"""
Creates a Table 2 using session-level data.
"""
import os
import sys
import nltk
import math
import time
import argparse
import numpy as np
import pandas as pd
from typing import *
from tqdm import tqdm
from queue import Queue
from threading import Thread
import scipy.spatial.distance
import gensim.downloader as api

import evaluation.util
from evaluation import config
import evaluation.embeddings.util as eeu

METRIC_NAMES = [
    "WER",
    "BLEU",
    "COSINE",
    "EMD",
    "RAND_WER",
    "RAND_BLEU",
    "RAND_COSINE",
    "RAND_EMD",
]
SPEAKERS = ["T", "P"]


def main(args):
    """
	Main function which handles program flow, etc.
	:param args: Argparse namespace.
	"""
    if not os.path.exists(config.META_FQN):
        print(f"File does not exist: {config.META_FQN}")
        sys.exit(1)

    # Load the paired file. We want ALL the data.
    paired = evaluation.util.load_paired_json(skip_empty=True)

    # Compute WER and BLEU here.
    hash2metrics = compute_metrics(args, paired)

    # For each hash, determine the values for each dimension of interest.
    hash2dim_values, _ = load_dimensions()

    # For each hash, output its metrics along with its relevant metadata.
    with open(config.TABLE2_FQN, "w") as f:
        # Write the header.
        f.write("hash\tspeaker\tgender\tsess_num\tage\tphq")
        for metric in METRIC_NAMES:
            f.write(f"\t{metric}")
        f.write("\n")

        # for each hash, write the metrics to file.
        for hash_ in hash2metrics:
            for speaker in hash2metrics[hash_]:
                f.write(f"{hash_}\t{speaker}")
                # Some hashes don't have all metadata.
                for dim in config.DIMENSIONS:
                    meta_for_this_hash = hash2dim_values[hash_]
                    if dim in meta_for_this_hash:
                        val = meta_for_this_hash[dim]
                    else:
                        val = ""
                    f.write(f"\t{val}")

                for metric in METRIC_NAMES:
                    value = hash2metrics[hash_][speaker][metric]
                    f.write(f"\t{value}")
                f.write("\n")
    print(config.TABLE2_FQN)


def load_dimensions() -> (Dict[str, Dict[str, int]], Dict[str, List[int]]):
    """
	Loads the metadata file and returns various dimensions for each hash file.
	:return hash2dims: Dictionary with key=hash, value=Dict of dimensions.
	:return unique_dim_vals: For each dimension, contains the unique values.
	"""
    hash2dims = {}
    unique_dim_vals = {x: set() for x in config.DIMENSIONS}
    df = pd.read_csv(config.META_FQN, sep="\t")
    for _, row in df.iterrows():
        hash_ = row["hash"]
        # Skip hashes already seen.
        if hash_ in hash2dims:
            print(f"Duplicate: {hash_}")
            continue
        # Populate the dimension's values.
        values = {}
        for key in config.DIMENSIONS:
            v = row[key]
            if str(v) == "nan":
                continue
            elif isinstance(v, float) or isinstance(v, int):
                v = int(v)
                values[key] = v
                unique_dim_vals[key].add(v)
            elif key == "gender_imputed":
                values[key] = v
                unique_dim_vals[key].add(v)
        hash2dims[hash_] = values

    return hash2dims, unique_dim_vals


def compute_metrics(args, paired: Dict) -> Dict:
    """
	Computes WER and BLEU.

	Args:
		paired: Paired dictionary.
		no_embedding: If True, does not compute embedding metrics.
	"""
    # Load the Word2vec model.
    model, w2v_keys = None, None
    if not args.no_embedding:
        print(f"Loading word2vec model...")
        model = api.load("word2vec-google-news-300")
        w2v_keys = set(model.vocab)

    # Keys: Hash, speaker, metric_name
    hash2metrics: Dict[str, Dict[str, Dict[str, List[float]]]] = {}

    # Create the workload queue.
    q = Queue()
    r = Queue()

    # Concat all sentences for the speaker/patient into a single string.
    # Keys: Hash, speaker. Value: List of phrases.
    gts = {}
    preds = {}
    added_to_queue = set()
    for gid in paired.keys():
        hash_ = paired[gid]["hash"]
        speaker = paired[gid]["speaker"]
        if speaker not in SPEAKERS:
            continue

        if hash_ not in gts:
            gts[hash_] = {s: [] for s in SPEAKERS}
            preds[hash_] = {s: [] for s in SPEAKERS}

        gts[hash_][speaker].append(paired[gid]["gt"])
        preds[hash_][speaker].append(paired[gid]["pred"])

        # Add this (hash, speaker) to the workload queue.
        item = (hash_, speaker)
        if item not in added_to_queue:
            q.put(item)
            added_to_queue.add(item)

    n_elements = q.qsize()
    threads = []
    # Create the threads.
    for _ in range(args.n_threads):
        t = Thread(
            target=worker, args=(args, q, r, model, w2v_keys, gts, preds)
        )
        threads.append(t)

    # Start the threads.
    for t in threads:
        t.start()

    print(f"Waiting for {args.n_threads} threads to finish...")
    pbar_proc = Thread(target=progress_worker, args=(q, n_elements))
    pbar_proc.start()

    # Wait for them to finish.
    for t in threads:
        t.join()

    # Read the result queue and populate our hash2metrics data structure.
    while not r.empty():
        hash_, speaker, metric, value = r.get()

        # If new hash, populate the dict with accumulator lists.
        if hash_ not in hash2metrics:
            hash2metrics[hash_] = {}
            for s in SPEAKERS:
                hash2metrics[hash_][s] = {name: 0 for name in METRIC_NAMES}

        hash2metrics[hash_][speaker][metric] = value

    return hash2metrics


def progress_worker(q: Queue, max_size: int):
    """Updates the progress bar."""
    pbar = tqdm(total=max_size)
    prev_count = 0
    while q.qsize() > 0:
        remaining = q.qsize()
        completed = max_size - remaining
        if completed > prev_count:
            delta = completed - prev_count
            prev_count = completed
            pbar.update(delta)
        time.sleep(0.1)


def worker(
    args, q: Queue, r: Queue, model: Dict, w2v_keys: Set, gts: Dict, preds: Dict
):
    """Multi-threaded implementation of the metrics computation."""
    while not q.empty():
        hash_, speaker = q.get()
        # Create the single strings.
        gt = " ".join(gts[hash_][speaker])
        pred = " ".join(preds[hash_][speaker])
        random_sentence = eeu.random_sentences(1, use_corpus=False)[0]

        # Compute WER and BLEU.
        bleu = nltk.translate.bleu_score.sentence_bleu(
            references=[gt], hypothesis=pred
        )
        wer = evaluation.util.word_error_rate(pred, gt)
        rand_bleu = nltk.translate.bleu_score.sentence_bleu(
            references=[gt], hypothesis=random_sentence
        )
        rand_wer = evaluation.util.word_error_rate(random_sentence, gt)

        # Compute embedding distances.
        n_segments = len(gts[hash_][speaker])
        accumulator = {
            "EMD": [],
            "COSINE": [],
            "RAND_EMD": [],
            "RAND_COSINE": [],
        }

        if not args.no_embedding:
            for j in range(n_segments):
                # Compare the GT vs pred.
                segment_gt = gts[hash_][speaker][j]
                segment_pred = preds[hash_][speaker][j]
                cosine, emd = eeu.compute_distances(
                    model, w2v_keys, segment_gt, segment_pred
                )
                if eeu.is_valid_distance(emd):
                    accumulator["EMD"].append(emd)
                if eeu.is_valid_distance(cosine):
                    accumulator["COSINE"].append(cosine)

                # Generate a random sentence and measure the performance of a random sentence.
                random_sentence = eeu.random_sentences(1, use_corpus=False)[0]
                rand_cosine, rand_emd = eeu.compute_distances(
                    model, w2v_keys, segment_gt, random_sentence
                )
                if eeu.is_valid_distance(rand_emd):
                    accumulator["RAND_EMD"].append(rand_emd)
                if eeu.is_valid_distance(rand_cosine):
                    accumulator["RAND_COSINE"].append(rand_cosine)

            # Compute the session-level mean cosine and EMD.
            for metric in ["EMD", "COSINE", "RAND_EMD", "RAND_COSINE"]:
                avg = np.asarray(accumulator[metric]).mean()
                r.put((hash_, speaker, metric, avg))

        # Store the result.
        r.put((hash_, speaker, "WER", wer))
        r.put((hash_, speaker, "BLEU", bleu))
        r.put((hash_, speaker, "RAND_WER", rand_wer))
        r.put((hash_, speaker, "RAND_BLEU", rand_bleu))


def get_mean_std(
    speaker: str, metric_name: str, accumulator: Dict, dim: str, val: int
):
    """
	Checks if the embedding name, dim, val combo is empty or not in `accumulator`.
	:param speaker: P or T
	:param metric_name: WER or BLEU
	:param accumulator: Accumulator dictionary of distance values.
	:param dim: Dimension name.
	:param val: Dimension value.
	:return mean: Mean value of the array.
	:return std: Standard deviation.
	:return n: Number of elements.
	"""
    values = np.asarray(accumulator[speaker][metric_name][dim][val])
    n = len(values)
    if n == 0:
        return None, None, None
    mean = values.mean()
    std = values.std()
    return mean, std, n


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--n_threads", default=4, type=int, help="Number of threads to use."
    )
    parser.add_argument(
        "--no_embedding",
        action="store_true",
        help="If True, does not compute embeddings.",
    )
    main(parser.parse_args())


"""
Creates a Table 2 using session-level data.
"""
import os
import sys
import nltk
import math
import time
import argparse
import numpy as np
import pandas as pd
from typing import *
from tqdm import tqdm
from queue import Queue
from threading import Thread
import scipy.spatial.distance
import gensim.downloader as api

import evaluation.util
from evaluation import config
import evaluation.embeddings.util as eeu

METRIC_NAMES = [
    "WER",
    "BLEU",
    "COSINE",
    "EMD",
    "RAND_WER",
    "RAND_BLEU",
    "RAND_COSINE",
    "RAND_EMD",
]
SPEAKERS = ["T", "P"]


def main(args):
    """
	Main function which handles program flow, etc.
	:param args: Argparse namespace.
	"""
    if not os.path.exists(config.META_FQN):
        print(f"File does not exist: {config.META_FQN}")
        sys.exit(1)

    # Load the paired file. We want ALL the data.
    paired = evaluation.util.load_paired_json(skip_empty=True)

    # Compute WER and BLEU here.
    hash2metrics = compute_metrics(args, paired)

    # For each hash, determine the values for each dimension of interest.
    hash2dim_values, _ = load_dimensions()

    # For each hash, output its metrics along with its relevant metadata.
    with open(config.TABLE2_FQN, "w") as f:
        # Write the header.
        f.write("hash\tspeaker\tgender\tsess_num\tage\tphq")
        for metric in METRIC_NAMES:
            f.write(f"\t{metric}")
        f.write("\n")

        # for each hash, write the metrics to file.
        for hash_ in hash2metrics:
            for speaker in hash2metrics[hash_]:
                f.write(f"{hash_}\t{speaker}")
                # Some hashes don't have all metadata.
                for dim in config.DIMENSIONS:
                    meta_for_this_hash = hash2dim_values[hash_]
                    if dim in meta_for_this_hash:
                        val = meta_for_this_hash[dim]
                    else:
                        val = ""
                    f.write(f"\t{val}")

                for metric in METRIC_NAMES:
                    value = hash2metrics[hash_][speaker][metric]
                    f.write(f"\t{value}")
                f.write("\n")
    print(config.TABLE2_FQN)


def load_dimensions() -> (Dict[str, Dict[str, int]], Dict[str, List[int]]):
    """
	Loads the metadata file and returns various dimensions for each hash file.
	:return hash2dims: Dictionary with key=hash, value=Dict of dimensions.
	:return unique_dim_vals: For each dimension, contains the unique values.
	"""
    hash2dims = {}
    unique_dim_vals = {x: set() for x in config.DIMENSIONS}
    df = pd.read_csv(config.META_FQN, sep="\t")
    for _, row in df.iterrows():
        hash_ = row["hash"]
        # Skip hashes already seen.
        if hash_ in hash2dims:
            print(f"Duplicate: {hash_}")
            continue
        # Populate the dimension's values.
        values = {}
        for key in config.DIMENSIONS:
            v = row[key]
            if str(v) == "nan":
                continue
            elif isinstance(v, float) or isinstance(v, int):
                v = int(v)
                values[key] = v
                unique_dim_vals[key].add(v)
            elif key == "gender_imputed":
                values[key] = v
                unique_dim_vals[key].add(v)
        hash2dims[hash_] = values

    return hash2dims, unique_dim_vals


def compute_metrics(args, paired: Dict) -> Dict:
    """
	Computes WER and BLEU.

	Args:
		paired: Paired dictionary.
		no_embedding: If True, does not compute embedding metrics.
	"""
    # Load the Word2vec model.
    model, w2v_keys = None, None
    if not args.no_embedding:
        print(f"Loading word2vec model...")
        model = api.load("word2vec-google-news-300")
        w2v_keys = set(model.vocab)

    # Keys: Hash, speaker, metric_name
    hash2metrics: Dict[str, Dict[str, Dict[str, List[float]]]] = {}

    # Create the workload queue.
    q = Queue()
    r = Queue()

    # Concat all sentences for the speaker/patient into a single string.
    # Keys: Hash, speaker. Value: List of phrases.
    gts = {}
    preds = {}
    added_to_queue = set()
    for gid in paired.keys():
        hash_ = paired[gid]["hash"]
        speaker = paired[gid]["speaker"]
        if speaker not in SPEAKERS:
            continue

        if hash_ not in gts:
            gts[hash_] = {s: [] for s in SPEAKERS}
            preds[hash_] = {s: [] for s in SPEAKERS}

        gts[hash_][speaker].append(paired[gid]["gt"])
        preds[hash_][speaker].append(paired[gid]["pred"])

        # Add this (hash, speaker) to the workload queue.
        item = (hash_, speaker)
        if item not in added_to_queue:
            q.put(item)
            added_to_queue.add(item)

    n_elements = q.qsize()
    threads = []
    # Create the threads.
    for _ in range(args.n_threads):
        t = Thread(
            target=worker, args=(args, q, r, model, w2v_keys, gts, preds)
        )
        threads.append(t)

    # Start the threads.
    for t in threads:
        t.start()

    print(f"Waiting for {args.n_threads} threads to finish...")
    pbar_proc = Thread(target=progress_worker, args=(q, n_elements))
    pbar_proc.start()

    # Wait for them to finish.
    for t in threads:
        t.join()

    # Read the result queue and populate our hash2metrics data structure.
    while not r.empty():
        hash_, speaker, metric, value = r.get()

        # If new hash, populate the dict with accumulator lists.
        if hash_ not in hash2metrics:
            hash2metrics[hash_] = {}
            for s in SPEAKERS:
                hash2metrics[hash_][s] = {name: 0 for name in METRIC_NAMES}

        hash2metrics[hash_][speaker][metric] = value

    return hash2metrics


def progress_worker(q: Queue, max_size: int):
    """Updates the progress bar."""
    pbar = tqdm(total=max_size)
    prev_count = 0
    while q.qsize() > 0:
        remaining = q.qsize()
        completed = max_size - remaining
        if completed > prev_count:
            delta = completed - prev_count
            prev_count = completed
            pbar.update(delta)
        time.sleep(0.1)


def worker(
    args, q: Queue, r: Queue, model: Dict, w2v_keys: Set, gts: Dict, preds: Dict
):
    """Multi-threaded implementation of the metrics computation."""
    while not q.empty():
        hash_, speaker = q.get()
        # Create the single strings.
        gt = " ".join(gts[hash_][speaker])
        pred = " ".join(preds[hash_][speaker])
        random_sentence = eeu.random_sentences(1, use_corpus=False)[0]

        # Compute WER and BLEU.
        bleu = nltk.translate.bleu_score.sentence_bleu(
            references=[gt], hypothesis=pred
        )
        wer = evaluation.util.word_error_rate(pred, gt)
        rand_bleu = nltk.translate.bleu_score.sentence_bleu(
            references=[gt], hypothesis=random_sentence
        )
        rand_wer = evaluation.util.word_error_rate(random_sentence, gt)

        # Compute embedding distances.
        n_segments = len(gts[hash_][speaker])
        accumulator = {
            "EMD": [],
            "COSINE": [],
            "RAND_EMD": [],
            "RAND_COSINE": [],
        }

        if not args.no_embedding:
            for j in range(n_segments):
                # Compare the GT vs pred.
                segment_gt = gts[hash_][speaker][j]
                segment_pred = preds[hash_][speaker][j]
                cosine, emd = eeu.compute_distances(
                    model, w2v_keys, segment_gt, segment_pred
                )
                if eeu.is_valid_distance(emd):
                    accumulator["EMD"].append(emd)
                if eeu.is_valid_distance(cosine):
                    accumulator["COSINE"].append(cosine)

                # Generate a random sentence and measure the performance of a random sentence.
                random_sentence = eeu.random_sentences(1, use_corpus=False)[0]
                rand_cosine, rand_emd = eeu.compute_distances(
                    model, w2v_keys, segment_gt, random_sentence
                )
                if eeu.is_valid_distance(rand_emd):
                    accumulator["RAND_EMD"].append(rand_emd)
                if eeu.is_valid_distance(rand_cosine):
                    accumulator["RAND_COSINE"].append(rand_cosine)

            # Compute the session-level mean cosine and EMD.
            for metric in ["EMD", "COSINE", "RAND_EMD", "RAND_COSINE"]:
                avg = np.asarray(accumulator[metric]).mean()
                r.put((hash_, speaker, metric, avg))

        # Store the result.
        r.put((hash_, speaker, "WER", wer))
        r.put((hash_, speaker, "BLEU", bleu))
        r.put((hash_, speaker, "RAND_WER", rand_wer))
        r.put((hash_, speaker, "RAND_BLEU", rand_bleu))


def get_mean_std(
    speaker: str, metric_name: str, accumulator: Dict, dim: str, val: int
):
    """
	Checks if the embedding name, dim, val combo is empty or not in `accumulator`.
	:param speaker: P or T
	:param metric_name: WER or BLEU
	:param accumulator: Accumulator dictionary of distance values.
	:param dim: Dimension name.
	:param val: Dimension value.
	:return mean: Mean value of the array.
	:return std: Standard deviation.
	:return n: Number of elements.
	"""
    values = np.asarray(accumulator[speaker][metric_name][dim][val])
    n = len(values)
    if n == 0:
        return None, None, None
    mean = values.mean()
    std = values.std()
    return mean, std, n


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--n_threads", default=4, type=int, help="Number of threads to use."
    )
    parser.add_argument(
        "--no_embedding",
        action="store_true",
        help="If True, does not compute embeddings.",
    )
    main(parser.parse_args())


Creates a Table 2 using session-level data. Main function which handles program flow, etc. 	:param args: Argparse namespace. # Load the paired file. We want ALL the data. # Compute WER and BLEU here. # For each hash, determine the values for each dimension of interest. # For each hash, output its metrics along with its relevant metadata. # Write the header. # for each hash, write the metrics to file. # Some hashes don't have all metadata. Loads the metadata file and returns various dimensions for each hash file. 	:return hash2dims: Dictionary with key=hash, value=Dict of dimensions. 	:return unique_dim_vals: For each dimension, contains the unique values. # Skip hashes already seen. # Populate the dimension's values. Computes WER and BLEU.  	Args: 		paired: Paired dictionary. 		no_embedding: If True, does not compute embedding metrics. # Load the Word2vec model. # Keys: Hash, speaker, metric_name # Create the workload queue. # Concat all sentences for the speaker/patient into a single string. # Keys: Hash, speaker. Value: List of phrases. # Add this (hash, speaker) to the workload queue. # Create the threads. # Start the threads. # Wait for them to finish. # Read the result queue and populate our hash2metrics data structure. # If new hash, populate the dict with accumulator lists. Updates the progress bar. Multi-threaded implementation of the metrics computation. # Create the single strings. # Compute WER and BLEU. # Compute embedding distances. # Compare the GT vs pred. # Generate a random sentence and measure the performance of a random sentence. # Compute the session-level mean cosine and EMD. # Store the result. Checks if the embedding name, dim, val combo is empty or not in `accumulator`. 	:param speaker: P or T 	:param metric_name: WER or BLEU 	:param accumulator: Accumulator dictionary of distance values. 	:param dim: Dimension name. 	:param val: Dimension value. 	:return mean: Mean value of the array. 	:return std: Standard deviation. 	:return n: Number of elements.