import math
from collections.abc import Iterable

import numpy as np
import sacrebleu
import sklearn.metrics
import random
import evaluate

from lm_eval.api.registry import register_metric, register_aggregation

import logging

eval_logger = logging.getLogger("lm-eval")


class BaseMetric:
    def __init__(
        self,
        aggregation=None,
    ) -> None:
        self.aggregation = aggregation

    def __call__(self, *items):

        sample_wise_score = self.sample_wise_compute(*items)

        if self.aggregation is not None:
            return self.aggregation(sample_wise_score)
        else:
            return self.set_wise_compute(sample_wise_score)

    def sample_wise_compute(self, *items):
        return items

    def set_wise_compute(self, *items):
        return items


# Register Aggregations First
@register_aggregation("mean")
def mean(arr):
    return sum(arr) / len(arr)


@register_aggregation("median")
def median(arr):
    return arr[len(arr) // 2]


@register_metric(
    metric="perplexity",
    higher_is_better=False,
    output_type="loglikelihood",
)
class PerplexityMetric(BaseMetric):
    def sample_wise_compute(self, ll, is_greedy):
        return ll

    def set_wise_compute(self, items):
        return math.exp(-mean(items))


@register_metric(
    metric="acc",
    higher_is_better=True,
    output_type="loglikelihood",
    aggregation="mean",
)
class LoglikelihoodAccMetric(BaseMetric):
    def __call__(self, ll, is_greedy):
        return int(is_greedy)


@register_aggregation("f1")
def f1_score(items):
    unzipped_list = list(zip(*items))
    golds = unzipped_list[0]
    preds = unzipped_list[1]
    fscore = sklearn.metrics.f1_score(golds, preds)

    return np.max(fscore)


@register_aggregation("matthews_corrcoef")
def matthews_corrcoef(items):
    unzipped_list = list(zip(*items))
    golds = unzipped_list[0]
    preds = unzipped_list[1]
    # print(preds)
    return sklearn.metrics.matthews_corrcoef(golds, preds)


@register_aggregation("bleu")
def bleu(items):
    """The Bilingual Evaluation Understudy Score, or BLEU for short, is a metric
    for evaluating a generated sentence to a reference sentence. It counts matching
    n-grams in the candidate translation to n-grams in the reference text, where
    1-gram or unigram would be each token and a bigram comparison would be each
    word pair. The comparison is made regardless of word order
    Source: https://machinelearningmastery.com/calculate-bleu-score-for-text-python/
    Paper: https://www.aclweb.org/anthology/P02-1040/

    Higher is better
    """
    refs = list(zip(*items))[0]
    preds = list(zip(*items))[1]
    refs, preds = _sacreformat(refs, preds)
    return sacrebleu.corpus_bleu(preds, refs).score


@register_aggregation("chrf")
def chrf(items):
    """chrF++ is a tool for automatic evaluation of machine translation output
    based on character n-gram precision and recall enhanced with word n-grams.
    Source: https://github.com/m-popovic/chrF
    Paper: https://www.aclweb.org/anthology/W15-3049.pdf

    Higher is better  # TODO I think
    """
    refs = list(zip(*items))[0]
    preds = list(zip(*items))[1]
    refs, preds = _sacreformat(refs, preds)
    return sacrebleu.corpus_chrf(preds, refs).score


@register_aggregation("ter")
def ter(items):
    """Translation Error Rate is an error metric for machine translation that
    measures the number of edits required to change a system output into one
    of the references
    Source: http://www.cs.umd.edu/~snover/tercom/
    Paper: http://mt-archive.info/AMTA-2006-Snover.pdf

    Lower is better
    """
    refs = list(zip(*items))[0]
    preds = list(zip(*items))[1]
    refs, preds = _sacreformat(refs, preds)
    return sacrebleu.corpus_ter(preds, refs).score


# @register_metric(
#     metric="acc",
#     higher_is_better=True,
#     output_type=["loglikelihood", "multiple_choice"],
#     aggregation="mean",
# )
# def acc_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="acc_norm",
#     higher_is_better=True,
#     output_type=["loglikelihood", "multiple_choice"],
#     aggregation="mean",
# )
# def acc_norm_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="acc_mutual_info",
#     higher_is_better=True,
#     output_type="multiple_choice",
#     aggregation="mean",
# )
# def acc_mutual_info_fn(items):  # This is a passthrough function
#     return items


exact_match = evaluate.load("exact_match")


# @register_metric(
#     metric="exact_match",
#     higher_is_better=True,
#     output_type="generate_until",
#     aggregation="mean",
# )
# def exact_match_fn(**kwargs):
#     return exact_match.compute(**kwargs)


@register_metric(
    metric="word_perplexity",
    higher_is_better=False,
    output_type="loglikelihood_rolling",
)
class BytePerplexityMetric(BaseMetric):
    def sample_wise_compute(self, loglikelihood, _words, _bytes):
        return loglikelihood, _words

    def set_wise_compute(self, items):
        return math.exp(-weighted_mean(items))


@register_metric(
    metric="byte_perplexity",
    higher_is_better=False,
    output_type="loglikelihood_rolling",
)
class BytePerplexityMetric(BaseMetric):
    def sample_wise_compute(self, loglikelihood, _words, _bytes):
        return loglikelihood, _bytes

    def set_wise_compute(self, items):
        return math.exp(-weighted_mean(items))


@register_metric(
    metric="bits_per_byte",
    higher_is_better=False,
    output_type="loglikelihood_rolling",
)
class BitsPerByteMetric(BaseMetric):
    def sample_wise_compute(self, loglikelihood, _words, _bytes):
        return loglikelihood, _bytes

    def set_wise_compute(self, items):
        return -weighted_mean(items) / math.log(2)


def pop_stddev(arr):
    mu = mean(arr)
    return math.sqrt(sum([(x - mu) ** 2 for x in arr]) / len(arr))


def sample_stddev(arr):
    mu = mean(arr)
    return math.sqrt(sum([(x - mu) ** 2 for x in arr]) / (len(arr) - 1))


def mean_stderr(arr):
    return sample_stddev(arr) / math.sqrt(len(arr))


# @register_metric(
#     metric="mcc",
#     higher_is_better=True,
#     output_type="multiple_choice",
#     aggregation="matthews_corrcoef",
# )
# def mcc_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="f1",
#     higher_is_better=True,
#     output_type="multiple_choice",
#     aggregation="f1",
# )
# def f1_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="bleu",
#     higher_is_better=True,
#     output_type="generate_until",
#     aggregation="bleu",
# )
# def bleu_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="chrf",
#     higher_is_better=True,
#     output_type="generate_until",
#     aggregation="chrf",
# )
# def chrf_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="ter",
#     higher_is_better=True,
#     output_type="generate_until",
#     aggregation="ter",
# )
# def ter_fn(items):  # This is a passthrough function
#     return items


# @register_metric(
#     metric="acc_all",
#     higher_is_better=True,
#     output_type="loglikelihood",
#     aggregation="mean",
# )
# def acc_all(items):
#     # Only count as correct if all answers are labeled correctly for each question
#     question_scoring_dict = {}
#     preds = list(zip(*items))[0]
#     docs = list(zip(*items))[1]

#     for doc, pred in zip(docs, preds):
#         paragraph_id = doc["idx"]["paragraph"]
#         question_id = doc["idx"]["question"]
#         if (paragraph_id, question_id) not in question_scoring_dict:
#             question_scoring_dict[(paragraph_id, question_id)] = []

#         gold_label = doc["label"] == 1

#         question_scoring_dict[(paragraph_id, question_id)].append(gold_label == pred)
#     acc = np.mean([int(all(x)) for x in question_scoring_dict.values()])
#     return acc


def acc_all_stderr(items):
    # Only count as correct if all answers are labeled correctly for each question
    question_scoring_dict = {}
    preds = list(zip(*items))[0]
    docs = list(zip(*items))[1]

    for doc, pred in zip(docs, preds):
        question_id = doc["idx"]["question"]
        if question_id not in question_scoring_dict:
            question_scoring_dict[question_id] = []

        gold_label = doc["label"] == 1
        question_scoring_dict[question_id].append(gold_label == pred)

    acc = mean_stderr([int(all(x)) for x in question_scoring_dict.values()])
    return acc


def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
    """Compute max metric between prediction and each ground truth."""
    scores_for_ground_truths = []
    for ground_truth in ground_truths:
        score = metric_fn(prediction, ground_truth)
        scores_for_ground_truths.append(score)
    return max(scores_for_ground_truths)


def weighted_mean(items):
    a, b = zip(*items)
    return sum(a) / sum(b)


def is_non_str_iterable(obj):
    return isinstance(obj, Iterable) and not isinstance(obj, str)


def _sacreformat(refs, preds):
    """Format refs and preds for sacrebleu corpus calculation. It is very particular"""
    # Sacrebleu expects (List[str], List[List[str])
    #   e.g. sacrebleu.corpus_bleu([pred_t], [[ref1_stream], [ref2_stream], ...])

    # Note [ref1_stream] is the first reference for each pred.
    # So lists are size N and (M, N) for N preds and M possible refs for each pred
    # This is a different order of dimensions that I would expect

    # We expect refs to be List[str] or List[List[str]], the outer list corresponding to preds
    # Must become List[List[str]] with the inner list corresponding to preds
    if not is_non_str_iterable(refs):
        refs = list(refs)
    if not is_non_str_iterable(refs[0]):
        refs = [[ref] for ref in refs]
    refs = list(zip(*refs))
    # Note the number of refs in each ref list much match the number of preds

    # We expect preds to be List[str] or List[List[str]]. Must become List[str]
    if not is_non_str_iterable(preds):
        preds = list(preds)
    if is_non_str_iterable(preds[0]):
        assert len(preds[0]) == 1, f"Pred must be a str, was {preds[0]}"
        preds = [pred[0] for pred in preds]

    return refs, preds


# stderr stuff


class _bootstrap_internal:
    def __init__(self, f, n) -> None:
        self.f = f
        self.n = n

    def __call__(self, v):
        i, xs = v
        rnd = random.Random()
        rnd.seed(i)
        res = []
        for _ in range(self.n):
            res.append(self.f(rnd.choices(xs, k=len(xs))))
        return res


def bootstrap_stderr(f, xs, iters):
    import multiprocessing as mp

    pool = mp.Pool(mp.cpu_count())
    # this gives a biased estimate of the stderr (i.e w/ the mean, it gives something
    # equivalent to stderr calculated without Bessel's correction in the stddev.
    # Unfortunately, I haven't been able to figure out what the right correction is
    # to make the bootstrap unbiased - i considered multiplying by sqrt(n/(n-1)) but
    # that would be ad-hoc and I can't prove that that would actually be an unbiased estimator)
    # Thankfully, shouldn't matter because our samples are pretty big usually anyways
    res = []
    chunk_size = min(1000, iters)
    from tqdm import tqdm

    print("bootstrapping for stddev:", f.__name__)
    for bootstrap in tqdm(
        pool.imap(
            _bootstrap_internal(f, chunk_size),
            [(i, xs) for i in range(iters // chunk_size)],
        ),
        total=iters // chunk_size,
    ):
        # sample w replacement
        res.extend(bootstrap)

    pool.close()
    return sample_stddev(res)


def stderr_for_metric(metric, bootstrap_iters):
    bootstrappable = [
        median,
        matthews_corrcoef,
        f1_score,
        perplexity,
        bleu,
        chrf,
        ter,
    ]

    if metric in bootstrappable:
        return lambda x: bootstrap_stderr(metric, x, iters=bootstrap_iters)

    stderr = {mean: mean_stderr, acc_all: acc_all_stderr}

    return stderr.get(metric, None)