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        # TODO: Download external resources if needed
        pass

    def getLogMetric(self, pred : str, ref : str):
        ref = ref.strip(' \t\n\r')
        pred = pred.strip(' \t\n\r')
        
        # Find all timestrings in the log
        pred_timestrings = self.timestamp_pattern.findall(pred)
        ref_timestrings = self.timestamp_pattern.findall(ref)

        #Check if there is the correct amount of timestrings in the prediction
        if(len(pred_timestrings) != len(ref_timestrings)):
            return 0.0
        
        # If there are no timestrings, we must not check anything, we can directly return 1.0
        if (len(pred_timestrings) == 0):
            return 1.0
        
        # replace all digits in the reference timestamp (first timestamp) with '/d' to get
        # a regex that describes the format  
        pred_timestring_pattern = re.sub(r'\d', r'\\d', re.escape(pred_timestrings[0]))
        
        # A variable to save the previous timestamp (as datetime obj) to check monotonicity
        prev_datetime = None
        # Convert matches to datetime objects
        for ts in pred_timestrings:
            try:
                # Check if the format matches with the format of the first timestamp
                matchesPattern = re.fullmatch(pred_timestring_pattern, ts) is not None
                # Check if the timestamps are monotonically increasing
                cur_datetime = dateutil.parser.parse(ts)
                monotonicallyIncreasing = True if prev_datetime == None else prev_datetime <= cur_datetime
                prev_datetime = cur_datetime

                if not (matchesPattern and monotonicallyIncreasing):
                    # timestamps not consistent
                    return 0.0

            except Exception as e:
                # e.g. date format not parsable by dateutil.parser
                return 0.0

        # Correct amt of timestrings, monotonically increasing, consistent + (by dateutil.parser) parsable format
        return 1.0

    def _compute(self, predictions, references):
        """Returns the scores"""

        t_before_logmetric = time.perf_counter()
        timestamp_score = np.mean([self.getLogMetric(p,r) for p,r in zip(predictions,references)])
        t_after_logmetric = time.perf_counter()

        logmetric_duration = f" {t_after_logmetric - t_before_logmetric:0.10f}"

        return {
            "score": timestamp_score,
            "duration": logmetric_duration,
        }