"""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

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:
                key, value = line.split(":")
                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):
    defines = collections.defaultdict(list)
    for line in spec_src:
        line = line.strip()
        if not line.startswith("#define SPEC_FAIL_"):
            continue
        line = line[len("#define SPEC_FAIL_"):]
        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"
    if opname.startswith("PRECALL"):
        opname = "CALL"
    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):
    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}%"))
        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 = stats.get("Object new values")
        rows = []
        for key, value in stats.items():
            if key.startswith("Object"):
                if "materialize" in key:
                    materialize = f"{100*value/total:0.1f}%"
                else:
                    materialize = ""
                label = key[6:].strip()
                label = label[0].upper() + label[1:]
                rows.append((label, value, materialize))
        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 3 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(3)]
            if total2:
                succ_rows = [(opname[succ], count, f"{count/total2:.1%}")
                             for (succ, count) in successors[i].most_common(3)]
            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()

# SIG # Begin Windows Authenticode signature block
# MIIoZwYJKoZIhvcNAQcCoIIoWDCCKFQCAQExDzANBglghkgBZQMEAgEFADB5Bgor
# BgEEAYI3AgEEoGswaTA0BgorBgEEAYI3AgEeMCYCAwEAAAQQse8BENmB6EqSR2hd
# JGAGggIBAAIBAAIBAAIBAAIBADAxMA0GCWCGSAFlAwQCAQUABCDb7pL16ljbBFoh
# 3pN5iYIFv3uQ6Vs4rzkL+lhDYhvlmKCCDZowggYYMIIEAKADAgECAhMzAAAEbLAG
# JNKSnG/gAAAAAARsMA0GCSqGSIb3DQEBCwUAMH4xCzAJBgNVBAYTAlVTMRMwEQYD
# VQQIEwpXYXNoaW5ndG9uMRAwDgYDVQQHEwdSZWRtb25kMR4wHAYDVQQKExVNaWNy
# b3NvZnQgQ29ycG9yYXRpb24xKDAmBgNVBAMTH01pY3Jvc29mdCBDb2RlIFNpZ25p
# bmcgUENBIDIwMTEwHhcNMjUwNTE1MTg0ODMwWhcNMjYwNzA3MTg0ODMwWjCBiDEL
# MAkGA1UEBhMCVVMxEzARBgNVBAgTCldhc2hpbmd0b24xEDAOBgNVBAcTB1JlZG1v
# bmQxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEyMDAGA1UEAxMpTWlj
# cm9zb2Z0IDNyZCBQYXJ0eSBBcHBsaWNhdGlvbiBDb21wb25lbnQwggEiMA0GCSqG
# SIb3DQEBAQUAA4IBDwAwggEKAoIBAQCz70xLkah79Z5HXrDk1IFHHkud7j1kZefc
# eEdQnR0UWU5WlL2RhCftii4Ex1xQLBh5Y26nOVYVSUfWILNwjCaq+N0t7V38qk5h
# fQ0H4rulaBkgzkg0fsp89iXCh3YPMEUe17iJZlGSF0is16PaZ15wlhxf4eXo6fO5
# t+k1hAp2dvjBQEUhQZQjpX950u8kk7c/aTc4uU+S/ziWzKvsdp28qIyFe2Q8fZ6y
# nsANHvXlrPjJ7q12gcTKogtgSJUye3ISuwOjbBsMv5ifXMPMDqzMNJGKY1Z3iaIv
# JSKfyyJkdJkXbvLQMLgsytToUklgk2k+kAGsDc+fVPLUYPX02nsRAgMBAAGjggGC
# MIIBfjAfBgNVHSUEGDAWBgorBgEEAYI3TBEBBggrBgEFBQcDAzAdBgNVHQ4EFgQU
# +THphZBFVCz14Kvwgr3e/s1pM1gwVAYDVR0RBE0wS6RJMEcxLTArBgNVBAsTJE1p
# Y3Jvc29mdCBJcmVsYW5kIE9wZXJhdGlvbnMgTGltaXRlZDEWMBQGA1UEBRMNMjMx
# NTIyKzUwNTEyMzAfBgNVHSMEGDAWgBRIbmTlUAXTgqoXNzcitW2oynUClTBUBgNV
# HR8ETTBLMEmgR6BFhkNodHRwOi8vd3d3Lm1pY3Jvc29mdC5jb20vcGtpb3BzL2Ny
# bC9NaWNDb2RTaWdQQ0EyMDExXzIwMTEtMDctMDguY3JsMGEGCCsGAQUFBwEBBFUw
# UzBRBggrBgEFBQcwAoZFaHR0cDovL3d3dy5taWNyb3NvZnQuY29tL3BraW9wcy9j
# ZXJ0cy9NaWNDb2RTaWdQQ0EyMDExXzIwMTEtMDctMDguY3J0MAwGA1UdEwEB/wQC
# MAAwDQYJKoZIhvcNAQELBQADggIBAJ7qVkCcWiACY/XmN1Xc6BbhlX1/sLOxN1zU
# /6fIXCSBuDZSRNyRnL8oUq3EsMW/5bYaibkKP/fggdYpUS8EQ+PMUDDRfAVPOR8e
# z/YzVKsWdYc6PbBsGguSOQMR4c5hfdXMlIhlM/hUK3mYNO4TBTjHkX83Q3VyxQln
# wxvGrvISQf1MYOoGvEDq0YeNJwigl8AIpcmGajbLAN+FJjfZBW6+blzCgYyTzZad
# b3F7oWpE37pinRsSwuE118rimOqi2pbHGdQwBrFWP7nGrkd5ZZy/zqDUg3vKzqEW
# YjKWIe+D1OiGNKOSjInDi1975tkDJcBZceX4cEdxfL9rQNWi64+5J0ekbsPpZcx1
# k9LhnDQCazKnD7wPsI3BVmgWPL/LC/qOf46eXaNNPTrTk5UZarZkcelO8ICD9/7y
# osXrSax9J5Otl8y0fxBlB8jVuSRhxBoyDos8/zpjie7xRUnF0pgXR+qah7c5vXF+
# YyVVQsmjxC+/h/Usgq1DJVgwSUPCiZJ+gazeiC6YIqqNKLuRZYTaI6f3PmswvTtq
# k3q7w7M6EE+gtmq2SaDqkLfWNchWF2Z4NXh845IOaixBamj/KKV/98KxmJO+WAqL
# 621x9dL4Iq+2KT2M8E5vfHaHCROrjfkMrfmdAEsq2tSNjVo7yd36ISbfsrjwhV4O
# VNa67xHCMIIHejCCBWKgAwIBAgIKYQ6Q0gAAAAAAAzANBgkqhkiG9w0BAQsFADCB
# iDELMAkGA1UEBhMCVVMxEzARBgNVBAgTCldhc2hpbmd0b24xEDAOBgNVBAcTB1Jl
# ZG1vbmQxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEyMDAGA1UEAxMp
# TWljcm9zb2Z0IFJvb3QgQ2VydGlmaWNhdGUgQXV0aG9yaXR5IDIwMTEwHhcNMTEw
# NzA4MjA1OTA5WhcNMjYwNzA4MjEwOTA5WjB+MQswCQYDVQQGEwJVUzETMBEGA1UE
# CBMKV2FzaGluZ3RvbjEQMA4GA1UEBxMHUmVkbW9uZDEeMBwGA1UEChMVTWljcm9z
# b2Z0IENvcnBvcmF0aW9uMSgwJgYDVQQDEx9NaWNyb3NvZnQgQ29kZSBTaWduaW5n
# IFBDQSAyMDExMIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAq/D6chAc
# Lq3YbqqCEE00uvK2WCGfQhsqa+laUKq4BjgaBEm6f8MMHt03a8YS2AvwOMKZBrDI
# OdUBFDFC04kNeWSHfpRgJGyvnkmc6Whe0t+bU7IKLMOv2akrrnoJr9eWWcpgGgXp
# ZnboMlImEi/nqwhQz7NEt13YxC4Ddato88tt8zpcoRb0RrrgOGSsbmQ1eKagYw8t
# 00CT+OPeBw3VXHmlSSnnDb6gE3e+lD3v++MrWhAfTVYoonpy4BI6t0le2O3tQ5GD
# 2Xuye4Yb2T6xjF3oiU+EGvKhL1nkkDstrjNYxbc+/jLTswM9sbKvkjh+0p2ALPVO
# VpEhNSXDOW5kf1O6nA+tGSOEy/S6A4aN91/w0FK/jJSHvMAhdCVfGCi2zCcoOCWY
# OUo2z3yxkq4cI6epZuxhH2rhKEmdX4jiJV3TIUs+UsS1Vz8kA/DRelsv1SPjcF0P
# UUZ3s/gA4bysAoJf28AVs70b1FVL5zmhD+kjSbwYuER8ReTBw3J64HLnJN+/RpnF
# 78IcV9uDjexNSTCnq47f7Fufr/zdsGbiwZeBe+3W7UvnSSmnEyimp31ngOaKYnhf
# si+E11ecXL93KCjx7W3DKI8sj0A3T8HhhUSJxAlMxdSlQy90lfdu+HggWCwTXWCV
# mj5PM4TasIgX3p5O9JawvEagbJjS4NaIjAsCAwEAAaOCAe0wggHpMBAGCSsGAQQB
# gjcVAQQDAgEAMB0GA1UdDgQWBBRIbmTlUAXTgqoXNzcitW2oynUClTAZBgkrBgEE
# AYI3FAIEDB4KAFMAdQBiAEMAQTALBgNVHQ8EBAMCAYYwDwYDVR0TAQH/BAUwAwEB
# /zAfBgNVHSMEGDAWgBRyLToCMZBDuRQFTuHqp8cx0SOJNDBaBgNVHR8EUzBRME+g
# TaBLhklodHRwOi8vY3JsLm1pY3Jvc29mdC5jb20vcGtpL2NybC9wcm9kdWN0cy9N
# aWNSb29DZXJBdXQyMDExXzIwMTFfMDNfMjIuY3JsMF4GCCsGAQUFBwEBBFIwUDBO
# BggrBgEFBQcwAoZCaHR0cDovL3d3dy5taWNyb3NvZnQuY29tL3BraS9jZXJ0cy9N
# aWNSb29DZXJBdXQyMDExXzIwMTFfMDNfMjIuY3J0MIGfBgNVHSAEgZcwgZQwgZEG
# CSsGAQQBgjcuAzCBgzA/BggrBgEFBQcCARYzaHR0cDovL3d3dy5taWNyb3NvZnQu
# Y29tL3BraW9wcy9kb2NzL3ByaW1hcnljcHMuaHRtMEAGCCsGAQUFBwICMDQeMiAd
# AEwAZQBnAGEAbABfAHAAbwBsAGkAYwB5AF8AcwB0AGEAdABlAG0AZQBuAHQALiAd
# MA0GCSqGSIb3DQEBCwUAA4ICAQBn8oalmOBUeRou09h0ZyKbC5YR4WOSmUKWfdJ5
# DJDBZV8uLD74w3LRbYP+vj/oCso7v0epo/Np22O/IjWll11lhJB9i0ZQVdgMknzS
# Gksc8zxCi1LQsP1r4z4HLimb5j0bpdS1HXeUOeLpZMlEPXh6I/MTfaaQdION9Msm
# AkYqwooQu6SpBQyb7Wj6aC6VoCo/KmtYSWMfCWluWpiW5IP0wI/zRive/DvQvTXv
# biWu5a8n7dDd8w6vmSiXmE0OPQvyCInWH8MyGOLwxS3OW560STkKxgrCxq2u5bLZ
# 2xWIUUVYODJxJxp/sfQn+N4sOiBpmLJZiWhub6e3dMNABQamASooPoI/E01mC8Cz
# TfXhj38cbxV9Rad25UAqZaPDXVJihsMdYzaXht/a8/jyFqGaJ+HNpZfQ7l1jQeNb
# B5yHPgZ3BtEGsXUfFL5hYbXw3MYbBL7fQccOKO7eZS/sl/ahXJbYANahRr1Z85el
# CUtIEJmAH9AAKcWxm6U/RXceNcbSoqKfenoi+kiVH6v7RyOA9Z74v2u3S5fi63V4
# GuzqN5l5GEv/1rMjaHXmr/r8i+sLgOppO6/8MO0ETI7f33VtY5E90Z1WTk+/gFci
# oXgRMiF670EKsT/7qMykXcGhiJtXcVZOSEXAQsmbdlsKgEhr/Xmfwb1tbWrJUnMT
# DXpQzTGCGiMwghofAgEBMIGVMH4xCzAJBgNVBAYTAlVTMRMwEQYDVQQIEwpXYXNo
# aW5ndG9uMRAwDgYDVQQHEwdSZWRtb25kMR4wHAYDVQQKExVNaWNyb3NvZnQgQ29y
# cG9yYXRpb24xKDAmBgNVBAMTH01pY3Jvc29mdCBDb2RlIFNpZ25pbmcgUENBIDIw
# MTECEzMAAARssAYk0pKcb+AAAAAABGwwDQYJYIZIAWUDBAIBBQCgga4wGQYJKoZI
# hvcNAQkDMQwGCisGAQQBgjcCAQQwHAYKKwYBBAGCNwIBCzEOMAwGCisGAQQBgjcC
# ARUwLwYJKoZIhvcNAQkEMSIEIPAYD2dm+OfvN/z6FqhdTf+hvr2zogAhlp56eeBL
# fDMzMEIGCisGAQQBgjcCAQwxNDAyoBSAEgBNAGkAYwByAG8AcwBvAGYAdKEagBho
# dHRwOi8vd3d3Lm1pY3Jvc29mdC5jb20wDQYJKoZIhvcNAQEBBQAEggEARzQIKXR7
# vNDgDxI/pyuXWwQi7CwfO1kJf21/pEZ54bMkQHfDlXP9yAs5hLjZYOOj5FS+12Di
# +/f4XaNHjAwgTtG4JC47AgQQAF4JideZ/xxCiSMiQiiUutJolUIAl9ntSuHGxJrm
# 4ZEIZUwl9rfYHfcpSsnFQwQD5xtgR07JUAXZWxQDxLIS/uuzHYDcUkH6mVIiOEJQ
# enTSYj69TX6U8Sz0bWmml+pP5fpGcoynA2wf5GQZpklOW7siKqOFAy62oBVR3s4h
# 6N9NpKINeo7zH/aifLXsT6s7E8NnDJ722b84QXZHvr8W/yCYRUIJ1raZce0WQ2pp
# hIur0xs4manA5KGCF60wghepBgorBgEEAYI3AwMBMYIXmTCCF5UGCSqGSIb3DQEH
# AqCCF4YwgheCAgEDMQ8wDQYJYIZIAWUDBAIBBQAwggFaBgsqhkiG9w0BCRABBKCC
# AUkEggFFMIIBQQIBAQYKKwYBBAGEWQoDATAxMA0GCWCGSAFlAwQCAQUABCDN70lG
# cR4xD8Hrgr5ChIsad5zRVH9z6uObyXFjgThOfgIGaN7UoBuVGBMyMDI1MTAxNjE2
# NTEwMi4zNThaMASAAgH0oIHZpIHWMIHTMQswCQYDVQQGEwJVUzETMBEGA1UECBMK
# V2FzaGluZ3RvbjEQMA4GA1UEBxMHUmVkbW9uZDEeMBwGA1UEChMVTWljcm9zb2Z0
# IENvcnBvcmF0aW9uMS0wKwYDVQQLEyRNaWNyb3NvZnQgSXJlbGFuZCBPcGVyYXRp
# b25zIExpbWl0ZWQxJzAlBgNVBAsTHm5TaGllbGQgVFNTIEVTTjo0MzFBLTA1RTAt
# RDk0NzElMCMGA1UEAxMcTWljcm9zb2Z0IFRpbWUtU3RhbXAgU2VydmljZaCCEfsw
# ggcoMIIFEKADAgECAhMzAAACHUvAkoc4hX45AAEAAAIdMA0GCSqGSIb3DQEBCwUA
# MHwxCzAJBgNVBAYTAlVTMRMwEQYDVQQIEwpXYXNoaW5ndG9uMRAwDgYDVQQHEwdS
# ZWRtb25kMR4wHAYDVQQKExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xJjAkBgNVBAMT
# HU1pY3Jvc29mdCBUaW1lLVN0YW1wIFBDQSAyMDEwMB4XDTI1MDgxNDE4NDgzM1oX
# DTI2MTExMzE4NDgzM1owgdMxCzAJBgNVBAYTAlVTMRMwEQYDVQQIEwpXYXNoaW5n
# dG9uMRAwDgYDVQQHEwdSZWRtb25kMR4wHAYDVQQKExVNaWNyb3NvZnQgQ29ycG9y
# YXRpb24xLTArBgNVBAsTJE1pY3Jvc29mdCBJcmVsYW5kIE9wZXJhdGlvbnMgTGlt
# aXRlZDEnMCUGA1UECxMeblNoaWVsZCBUU1MgRVNOOjQzMUEtMDVFMC1EOTQ3MSUw
# IwYDVQQDExxNaWNyb3NvZnQgVGltZS1TdGFtcCBTZXJ2aWNlMIICIjANBgkqhkiG
# 9w0BAQEFAAOCAg8AMIICCgKCAgEAorSgaAA8oOl4ph574zw29egUN8DDepRHLX8F
# M1zHNJmXG6KrSqUKwzcKafopuYdPTETTCvb9aJfESuAU0iGNUFI/D6R0kvdfpe2o
# PX+E3sbTQvGi4JPH5qdIYUaJ45V/4bqe8eNvbWzpC+ZKjH193DeiI1XAI918JoQm
# BhlEXo/Ton1721luZJgincsf5LjMY3jX84WyXUSX3dsS7h/7xVI+w1yjg7pa+0y3
# o/me2Tsv6UJUdSTQap5ORGSfCnclnP1z3IiiWIWr3Vo7aIPWsgJzq3m5GxpxUHCQ
# k8qzUhk50y/uB+LGE3WIK2C77iy9iFsSfSLUnyMEzGRDW9mXHT4PH7Ozz6CHqQEi
# NvwcHqlvlCh1pHQh1NXQSAqOoVBs5mi6easf6yxWTfe5DrR79503r8pU6VqC2Y9X
# MRU4wH9QbYXYsIUZ33Jmndy22W1LBDAbxBPQHCBlncGDU3BgdhVUVLe80mggFO98
# FdkWho67w4kPdCTRkvdvkY8PrQYE/nQjHXCa0g7LcMttZb6ejMHfQ+tUWXv6+nZ4
# Ynkr2OkaxclFCw4RIYNMWD26AWbQj/WEdzga18fKtw66L5gzXPza6jFBfPJeKE3H
# 8QAuwpirmH4ms+5nUjNNQOmNgqJn0U1+3Yn7ClswD79YN0r3fdbYBMDApBZJpNlK
# 7q7HXRsCAwEAAaOCAUkwggFFMB0GA1UdDgQWBBSEWfBxNEamZtXm8gl92Yq80jfx
# XTAfBgNVHSMEGDAWgBSfpxVdAF5iXYP05dJlpxtTNRnpcjBfBgNVHR8EWDBWMFSg
# UqBQhk5odHRwOi8vd3d3Lm1pY3Jvc29mdC5jb20vcGtpb3BzL2NybC9NaWNyb3Nv
# ZnQlMjBUaW1lLVN0YW1wJTIwUENBJTIwMjAxMCgxKS5jcmwwbAYIKwYBBQUHAQEE
# YDBeMFwGCCsGAQUFBzAChlBodHRwOi8vd3d3Lm1pY3Jvc29mdC5jb20vcGtpb3Bz
# L2NlcnRzL01pY3Jvc29mdCUyMFRpbWUtU3RhbXAlMjBQQ0ElMjAyMDEwKDEpLmNy
# dDAMBgNVHRMBAf8EAjAAMBYGA1UdJQEB/wQMMAoGCCsGAQUFBwMIMA4GA1UdDwEB
# /wQEAwIHgDANBgkqhkiG9w0BAQsFAAOCAgEAkdweB4yxvLspLKq0D+miyD4Q0Ecx
# VFpNZuJxiR54gWRkeTDDuymNeB03JhlsBpbwSYJ5uZSgDBCvwHED2VL8lJpFlOpr
# JzxsXWC2NTfA+O+PO5Fk5jw6LHh6jeBADDEdQAx3Hqi7Zm0JwvQ93z5f6dtxkm29
# WqOcHYXRXfAQwy1hSrLXyfeblqR66jpP/9n0fCkWU4ggsUjQpQ2Ngj1DV09J4Y3y
# 7p9Nd81+Xs6qYo++7RKm8qiB/5NDeigOLjlAeFgiEXIRUJW+mJyqpQw+OORlaqcF
# jR8Hu0G+/7bMdek68YX+kPpDBk7Ue+I/xgiYJ1xcDRBn/vczLtN72+RIlD4UgXYL
# uBSCk//pDEPX5z39Cr+rkc6E4Y28FPk4BhloAyvp628P4xfElQY8TcxraUbZShyp
# ocE6ny95D1K1BkltZmrHVKCxmglnuOlM15NKIrXFlXCzdqpCtIwQ417wNAVF/QDP
# vzzbumPdTi6fb0tLbScYobV6zvbBsMsKEME4Tj1b9oIXC8dybJq4nbboEXYpRwi1
# QAbpSNrn+PxGW9uf1q63FnMJu4gm3Oh63njW/iVf723quzyHrSijWMgY0HiRiHQi
# 0Jyu0h8MdhRUp7mxbmLQckPiOFwAlIaUN/k725y/aLWpkRU6fqmLlEOyH5WpyLd2
# 3AYy9r8v+Qoba6swggdxMIIFWaADAgECAhMzAAAAFcXna54Cm0mZAAAAAAAVMA0G
# CSqGSIb3DQEBCwUAMIGIMQswCQYDVQQGEwJVUzETMBEGA1UECBMKV2FzaGluZ3Rv
# bjEQMA4GA1UEBxMHUmVkbW9uZDEeMBwGA1UEChMVTWljcm9zb2Z0IENvcnBvcmF0
# aW9uMTIwMAYDVQQDEylNaWNyb3NvZnQgUm9vdCBDZXJ0aWZpY2F0ZSBBdXRob3Jp
# dHkgMjAxMDAeFw0yMTA5MzAxODIyMjVaFw0zMDA5MzAxODMyMjVaMHwxCzAJBgNV
# BAYTAlVTMRMwEQYDVQQIEwpXYXNoaW5ndG9uMRAwDgYDVQQHEwdSZWRtb25kMR4w
# HAYDVQQKExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xJjAkBgNVBAMTHU1pY3Jvc29m
# dCBUaW1lLVN0YW1wIFBDQSAyMDEwMIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIIC
# CgKCAgEA5OGmTOe0ciELeaLL1yR5vQ7VgtP97pwHB9KpbE51yMo1V/YBf2xK4OK9
# uT4XYDP/XE/HZveVU3Fa4n5KWv64NmeFRiMMtY0Tz3cywBAY6GB9alKDRLemjkZr
# BxTzxXb1hlDcwUTIcVxRMTegCjhuje3XD9gmU3w5YQJ6xKr9cmmvHaus9ja+NSZk
# 2pg7uhp7M62AW36MEBydUv626GIl3GoPz130/o5Tz9bshVZN7928jaTjkY+yOSxR
# nOlwaQ3KNi1wjjHINSi947SHJMPgyY9+tVSP3PoFVZhtaDuaRr3tpK56KTesy+uD
# RedGbsoy1cCGMFxPLOJiss254o2I5JasAUq7vnGpF1tnYN74kpEeHT39IM9zfUGa
# RnXNxF803RKJ1v2lIH1+/NmeRd+2ci/bfV+AutuqfjbsNkz2K26oElHovwUDo9Fz
# pk03dJQcNIIP8BDyt0cY7afomXw/TNuvXsLz1dhzPUNOwTM5TI4CvEJoLhDqhFFG
# 4tG9ahhaYQFzymeiXtcodgLiMxhy16cg8ML6EgrXY28MyTZki1ugpoMhXV8wdJGU
# lNi5UPkLiWHzNgY1GIRH29wb0f2y1BzFa/ZcUlFdEtsluq9QBXpsxREdcu+N+VLE
# hReTwDwV2xo3xwgVGD94q0W29R6HXtqPnhZyacaue7e3PmriLq0CAwEAAaOCAd0w
# ggHZMBIGCSsGAQQBgjcVAQQFAgMBAAEwIwYJKwYBBAGCNxUCBBYEFCqnUv5kxJq+
# gpE8RjUpzxD/LwTuMB0GA1UdDgQWBBSfpxVdAF5iXYP05dJlpxtTNRnpcjBcBgNV
# HSAEVTBTMFEGDCsGAQQBgjdMg30BATBBMD8GCCsGAQUFBwIBFjNodHRwOi8vd3d3
# Lm1pY3Jvc29mdC5jb20vcGtpb3BzL0RvY3MvUmVwb3NpdG9yeS5odG0wEwYDVR0l
# BAwwCgYIKwYBBQUHAwgwGQYJKwYBBAGCNxQCBAweCgBTAHUAYgBDAEEwCwYDVR0P
# BAQDAgGGMA8GA1UdEwEB/wQFMAMBAf8wHwYDVR0jBBgwFoAU1fZWy4/oolxiaNE9
# lJBb186aGMQwVgYDVR0fBE8wTTBLoEmgR4ZFaHR0cDovL2NybC5taWNyb3NvZnQu
# Y29tL3BraS9jcmwvcHJvZHVjdHMvTWljUm9vQ2VyQXV0XzIwMTAtMDYtMjMuY3Js
# MFoGCCsGAQUFBwEBBE4wTDBKBggrBgEFBQcwAoY+aHR0cDovL3d3dy5taWNyb3Nv
# ZnQuY29tL3BraS9jZXJ0cy9NaWNSb29DZXJBdXRfMjAxMC0wNi0yMy5jcnQwDQYJ
# KoZIhvcNAQELBQADggIBAJ1VffwqreEsH2cBMSRb4Z5yS/ypb+pcFLY+TkdkeLEG
# k5c9MTO1OdfCcTY/2mRsfNB1OW27DzHkwo/7bNGhlBgi7ulmZzpTTd2YurYeeNg2
# LpypglYAA7AFvonoaeC6Ce5732pvvinLbtg/SHUB2RjebYIM9W0jVOR4U3UkV7nd
# n/OOPcbzaN9l9qRWqveVtihVJ9AkvUCgvxm2EhIRXT0n4ECWOKz3+SmJw7wXsFSF
# QrP8DJ6LGYnn8AtqgcKBGUIZUnWKNsIdw2FzLixre24/LAl4FOmRsqlb30mjdAy8
# 7JGA0j3mSj5mO0+7hvoyGtmW9I/2kQH2zsZ0/fZMcm8Qq3UwxTSwethQ/gpY3UA8
# x1RtnWN0SCyxTkctwRQEcb9k+SS+c23Kjgm9swFXSVRk2XPXfx5bRAGOWhmRaw2f
# pCjcZxkoJLo4S5pu+yFUa2pFEUep8beuyOiJXk+d0tBMdrVXVAmxaQFEfnyhYWxz
# /gq77EFmPWn9y8FBSX5+k77L+DvktxW/tM4+pTFRhLy/AsGConsXHRWJjXD+57XQ
# KBqJC4822rpM+Zv/Cuk0+CQ1ZyvgDbjmjJnW4SLq8CdCPSWU5nR0W2rRnj7tfqAx
# M328y+l7vzhwRNGQ8cirOoo6CGJ/2XBjU02N7oJtpQUQwXEGahC0HVUzWLOhcGby
# oYIDVjCCAj4CAQEwggEBoYHZpIHWMIHTMQswCQYDVQQGEwJVUzETMBEGA1UECBMK
# V2FzaGluZ3RvbjEQMA4GA1UEBxMHUmVkbW9uZDEeMBwGA1UEChMVTWljcm9zb2Z0
# IENvcnBvcmF0aW9uMS0wKwYDVQQLEyRNaWNyb3NvZnQgSXJlbGFuZCBPcGVyYXRp
# b25zIExpbWl0ZWQxJzAlBgNVBAsTHm5TaGllbGQgVFNTIEVTTjo0MzFBLTA1RTAt
# RDk0NzElMCMGA1UEAxMcTWljcm9zb2Z0IFRpbWUtU3RhbXAgU2VydmljZaIjCgEB
# MAcGBSsOAwIaAxUAuoO+BKbfXzqyfi9GLEdWHkCLeT+ggYMwgYCkfjB8MQswCQYD
# VQQGEwJVUzETMBEGA1UECBMKV2FzaGluZ3RvbjEQMA4GA1UEBxMHUmVkbW9uZDEe
# MBwGA1UEChMVTWljcm9zb2Z0IENvcnBvcmF0aW9uMSYwJAYDVQQDEx1NaWNyb3Nv
# ZnQgVGltZS1TdGFtcCBQQ0EgMjAxMDANBgkqhkiG9w0BAQsFAAIFAOybHy4wIhgP
# MjAyNTEwMTYwNzM3MThaGA8yMDI1MTAxNzA3MzcxOFowdDA6BgorBgEEAYRZCgQB
# MSwwKjAKAgUA7JsfLgIBADAHAgEAAgIA0DAHAgEAAgISjTAKAgUA7JxwrgIBADA2
# BgorBgEEAYRZCgQCMSgwJjAMBgorBgEEAYRZCgMCoAowCAIBAAIDB6EgoQowCAIB
# AAIDAYagMA0GCSqGSIb3DQEBCwUAA4IBAQC8q/AfTcBeGwJgHTjWZ1lOm4dEqHb/
# NEqvwMeCCJBNnbTqI+/N4KSxvtrbNf67fG3+jezqsrDizsSnTRsvr+Vq8Kv6WPAy
# vrd3kBQGblCFiuj+ZUh3iWQk8J5TOCEKTTYgBEoqeKyMq+fqgQQatkKBQl90CD2q
# eYnv8Q8lxT7oKYWKlnr1ifApL45K0ge0X6t1mOIg957BzVh2OUPedy1yfyOk/Zsx
# n/A+VqAnMyDy8DddSKQVKA4bpiUEYI6pw8pFjPw0fFFNZuNGVqewTyieZ2tMHEbq
# 17tC+HlAZc8JzzzvVb7EKAUj3yRJvfUDj/a6pGD/phhE0KPaUPZg6//7MYIEDTCC
# BAkCAQEwgZMwfDELMAkGA1UEBhMCVVMxEzARBgNVBAgTCldhc2hpbmd0b24xEDAO
# BgNVBAcTB1JlZG1vbmQxHjAcBgNVBAoTFU1pY3Jvc29mdCBDb3Jwb3JhdGlvbjEm
# MCQGA1UEAxMdTWljcm9zb2Z0IFRpbWUtU3RhbXAgUENBIDIwMTACEzMAAAIdS8CS
# hziFfjkAAQAAAh0wDQYJYIZIAWUDBAIBBQCgggFKMBoGCSqGSIb3DQEJAzENBgsq
# hkiG9w0BCRABBDAvBgkqhkiG9w0BCQQxIgQgEKs4gZshZGbPCNu0H/tiQu/mqGQx
# yy4dvkIXx0ZbG4kwgfoGCyqGSIb3DQEJEAIvMYHqMIHnMIHkMIG9BCCxtpXMXEiL
# JzrqM77ep4rTNwrMOj6gpWN9hZvpj5QFUTCBmDCBgKR+MHwxCzAJBgNVBAYTAlVT
# MRMwEQYDVQQIEwpXYXNoaW5ndG9uMRAwDgYDVQQHEwdSZWRtb25kMR4wHAYDVQQK
# ExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xJjAkBgNVBAMTHU1pY3Jvc29mdCBUaW1l
# LVN0YW1wIFBDQSAyMDEwAhMzAAACHUvAkoc4hX45AAEAAAIdMCIEILErup300v5E
# lzCyq6DFsmZ5ktLFkWCTYQ8AEY4mz0MSMA0GCSqGSIb3DQEBCwUABIICAF9ytvZi
# gnMEf2S5fkCVY2Ik/5hp14O6qPHSWVaCzEFz/gNoBAdG6b4FEJqYijIgMA0/BwYv
# Kwdf5yIvx11NnTYb4+0b/kQrhEJQIDi/uKXR/Ng+U33tVLPWRs2a79UVtm8dD5NV
# M2k4Cnfb3GOmB/oA8nAc2y+RNpOvKoSxzTT0gr5Vzk1KQnVr6L7qyxnXUCHSnqoV
# q1UqrNOiPLcPX8yRSl1Be3LjV1HGvGP9KPMm/CgiFuQOJdQq0F127+vcyZ0whf6i
# 5OUnihWw1fdt5hySh6Vo5fEOWm2xmPhnfBn7NM4189sJpGZxUBsZqmUVZg6+klOB
# +EHKkC2eHzQYGsfwkTgfihsKFJQt0WaqRKS2+ifDZU7Oy76can78c8E8pchS8zVQ
# dhRMnB4lEfZTzJ11W0+qXOpdDW9cUU5IVuLctVL2EMiK78Kcp1CaYWhea5pCtEoE
# qf3diJCZdD9jwvqlzx+uUFJSr3dChSo71cTiybd2SPczt4vl1VW+5lF+m7dUDWyn
# fN041BWCTDftX3OPZz45czzQf1MO0W7hlw2nr7nAyZZn8ItfJCV6WGbFve9lfrI5
# 8HOGcW73FA/QvQFl6Qhuse+wZ4oHq52ufodgd1MHUt/MNR3nvAbWYvonaZNfkPnE
# L0WuYPP6RVmQB8i9AjpQoyEIo2Ki0k9OMOmR
# SIG # End Windows Authenticode signature block