# 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 numpy as np

# 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"

# This code was taken from https://gist.github.com/kylebgorman/1081951/bce3de986e4b05fc0b63d4d9e0cfa4bde6664365
def _dist(A, B, insertion, deletion, substitution):
    D = np.zeros((len(A) + 1, len(B) + 1))
    for i in range(len(A)): 
        D[i + 1][0] = D[i][0] + deletion
    for j in range(len(B)): 
        D[0][j + 1] = D[0][j] + insertion
    for i in range(len(A)): # fill out middle of matrix
        for j in range(len(B)):
            if A[i] == B[j]:
                D[i + 1][j + 1] = D[i][j] # aka, it's free. 
            else:
                D[i + 1][j + 1] = min(D[i + 1][j] + insertion,
                                      D[i][j + 1] + deletion,
                                      D[i][j]     + substitution)
    return D

def levenshtein_distance(l1, l2, normalize=False):
    dist = _dist(l1, l2, 1, 1, 1)[-1][-1]
    if normalize:
        return dist / max(len(l1), len(l2))
    else:
        return dist

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

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.ComparisonInfo(
            # This is the description that will appear on the modules page.
            module_type="comparison",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            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 _compute(self, predictions, references, tokenizer=lambda x: x.split(), normalize=False):
        """Returns the scores"""

        dists = []
        for prediction, reference in zip(predictions, references):
            tokenized_prediction = tokenizer(prediction)
            tokenized_reference = tokenizer(reference)
            dists.append(levenshtein_distance(tokenized_prediction, tokenized_reference, normalize=normalize))
        
        avg_dist = np.mean(dists)
        std_dist = np.std(dists)
        
        return {
            "levenshtein_distance": avg_dist,
            "distance_std": std_dist,
            "distances": dists,
        }