"""Print a summary of specialization stats for all files in the
default stats folders.
"""

import collections
import os.path
import opcode
from datetime import date
import itertools
import argparse
import sys

if os.name == "nt":
    DEFAULT_DIR = "c:\\temp\\py_stats\\"
else:
    DEFAULT_DIR = "/tmp/py_stats/"

#Create list of all instruction names
specialized = iter(opcode._specialized_instructions)
opname = ["<0>"]
for name in opcode.opname[1:]:
    if name.startswith("<"):
        try:
            name = next(specialized)
        except StopIteration:
            pass
    opname.append(name)

# opcode_name --> opcode
# Sort alphabetically.
opmap = {name: i for i, name in enumerate(opname)}
opmap = dict(sorted(opmap.items()))

TOTAL = "specialization.deferred", "specialization.hit", "specialization.miss", "execution_count"

def print_specialization_stats(name, family_stats, defines):
    if "specializable" not in family_stats:
        return
    total = sum(family_stats.get(kind, 0) for kind in TOTAL)
    if total == 0:
        return
    with Section(name, 3, f"specialization stats for {name} family"):
        rows = []
        for key in sorted(family_stats):
            if key.startswith("specialization.failure_kinds"):
                continue
            if key in ("specialization.hit", "specialization.miss"):
                label = key[len("specialization."):]
            elif key == "execution_count":
                label = "unquickened"
            elif key in ("specialization.success",  "specialization.failure", "specializable"):
                continue
            elif key.startswith("pair"):
                continue
            else:
                label = key
            rows.append((f"{label:>12}", f"{family_stats[key]:>12}", f"{100*family_stats[key]/total:0.1f}%"))
        emit_table(("Kind", "Count", "Ratio"), rows)
        print_title("Specialization attempts", 4)
        total_attempts = 0
        for key in ("specialization.success",  "specialization.failure"):
            total_attempts += family_stats.get(key, 0)
        rows = []
        for key in ("specialization.success",  "specialization.failure"):
            label = key[len("specialization."):]
            label = label[0].upper() + label[1:]
            val = family_stats.get(key, 0)
            rows.append((label, val, f"{100*val/total_attempts:0.1f}%"))
        emit_table(("", "Count:", "Ratio:"), rows)
        total_failures = family_stats.get("specialization.failure", 0)
        failure_kinds = [ 0 ] * 30
        for key in family_stats:
            if not key.startswith("specialization.failure_kind"):
                continue
            _, index = key[:-1].split("[")
            index =  int(index)
            failure_kinds[index] = family_stats[key]
        failures = [(value, index) for (index, value) in enumerate(failure_kinds)]
        failures.sort(reverse=True)
        rows = []
        for value, index in failures:
            if not value:
                continue
            rows.append((kind_to_text(index, defines, name), value, f"{100*value/total_failures:0.1f}%"))
        emit_table(("Failure kind", "Count:", "Ratio:"), rows)

def gather_stats():
    stats = collections.Counter()
    for filename in os.listdir(DEFAULT_DIR):
        with open(os.path.join(DEFAULT_DIR, filename)) as fd:
            for line in fd:
                try:
                    key, value = line.split(":")
                except ValueError:
                    print (f"Unparsable line: '{line.strip()}' in  {filename}", file=sys.stderr)
                    continue
                key = key.strip()
                value = int(value)
                stats[key] += value
    return stats

def extract_opcode_stats(stats):
    opcode_stats = [ {} for _ in range(256) ]
    for key, value in stats.items():
        if not key.startswith("opcode"):
            continue
        n, _, rest = key[7:].partition("]")
        opcode_stats[int(n)][rest.strip(".")] = value
    return opcode_stats

def parse_kinds(spec_src, prefix="SPEC_FAIL"):
    defines = collections.defaultdict(list)
    start = "#define " + prefix + "_"
    for line in spec_src:
        line = line.strip()
        if not line.startswith(start):
            continue
        line = line[len(start):]
        name, val = line.split()
        defines[int(val.strip())].append(name.strip())
    return defines

def pretty(defname):
    return defname.replace("_", " ").lower()

def kind_to_text(kind, defines, opname):
    if kind < 7:
        return pretty(defines[kind][0])
    if opname.endswith("ATTR"):
        opname = "ATTR"
    if opname.endswith("SUBSCR"):
        opname = "SUBSCR"
    for name in defines[kind]:
        if name.startswith(opname):
            return pretty(name[len(opname)+1:])
    return "kind " + str(kind)

def categorized_counts(opcode_stats):
    basic = 0
    specialized = 0
    not_specialized = 0
    specialized_instructions = {
        op for op in opcode._specialized_instructions
        if "__" not in op and "ADAPTIVE" not in op}
    adaptive_instructions = {
        op for op in opcode._specialized_instructions
        if "ADAPTIVE" in op}
    for i, opcode_stat in enumerate(opcode_stats):
        if "execution_count" not in opcode_stat:
            continue
        count = opcode_stat['execution_count']
        name = opname[i]
        if "specializable" in opcode_stat:
            not_specialized += count
        elif name in adaptive_instructions:
            not_specialized += count
        elif name in specialized_instructions:
            miss = opcode_stat.get("specialization.miss", 0)
            not_specialized += miss
            specialized += count - miss
        else:
            basic += count
    return basic, not_specialized, specialized

def print_title(name, level=2):
    print("#"*level, name)
    print()

class Section:

    def __init__(self, title, level=2, summary=None):
        self.title = title
        self.level = level
        if summary is None:
            self.summary = title.lower()
        else:
            self.summary = summary

    def __enter__(self):
        print_title(self.title, self.level)
        print("<details>")
        print("<summary>", self.summary, "</summary>")
        print()
        return self

    def __exit__(*args):
        print()
        print("</details>")
        print()

def emit_table(header, rows):
    width = len(header)
    header_line = "|"
    under_line = "|"
    for item in header:
        under = "---"
        if item.endswith(":"):
            item = item[:-1]
            under += ":"
        header_line += item + " | "
        under_line += under + "|"
    print(header_line)
    print(under_line)
    for row in rows:
        if width is not None and len(row) != width:
            raise ValueError("Wrong number of elements in row '" + str(rows) + "'")
        print("|", " | ".join(str(i) for i in row), "|")
    print()

def emit_execution_counts(opcode_stats, total):
    with Section("Execution counts", summary="execution counts for all instructions"):
        counts = []
        for i, opcode_stat in enumerate(opcode_stats):
            if "execution_count" in opcode_stat:
                count = opcode_stat['execution_count']
                miss = 0
                if "specializable" not in opcode_stat:
                    miss = opcode_stat.get("specialization.miss")
                counts.append((count, opname[i], miss))
        counts.sort(reverse=True)
        cumulative = 0
        rows = []
        for (count, name, miss) in counts:
            cumulative += count
            if miss:
                miss =  f"{100*miss/count:0.1f}%"
            else:
                miss = ""
            rows.append((name, count, f"{100*count/total:0.1f}%",
                        f"{100*cumulative/total:0.1f}%", miss))
        emit_table(
            ("Name", "Count:", "Self:", "Cumulative:", "Miss ratio:"),
            rows
        )


def emit_specialization_stats(opcode_stats):
    spec_path = os.path.join(os.path.dirname(__file__), "../../Python/specialize.c")
    with open(spec_path) as spec_src:
        defines = parse_kinds(spec_src)
    with Section("Specialization stats", summary="specialization stats by family"):
        for i, opcode_stat in enumerate(opcode_stats):
            name = opname[i]
            print_specialization_stats(name, opcode_stat, defines)

def emit_specialization_overview(opcode_stats, total):
    basic, not_specialized, specialized = categorized_counts(opcode_stats)
    with Section("Specialization effectiveness"):
        emit_table(("Instructions", "Count:", "Ratio:"), (
            ("Basic", basic, f"{basic*100/total:0.1f}%"),
            ("Not specialized", not_specialized, f"{not_specialized*100/total:0.1f}%"),
            ("Specialized", specialized, f"{specialized*100/total:0.1f}%"),
        ))

def emit_call_stats(stats):
    stats_path = os.path.join(os.path.dirname(__file__), "../../Include/pystats.h")
    with open(stats_path) as stats_src:
        defines = parse_kinds(stats_src, prefix="EVAL_CALL")
    with Section("Call stats", summary="Inlined calls and frame stats"):
        total = 0
        for key, value in stats.items():
            if "Calls to" in key:
                total += value
        rows = []
        for key, value in stats.items():
            if "Calls to" in key:
                rows.append((key, value, f"{100*value/total:0.1f}%"))
            elif key.startswith("Calls "):
                name, index = key[:-1].split("[")
                index =  int(index)
                label = name + " (" + pretty(defines[index][0]) + ")"
                rows.append((label, value, f"{100*value/total:0.1f}%"))
        for key, value in stats.items():
            if key.startswith("Frame"):
                rows.append((key, value, f"{100*value/total:0.1f}%"))
        emit_table(("", "Count:", "Ratio:"), rows)

def emit_object_stats(stats):
    with Section("Object stats", summary="allocations, frees and dict materializatons"):
        total_materializations = stats.get("Object new values")
        total_allocations = stats.get("Object allocations")
        total_increfs = stats.get("Object interpreter increfs") + stats.get("Object increfs")
        total_decrefs = stats.get("Object interpreter decrefs") + stats.get("Object decrefs")
        rows = []
        for key, value in stats.items():
            if key.startswith("Object"):
                if "materialize" in key:
                    ratio = f"{100*value/total_materializations:0.1f}%"
                elif "allocations" in key:
                    ratio = f"{100*value/total_allocations:0.1f}%"
                elif "increfs"     in key:
                    ratio = f"{100*value/total_increfs:0.1f}%"
                elif "decrefs"     in key:
                    ratio = f"{100*value/total_decrefs:0.1f}%"
                else:
                    ratio = ""
                label = key[6:].strip()
                label = label[0].upper() + label[1:]
                rows.append((label, value, ratio))
        emit_table(("",  "Count:", "Ratio:"), rows)

def get_total(opcode_stats):
    total = 0
    for opcode_stat in opcode_stats:
        if "execution_count" in opcode_stat:
            total += opcode_stat['execution_count']
    return total

def emit_pair_counts(opcode_stats, total):
    pair_counts = []
    for i, opcode_stat in enumerate(opcode_stats):
        if i == 0:
            continue
        for key, value in opcode_stat.items():
            if key.startswith("pair_count"):
                x, _, _ = key[11:].partition("]")
                if value:
                    pair_counts.append((value, (i, int(x))))
    with Section("Pair counts", summary="Pair counts for top 100 pairs"):
        pair_counts.sort(reverse=True)
        cumulative = 0
        rows = []
        for (count, pair) in itertools.islice(pair_counts, 100):
            i, j = pair
            cumulative += count
            rows.append((opname[i] + " " + opname[j], count, f"{100*count/total:0.1f}%",
                        f"{100*cumulative/total:0.1f}%"))
        emit_table(("Pair", "Count:", "Self:", "Cumulative:"),
            rows
        )
    with Section("Predecessor/Successor Pairs", summary="Top 5 predecessors and successors of each opcode"):
        predecessors = collections.defaultdict(collections.Counter)
        successors = collections.defaultdict(collections.Counter)
        total_predecessors = collections.Counter()
        total_successors = collections.Counter()
        for count, (first, second) in pair_counts:
            if count:
                predecessors[second][first] = count
                successors[first][second] = count
                total_predecessors[second] += count
                total_successors[first] += count
        for name, i in opmap.items():
            total1 = total_predecessors[i]
            total2 = total_successors[i]
            if total1 == 0 and total2 == 0:
                continue
            pred_rows = succ_rows = ()
            if total1:
                pred_rows = [(opname[pred], count, f"{count/total1:.1%}")
                             for (pred, count) in predecessors[i].most_common(5)]
            if total2:
                succ_rows = [(opname[succ], count, f"{count/total2:.1%}")
                             for (succ, count) in successors[i].most_common(5)]
            with Section(name, 3, f"Successors and predecessors for {name}"):
                emit_table(("Predecessors", "Count:", "Percentage:"),
                    pred_rows
                )
                emit_table(("Successors", "Count:", "Percentage:"),
                    succ_rows
                )

def main():
    stats = gather_stats()
    opcode_stats = extract_opcode_stats(stats)
    total = get_total(opcode_stats)
    emit_execution_counts(opcode_stats, total)
    emit_pair_counts(opcode_stats, total)
    emit_specialization_stats(opcode_stats)
    emit_specialization_overview(opcode_stats, total)
    emit_call_stats(stats)
    emit_object_stats(stats)
    print("---")
    print("Stats gathered on:", date.today())

if __name__ == "__main__":
    main()