diff --git a/src/embit/util/ctypes_secp256k1.py b/src/embit/util/ctypes_secp256k1.py
index 798d56e..2a0c1d7 100644
--- a/src/embit/util/ctypes_secp256k1.py
+++ b/src/embit/util/ctypes_secp256k1.py
@@ -1,6 +1,7 @@
 import ctypes, os
 import ctypes.util
 import platform
+import threading
 
 from ctypes import (
     cast,
@@ -18,6 +19,14 @@ from ctypes import (
     POINTER,
 )
 
+_lock = threading.Lock()
+# @locked decorator
+def locked(func):
+    def wrapper(*args, **kwargs):
+        with _lock:
+            return func(*args, **kwargs)
+    return wrapper
+
 # Flags to pass to context_create.
 CONTEXT_VERIFY = 0b0100000001
 CONTEXT_SIGN = 0b1000000001
@@ -63,7 +72,7 @@ def _find_library():
             library_path = "/usr/local/lib/libsecp256k1.so.0"
     return library_path
 
-
+@locked
 def _init(flags=(CONTEXT_SIGN | CONTEXT_VERIFY)):
     library_path = _find_library()
     # meh, can't find library
@@ -383,6 +392,7 @@ def _init(flags=(CONTEXT_SIGN | CONTEXT_VERIFY)):
 _secp = _init()
 
 # bindings equal to ones in micropython
+@locked
 def context_randomize(seed, context=_secp.ctx):
     if len(seed) != 32:
         raise ValueError("Seed should be 32 bytes long")
@@ -390,6 +400,7 @@ def context_randomize(seed, context=_secp.ctx):
         raise RuntimeError("Failed to randomize context")
 
 
+@locked
 def ec_pubkey_create(secret, context=_secp.ctx):
     if len(secret) != 32:
         raise ValueError("Private key should be 32 bytes long")
@@ -400,6 +411,7 @@ def ec_pubkey_create(secret, context=_secp.ctx):
     return pub
 
 
+@locked
 def ec_pubkey_parse(sec, context=_secp.ctx):
     if len(sec) != 33 and len(sec) != 65:
         raise ValueError("Serialized pubkey should be 33 or 65 bytes long")
@@ -416,6 +428,7 @@ def ec_pubkey_parse(sec, context=_secp.ctx):
     return pub
 
 
+@locked
 def ec_pubkey_serialize(pubkey, flag=EC_COMPRESSED, context=_secp.ctx):
     if len(pubkey) != 64:
         raise ValueError("Pubkey should be 64 bytes long")
@@ -429,6 +442,7 @@ def ec_pubkey_serialize(pubkey, flag=EC_COMPRESSED, context=_secp.ctx):
     return sec
 
 
+@locked
 def ecdsa_signature_parse_compact(compact_sig, context=_secp.ctx):
     if len(compact_sig) != 64:
         raise ValueError("Compact signature should be 64 bytes long")
@@ -439,6 +453,7 @@ def ecdsa_signature_parse_compact(compact_sig, context=_secp.ctx):
     return sig
 
 
+@locked
 def ecdsa_signature_parse_der(der, context=_secp.ctx):
     sig = bytes(64)
     r = _secp.secp256k1_ecdsa_signature_parse_der(context, sig, der, len(der))
@@ -447,6 +462,7 @@ def ecdsa_signature_parse_der(der, context=_secp.ctx):
     return sig
 
 
+@locked
 def ecdsa_signature_serialize_der(sig, context=_secp.ctx):
     if len(sig) != 64:
         raise ValueError("Signature should be 64 bytes long")
@@ -458,6 +474,7 @@ def ecdsa_signature_serialize_der(sig, context=_secp.ctx):
     return der[: sz.value]
 
 
+@locked
 def ecdsa_signature_serialize_compact(sig, context=_secp.ctx):
     if len(sig) != 64:
         raise ValueError("Signature should be 64 bytes long")
@@ -468,6 +485,7 @@ def ecdsa_signature_serialize_compact(sig, context=_secp.ctx):
     return ser
 
 
+@locked
 def ecdsa_signature_normalize(sig, context=_secp.ctx):
     if len(sig) != 64:
         raise ValueError("Signature should be 64 bytes long")
@@ -476,6 +494,7 @@ def ecdsa_signature_normalize(sig, context=_secp.ctx):
     return sig2
 
 
+@locked
 def ecdsa_verify(sig, msg, pub, context=_secp.ctx):
     if len(sig) != 64:
         raise ValueError("Signature should be 64 bytes long")
@@ -487,6 +506,7 @@ def ecdsa_verify(sig, msg, pub, context=_secp.ctx):
     return bool(r)
 
 
+@locked
 def ecdsa_sign(msg, secret, nonce_function=None, extra_data=None, context=_secp.ctx):
     if len(msg) != 32:
         raise ValueError("Message should be 32 bytes long")
@@ -501,12 +521,14 @@ def ecdsa_sign(msg, secret, nonce_function=None, extra_data=None, context=_secp.
     return sig
 
 
+@locked
 def ec_seckey_verify(secret, context=_secp.ctx):
     if len(secret) != 32:
         raise ValueError("Secret should be 32 bytes long")
     return bool(_secp.secp256k1_ec_seckey_verify(context, secret))
 
 
+@locked
 def ec_privkey_negate(secret, context=_secp.ctx):
     if len(secret) != 32:
         raise ValueError("Secret should be 32 bytes long")
@@ -515,6 +537,7 @@ def ec_privkey_negate(secret, context=_secp.ctx):
     return b
 
 
+@locked
 def ec_pubkey_negate(pubkey, context=_secp.ctx):
     if len(pubkey) != 64:
         raise ValueError("Pubkey should be a 64-byte structure")
@@ -525,6 +548,7 @@ def ec_pubkey_negate(pubkey, context=_secp.ctx):
     return pub
 
 
+@locked
 def ec_privkey_tweak_add(secret, tweak, context=_secp.ctx):
     if len(secret) != 32 or len(tweak) != 32:
         raise ValueError("Secret and tweak should both be 32 bytes long")
@@ -533,6 +557,7 @@ def ec_privkey_tweak_add(secret, tweak, context=_secp.ctx):
         raise ValueError("Failed to tweak the secret")
     return None
 
+@locked
 def ec_pubkey_tweak_add(pub, tweak, context=_secp.ctx):
     if len(pub) != 64:
         raise ValueError("Public key should be 64 bytes long")
@@ -544,6 +569,7 @@ def ec_pubkey_tweak_add(pub, tweak, context=_secp.ctx):
     return None
 
 
+@locked
 def ec_privkey_add(secret, tweak, context=_secp.ctx):
     if len(secret) != 32 or len(tweak) != 32:
         raise ValueError("Secret and tweak should both be 32 bytes long")
@@ -555,6 +581,7 @@ def ec_privkey_add(secret, tweak, context=_secp.ctx):
     return s
 
 
+@locked
 def ec_pubkey_add(pub, tweak, context=_secp.ctx):
     if len(pub) != 64:
         raise ValueError("Public key should be 64 bytes long")
@@ -566,6 +593,7 @@ def ec_pubkey_add(pub, tweak, context=_secp.ctx):
     return p
 
 
+@locked
 def ec_privkey_tweak_mul(secret, tweak, context=_secp.ctx):
     if len(secret) != 32 or len(tweak) != 32:
         raise ValueError("Secret and tweak should both be 32 bytes long")
@@ -573,6 +601,7 @@ def ec_privkey_tweak_mul(secret, tweak, context=_secp.ctx):
         raise ValueError("Failed to tweak the secret")
 
 
+@locked
 def ec_pubkey_tweak_mul(pub, tweak, context=_secp.ctx):
     if len(pub) != 64:
         raise ValueError("Public key should be 64 bytes long")
@@ -582,6 +611,7 @@ def ec_pubkey_tweak_mul(pub, tweak, context=_secp.ctx):
         raise ValueError("Failed to tweak the public key")
 
 
+@locked
 def ec_pubkey_combine(*args, context=_secp.ctx):
     pub = bytes(64)
     pubkeys = (c_char_p * len(args))(*args)
@@ -591,6 +621,7 @@ def ec_pubkey_combine(*args, context=_secp.ctx):
     return pub
 
 # schnorrsig
+@locked
 def xonly_pubkey_from_pubkey(pubkey, context=_secp.ctx):
     if len(pubkey)!=64:
         raise ValueError("Pubkey should be 64 bytes long")
@@ -602,6 +633,7 @@ def xonly_pubkey_from_pubkey(pubkey, context=_secp.ctx):
         raise RuntimeError("Failed to convert the pubkey")
     return xonly_pub, bool(parity.contents.value)
 
+@locked
 def schnorrsig_verify(sig, msg, pubkey, context=_secp.ctx):
     assert len(sig) == 64
     assert len(msg) == 32
@@ -609,6 +641,7 @@ def schnorrsig_verify(sig, msg, pubkey, context=_secp.ctx):
     res = _secp.secp256k1_schnorrsig_verify(context, sig, msg, pubkey)
     return bool(res)
 
+@locked
 def keypair_create(secret, context=_secp.ctx):
     assert len(secret) == 32
     keypair = bytes(96)
@@ -617,18 +650,21 @@ def keypair_create(secret, context=_secp.ctx):
         raise ValueError("Failed to create keypair")
     return keypair
 
+# not @locked because it uses keypair_create inside
 def schnorrsig_sign(msg, keypair, nonce_function=None, extra_data=None, context=_secp.ctx):
     assert len(msg) == 32
     if len(keypair) == 32:
         keypair = keypair_create(keypair, context=context)
-    assert len(keypair) == 96
-    sig = bytes(64)
-    r = _secp.secp256k1_schnorrsig_sign(context, sig, msg, keypair, nonce_function, extra_data)
-    if r == 0:
-        raise ValueError("Failed to sign")
-    return sig
+    with _lock:
+        assert len(keypair) == 96
+        sig = bytes(64)
+        r = _secp.secp256k1_schnorrsig_sign(context, sig, msg, keypair, nonce_function, extra_data)
+        if r == 0:
+            raise ValueError("Failed to sign")
+        return sig
 
 # recoverable
+@locked
 def ecdsa_sign_recoverable(msg, secret, context=_secp.ctx):
     if len(msg) != 32:
         raise ValueError("Message should be 32 bytes long")
@@ -641,6 +677,7 @@ def ecdsa_sign_recoverable(msg, secret, context=_secp.ctx):
     return sig
 
 
+@locked
 def ecdsa_recoverable_signature_serialize_compact(sig, context=_secp.ctx):
     if len(sig) != 65:
         raise ValueError("Recoverable signature should be 65 bytes long")
@@ -654,6 +691,7 @@ def ecdsa_recoverable_signature_serialize_compact(sig, context=_secp.ctx):
     return ser, idx[0]
 
 
+@locked
 def ecdsa_recoverable_signature_parse_compact(compact_sig, recid, context=_secp.ctx):
     if len(compact_sig) != 64:
         raise ValueError("Signature should be 64 bytes long")
@@ -666,6 +704,7 @@ def ecdsa_recoverable_signature_parse_compact(compact_sig, recid, context=_secp.
     return sig
 
 
+@locked
 def ecdsa_recoverable_signature_convert(sigin, context=_secp.ctx):
     if len(sigin) != 65:
         raise ValueError("Recoverable signature should be 65 bytes long")
@@ -676,6 +715,7 @@ def ecdsa_recoverable_signature_convert(sigin, context=_secp.ctx):
     return sig
 
 
+@locked
 def ecdsa_recover(sig, msghash, context=_secp.ctx):
     if len(sig) != 65:
         raise ValueError("Recoverable signature should be 65 bytes long")
@@ -689,6 +729,7 @@ def ecdsa_recover(sig, msghash, context=_secp.ctx):
 
 # zkp modules
 
+@locked
 def pedersen_commitment_parse(inp, context=_secp.ctx):
     if len(inp)!=33:
         raise ValueError("Serialized commitment should be 33 bytes long")
@@ -698,6 +739,7 @@ def pedersen_commitment_parse(inp, context=_secp.ctx):
         raise ValueError("Failed to parse commitment")
     return commit
 
+@locked
 def pedersen_commitment_serialize(commit, context=_secp.ctx):
     if len(commit)!=64:
         raise ValueError("Commitment should be 64 bytes long")
@@ -707,6 +749,7 @@ def pedersen_commitment_serialize(commit, context=_secp.ctx):
         raise ValueError("Failed to serialize commitment")
     return sec
 
+@locked
 def pedersen_commit(vbf, value, gen, context=_secp.ctx):
     if len(gen)!=64:
         raise ValueError("Generator should be 64 bytes long")
@@ -718,6 +761,7 @@ def pedersen_commit(vbf, value, gen, context=_secp.ctx):
         raise ValueError("Failed to create commitment")
     return commit
 
+@locked
 def pedersen_blind_generator_blind_sum(values, gens, vbfs, num_inputs, context=_secp.ctx):
     vals = (c_uint64 * len(values))(*values)
     vbf = bytes(vbfs[-1])
@@ -733,6 +777,7 @@ def pedersen_blind_generator_blind_sum(values, gens, vbfs, num_inputs, context=_
     assert len(res) == 32
     return res
 
+@locked
 def pedersen_verify_tally(ins, outs, context=_secp.ctx):
     in_ptr = (c_char_p * len(ins))(*ins)
     out_ptr = (c_char_p * len(outs))(*outs)
@@ -740,6 +785,7 @@ def pedersen_verify_tally(ins, outs, context=_secp.ctx):
     return bool(res)
 
 # generator
+@locked
 def generator_parse(inp, context=_secp.ctx):
     if len(inp)!=33:
         raise ValueError("Serialized generator should be 33 bytes long")
@@ -749,6 +795,7 @@ def generator_parse(inp, context=_secp.ctx):
         raise ValueError("Failed to parse generator")
     return gen
 
+@locked
 def generator_generate(asset, context=_secp.ctx):
     if len(asset)!=32:
         raise ValueError("Asset should be 32 bytes long")
@@ -758,6 +805,7 @@ def generator_generate(asset, context=_secp.ctx):
         raise ValueError("Failed to generate generator")
     return gen
 
+@locked
 def generator_generate_blinded(asset, abf, context=_secp.ctx):
     if len(asset)!=32:
         raise ValueError("Asset should be 32 bytes long")
@@ -769,6 +817,7 @@ def generator_generate_blinded(asset, abf, context=_secp.ctx):
         raise ValueError("Failed to generate generator")
     return gen
 
+@locked
 def generator_serialize(generator, context=_secp.ctx):
     if len(generator)!=64:
         raise ValueError("Generator should be 64 bytes long")
@@ -778,6 +827,7 @@ def generator_serialize(generator, context=_secp.ctx):
     return sec
 
 # rangeproof
+@locked
 def rangeproof_rewind(proof, nonce, value_commitment, script_pubkey, generator, message_length=64, context=_secp.ctx):
     if len(generator)!=64:
         raise ValueError("Generator should be 64 bytes long")
@@ -804,6 +854,7 @@ def rangeproof_rewind(proof, nonce, value_commitment, script_pubkey, generator,
         raise RuntimeError("Failed to rewind the proof")
     return value_out.contents.value, vbf_out, msg[:msglen.contents.value], min_value.contents.value, max_value.contents.value
 
+@locked
 def rangeproof_sign(nonce, value, value_commitment, vbf, message, extra, gen, min_value=1, exp=0, min_bits=52, context=_secp.ctx):
     if len(gen)!=64:
         raise ValueError("Generator should be 64 bytes long")
@@ -823,6 +874,7 @@ def rangeproof_sign(nonce, value, value_commitment, vbf, message, extra, gen, mi
         raise RuntimeError("Failed to generate the proof")
     return bytes(proof[:prooflen.contents.value])
 
+@locked
 def musig_pubkey_combine(*args, context=_secp.ctx):
     pub = bytes(64)
     # TODO: strange that behaviour is different from pubkey_combine...
@@ -833,6 +885,7 @@ def musig_pubkey_combine(*args, context=_secp.ctx):
     return pub
 
 # surjection proof
+@locked
 def surjectionproof_initialize(in_tags, out_tag, seed, tags_to_use=None, iterations=100, context=_secp.ctx):
     if tags_to_use is None:
         tags_to_use = min(3, len(in_tags))
@@ -847,16 +900,19 @@ def surjectionproof_initialize(in_tags, out_tag, seed, tags_to_use=None, iterati
         raise RuntimeError("Failed to initialize the proof")
     return proof, input_index.contents.value
 
+@locked
 def surjectionproof_generate(proof, in_idx, in_tags, out_tag, in_abf, out_abf, context=_secp.ctx):
     res = _secp.secp256k1_surjectionproof_generate(context, proof, b"".join(in_tags), len(in_tags), out_tag, in_idx, in_abf, out_abf)
     if not res:
         raise RuntimeError("Failed to generate surjection proof")
     return proof
 
+@locked
 def surjectionproof_verify(proof, in_tags, out_tag, context=_secp.ctx):
     res = _secp.secp256k1_surjectionproof_verify(context, proof, b"".join(in_tags), len(in_tags), out_tag)
     return bool(res)
 
+@locked
 def surjectionproof_serialize(proof, context=_secp.ctx):
     s = _secp.secp256k1_surjectionproof_serialized_size(context, proof)
     b = bytes(s)
@@ -867,6 +923,7 @@ def surjectionproof_serialize(proof, context=_secp.ctx):
         raise RuntimeError("Failed to serialize surjection proof - size mismatch")
     return b
 
+@locked
 def surjectionproof_parse(proof, context=_secp.ctx):
     parsed_proof = bytes(4+8+256//8+32*257)
     res = _secp.secp256k1_surjectionproof_parse(context, parsed_proof, proof, len(proof))
diff --git a/tests/tests/__init__.py b/tests/tests/__init__.py
index 52796d4..90f43c5 100644
--- a/tests/tests/__init__.py
+++ b/tests/tests/__init__.py
@@ -14,3 +14,4 @@ from .test_taproot import *
 
 if sys.implementation.name != "micropython":
     from .test_bindings import *
+    from .test_threading import *
diff --git a/tests/tests/test_threading.py b/tests/tests/test_threading.py
new file mode 100644
index 0000000..588586d
--- /dev/null
+++ b/tests/tests/test_threading.py
@@ -0,0 +1,81 @@
+from unittest import TestCase
+from embit.ec import PrivateKey
+from embit.liquid.pset import PSET
+from embit.hashes import tagged_hash
+from embit.liquid import slip77
+import threading
+
+mbkey = PrivateKey.from_string("L2U2zGBgimb2vNee3bTw2y936PDJZXq3p7nMXEWuPP5MmpE1nCfv")
+B64PSET = "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vJsiqHG+0GCv74xpxKZUFCUUn1BD2x8pHmICjbyFXBbi97uGAKKNLZoSUSyHxm4m5rURn+MS1FuD2BkNHoWOia16kSItId7ieiy93WUdo9eAB8XoP8feidVfZryYCkzu4cXTZcgxYioVpu5+aoY8lzXMoi7Hk1wv8LinG0aTZ/DB0O/mBn/1ZF95v3l0Nc15LGwKRFJYppVmFtp8RSHrdAaTwXA4SUBoNtwpqgvFJNAXwWIDj+VGtH/usMGzBpmOdgHg5jmrSGGsiVa2rAh0twyHuT12wcOBnK7LX7iftiKZ8RFp6oyyjdd+CMOVeLO+z3xo2ThGeePL2g9uwXCrvroEgvJ5zh+baqSDW6tWAD95WAKoSthhRqxSv73Lqn/mwW6A5zaFxiD6sSgJMnBXgdLy+//izFDoU9YiXAGm1AOYSwV3VOkFZueutG9DbISvTjyGyxcPCVEsxfpH/0sSW/QO175whD76moHD7Q/qtC0wZXqryzIb+Y/7qqWeYz6KPz+v/j2zYn4TENMQGHTEYk22Nyri88hmgWbnU6CaBTWnxtiXhrLiywbX270cooEt84p7EwVlzyUJuE1mrafxmtq5sptfjFFNLbT61UTd8YjbZ70tCStnymYFX38yo5zhGGn9W5EuQ+oWeewMDfymVykOpi72Bbcha9M1aZSslFFXFfwrUX6xZJLeyrRAS/lGEXun3lRzrNr93EGL5AFjL6juHLmqcfG+l7gFAjaMXCRt3uenalx3x52XxtDwG7azD7lcsD9pUbRE5FMaf2h2Kg61h5YgIOQbDoF57CJKys9boHHpGK+S2vYvlEl+C5c1DJWLVXKCGbB4gl7WXgCoyj264QfQ1IBy1XgKAuwetsBlXWaX6FPp6NSzM6e2MXQ7h9tzl3lwyZri7QlB7Tm9EatSAx3OUKRXRWp0AkCKcQtKoblEtLJ6kgh5Ekzcuesc9MM6ZThNfobaOyO2spdxogk/rvddhIq8fgd4nK7T3KvaZkfauifXQQZFAicp7xxeFD6HEuge9Q5jL3oHfaZ1KrSZctCY7HtXr8I5oHRbMv8n1/Ip5NJnrt8L17elnYWRWAr9NSFjuKhuZ7iTo2d8X0nstYc+linzzamECQo661fOMTeAw+ev+LZ12Ak4Q31xNBnpPoRnClrEHpQlucNazSNTYX23uZSYIWfjMVkb1/BswPluErENciH7dk1fhAoJrygUDI1YaZgm3OGC6gNrPuvN3iCLqOYCe999D41SDBkp2oJ+abg1DiUdRV3yw8n5roDadsQ7XSwx+S9VSazUua9IXC9LQmMIPWNBgjvzRy+z69N/bhPN8ZH/vgt1PgRtBKUP9lXsCwscFZPQYwqYBfa45wfjOwK+7eCqhvSS8R/c+qXq2348ewZ6krqGkkMjbQZpEi13cQEfr8PNOVb2NTwkG/Kh2NkLzI1zlYAyfkTmRFg/3eVx4aMnWjqXNaKtJSMrBo+L9glVXmfuVDcLXGFjws9XfkEdRE21Sf6f92W0V47Yv5FMjHfmky8dMQ/riXxbz5KYRWv8cUethtp2hlhizJqMKJl5uLop6P0o3mjqznxETg+WtD1PhnabxpvA8/tLzZqShO2Dkvh08GpKvjrNEvhJqxp8afTAigJCG4ur5FbMzTidd27ShFySUCY8VfIM4VSiRnkbzqQpCZ4obfGaLb1T267sSB+JPIdy+3fFLClhtT0ZMPUWWeatGSoH+GqW7IWuCERdN1j83ngOalczaktcc1dS6YYJrLg/srZH+kcrnGtO0NXGwL1+iE9z/JG13SyK3NJe1qggkixqgdfKJH5TLZQH1AAEA/VQBAgAAAAAB3J/IrUwUWy8EubZ6BxwFyKfoDnQFx8Ws3k0G+TsZ0vMBAAAAAP3///8DC3Nhx39lJvCLk85hLK2jOideItHxtmwbbBm1TX8i72e0CMMNyrbkaul04PRt3rrI6xW5bHZJ5He/eRY6am10RnCdAlGhFKKBV3QTY2LfUbhY9DDFmzdg3RJjOsmuvMHsq/aBFgAUIZejNpgO7ZxNU8/VHk+Yde2Nt0gKXXLGxoz72obRjRIitW10XkLnHe6XuTxgjdwoiSLX2dEIEjEAT0SqTZcQf7pthn4SIqxEp/AXEbH3eackEVQSvXcCH2Xx/4RDwnmCXfVbSRbvIIYHcgon/ko/Z/qsxfx2NLAWABTQxKPvCemXtumeOX5Rj+PkGhGMoQEYaWzyPjIJpuhEedAfE/55yQE1auU5B8yILP/T4mQBIwEAAAAAAAAA+QAAdAAAAAEBegpdcsbGjPvahtGNEiK1bXReQucd7pe5PGCN3CiJItfZ0QgSMQBPRKpNlxB/um2GfhIirESn8BcRsfd5pyQRVBK9dwIfZfH/hEPCeYJd9VtJFu8ghgdyCif+Sj9n+qzF/HY0sBYAFNDEo+8J6Ze26Z45flGP4+QaEYyhIgYC56slN7XUnpcDCargbp5J82zhyf671E7I4NHMoLT5wxkYc8XaClQAAIABAACAAAAAgAAAAAAAAAAAAQ4ghLK/ZrL/Qp6OnIo/TB3J8j4nYwlPq8i57lKBZgMg6XgBDwQBAAAAARAE/f///wv8CGVsZW1lbnRzAAgA4fUFAAAAAAv8CGVsZW1lbnRzASC0vDFvvZYaY1OBypwMOHCImi1rABcHHgGIzSOQbJt81Qv8CGVsZW1lbnRzAiAYaWzyPjIJpuhEedAfE/55yQE1auU5B8yILP/T4mQBIwv8CGVsZW1lbnRzAyCbUmpOG0+u3lnxy7+bij2JAfcKiXJ4hyK/E2OFxXxbkQf8BHBzZXQO/U4QYDMAAAAAAAAAAZHeKwBfNKVVpnI+FqH09q+ZGMBAELa8Jy2JXJqhP8TqRoueL7w70A+1efTnunpcM4Vv/HNjQdfRLWrgQA/wR6C7Eev4rA+RSGvcKrxHApafS/04BeWQKGXuqQYkKrw3ahLRWNCbFiF7Y0uwaN2BVD9IrdLis2SHCR1pDjkdInEBzIWXwjLzyM42tkmY5e9ZDwY/HIleJBeSPci7OXOo4IYV8exLicoFGv62MgJW+sa7eNwejbP7GTAuXtS+NwpdPUAxBTKoIkuPy9n4aNaaQxylvgnis2Zxum0MzspBde+YEGBlP4DMSh/U5HiLOuYUWRdUXV0J72TX2ERgtd3KBiaPEgueKxdDzBUcRIxT769BZVY54IS/zzDpDxJ4ZA5av7qyB1Ojg74O54JOq2JQwmhts5a4GdWnEFrpCt2Eeqar370y1N4d9vKacwYWUlrQoM6jNplf911SFyqeiWZdE6kEoPc77iz7rePu+A9vZ650PXr1rmvkY4wlDv/lSFS+pr3HpsMX7RnfS8YwSmKEF3IKTMvHdJ/2x5uc5kTObfjbJB/mC1p2yr6MEI5+nuxOp0xIc+QV/XkREPelerpazXvtxNYjjxoH6X0CM6IKtzqcTJPXUKj5SiKVBMU89jAunS9taBGbv2PvvNaWqps92qVjbz/XtkJ1dbdaB1o6pCSPWBO9VoJ5uJq/+WlBhHRnV6JtiisQui8oKFopP8+m/VTrwTtT1yEkMoEplRnpY0d2qwQydJLoX3RPz0tbbfDFdi40Vv8pEfk2HZYIRTsPRyFG/Sz92b6D5Oy4bYobd42tRNbBkBusphu0/RfrT9SuR/raXm83klYqPkcNLFHfotsEJxLEYsG4vZR+Cj/SK3jptLDWofQLB9jzQxoyM/Xk3voBE0DBDyTmge4SL8BGbzVvFIKv6gaK5YGN7b8FK2dhwu9yTsk+BOVLnYsi+UiGQKT8IMVhlig7IIpRN54i7GeCl0Edor+BO85PSWUWCAwdB5lL1FAhg/RDxgjiwemS/COQgUgixt91b1lNSb0qNbfOzFIT5hENdKfdDaqzzfXGOb+3/LKeK1RXjcKtOyGa7cLQotpEm3ET7SEjXM8NHzy9HBXRMCbE0LlxCsxSz9vu9VHuZzmSQh5fgRT1PUCb7NZCUoJNdBpxFJkiBRfsF0TTY3cw4mIdAEDkBsEWrmSWHYo8S9r2dhnI/92aJ2XVxBsVUu9x+txFBeqiOZB41JK5TZMFXe7yU0ghHpMTCbkgenn5vgongWIKz2aAUyqb7YvXozQvE7p44wy6PoOMDq+r4mru2XWaK/FNylNUYbdDLCXiGOBzEyyQ79hRRupGD3xPbYiQxeWNBRXZwD3WzZetPO1c7d7O5fswJQW9KRJD1MPbM0JAcNrLbUI5z+6+jQBBTkGNF7wmCwu5jMhkzUJANayfDzJyk8zTmTbDhtg0MoK0ydEQt5PHKHCbGl+fchNXQZDA81NN6MaviOSqSIaciNI7LoLH42E7nTVniobiobD9XB32wtihoAx8PEbbjavH7gDYyGWv2IpLBB0L1spJfdisS7V4XFS3DHd+P1B7q5yBxDhtTlM3p7MywsTyma3s/8NLI0VxyUdEYV+MvvIkd6dtZ62DTDcqmqnQTqyb+OmvCcZY3Xv8SATuXr9fUI8PJ/sGOFJRJJUu+uTZtpli31Kjtm5C82RWTiPeyOZAdkYTa1Qos5q2rINiQr3WFEUL1qRGXlq/EyDbpnG72fAFgsDfvSk26s8wFOSTrU1Ls+w0tm/ToSqwXWQKYjKfiSBpaA8cjz7B7NTOnf8D3RfYLlPLC0jKI4AVl1OHaJ1u+s87UWiphxMrLtOM1mmOVR0uoSDmtSbzS8VQHtUyVvSkBAF2AL3unIy+5Ke65YBeDMIUZ5mMui4w8SCKyIFl+E9ztQ7wjDeguIvN7C8B9l6rzUc1Lcsg2s5rk+OgNB8Arv4maqV41bleQIss98cpM/Q/FUFitRxLEHn3RuDSmciJQnoIoFskFikpRkFCmZKMuFfHH7U+//nFca7v8ow9H0GxeKAIdZLvv/4MvPkulbUoQUV9SMwXmuyI6Gx+8NWscNkftJ2mSPrT61GgQ+YtmKZ5OXA/BVezm5GN0bHN5gSSJAAYM5SqAyWs21BRS//PJEoHaY0cLKFyML1MTYYo/gyT3vTkGZmmhJVdkUpd2lHp0cG/nbALktC+GKo7fDlxq2MYvEOctqbvs1WXjUZ2IcuLTyXDmKI6VE1bJuC4XUce5lYfrfj55qqdHSX9ex7qkFKjyHuGAgranMN1PKm9JCQG0nc4ffH0AKVAhHINAAoTFuq9z9PkRjz93CDiKx5HXryY8nwthC/k9udZdH6PXmnrNzhG3nt/pfgaZ7pHllo1hroofC/24FnHkoTTNZXKdOFfAj6BKyan9WMsvy2f5DEEgdpLSajvP4r7RzzI85b6tbQYybAf8e0lZqAb1erVdp/I8/u20lxia1bieQknPUzK0gjE0l1CGC7iEWBLpf0xIkdBzee2rSpMMkOdyyw9p3kHHz9lizSVNKS/E2X5BMg/lNYiEUcavg3Qk4gkOmp3RWVteLYLJWUiaFreVbgdJmVZyMT+L9bb9NacNLKbiFfLdmc2gqytZsCj0NgERKX2Wc56s3Cdk8cfOfNnjOxJsi1KDPUxVMn3PO5gy00oMQQ6qa5VKtxw6d5rLXV9Mk0gOxVpasruLqwb2q9appaobWibUMZUGb+2ZIxtMzu0NEoG+yS2mTfkQhBo7L30h+wZMvj/Zp2Phc7wTVJOTIxFGjGXY/3SJbWh/sB09Ih2qguRGsaG57gK62z9kQ9oU1QMS8kWSoHuxxl4cg/CwJ74Qg+ENeY7w6ElszyL/AQXsGBZkd34n4hzrYSvAyT74pvePKeEWP/HAxI5Ivp+M+FDoCtrpyd5gPdPQsIfZnmO0age1iNiqqs6ATPxsEEE5MDro5YM4EdvDAJD0ZAeqGO0KZmNoKuiK2r3tJmLSbaZJoSpR4IUHqzuYrrdVkfewyUcyERbE9ujZxEvHcPcIJ9Z62tMVoMuz7v5pj01y2ohHPrLqIOWztsNNrwBIX9fQmwtSyl/3crgrfmtRmykyNsmg4echb3rF6iGBGioKvSeL8fpSLEIQDWwZuhR0QLExVRqNULMKmxYQJM3+ofk5Hpp2JA/c8vshFYGM6hi7LmOo9/XRaAzW0/u7wl4rZ5yxV1spti25vqBeAZK48E3RFmFX+1J5369Idrs8wkZtOt25FRGcy17F1k4Fb3J9BGof2UjmQWBSHhOOoDDci6KI1gfXfPPH8/Gz27wLG4+adiViEDjSVNihpkHCGI67ugBd1ZVIt1aiuf0Kv1Najrd+Jp+Y9tFM6SFs1ErvaR5vCDSAC2PYNFqbzwztm41h/LDmptxxQLuu2HhjWoaTiWJ5lRgavuuWf+dUuCS/FDhTD+4CmG2t1sgW9E75RKA06LNUbqwO4mOmv3gdzKqiixfCO7NIoHO8o1zz8g6lqvqG3w91o3APdtyLMCKJpoBr5XJl2yzDGEgFW6y0HN8e9P8KPX3/j3lg/MeEx8H6s2oG5ySfNNOldh3U15XKcElXpCHgtWfKknzahFeeqAVSu9ikn5ErbBVbIvGtBqC8UOdG2exuQTxhyrPmJGD0Lv/lx+YbweR8e1Ar3Y2H8ML+NzuW9hLqmeJyatC5Q+d2Zy0nyOcanGmQ4sKJd8Al7jhNMbczVgbM1dku5R9f5bzFHuZ2OzuQC21aTvVwRhbyo1fcLcsqqsuWUZ64OUE0HfYOQwF2Gv/It/uvvfM8FmGPfHJGuFRBUGP6lSiMr761KnVw6yGinFtFqTsky2GxMYJsTBCjDr7V4l55RPyCeknsJWOiwS94qqGJC8SRX5Ken2f8atut7kH4aSEDo+4tOSGUAAntUCfL5QnmaWg6BI+OykJx39v6EpnRaLb6oa3YtJvKXacbCYHp4Jmjp2ml1c0EKwh6z/ka2ad6pP0LKrDFo81GbV5E17U4p249tjfpHpnrt6MAhsNDi2bNgxl0CZkhjGpO681CuKQCbrX1spi/FqAU9mKinybwGya6fdS18kpNAyWK3UQ3DPvCH5NNRdXblUS2KDksUjLmd82DmpgloitifwcFPFNBaTJNKSn2FQ2U2demarYB8sHAc+rvJuZk+va10TX/5nRKmOIepXPQJDNUnqJxJn4rDW22g60hIgKPTejW+MvAwFJkI+AWn99Pk42YsPF3/C/PnJ34kyfUFSM3zT0DnAIh7vZa6eE09xogGqIXnnVAINZUbsfpjgK1xIkG5Jm9M8xujkhJBxjEViJLjPi4o50cnGvQAjEl1rFIm+TEBzKYh4QCpmwmsAdQXQnZBNCeXsg1qmtOTfvMBP5ImGnwiWyRy3ZO4pcF+G9NUEZGdXZ7X0G2i/Bqu5WgxmleArU8MzAIg9QSNMPScNk+MUjFTg0AaO+SjdOAhKvqBG8C1shp7b8c0ov5B7z/vUEMNMDNAOZjSvQNQoj6uLjKE8a/AqebQeXG7kf/H1lMElVDovQBDvAMu4HYVZSgyp7C7f8zQZKrdxUQnAnOiUzgjOBt0KA/tvmbPaoaBzUBaMp55HjbHSZluioeYso2jlZrzDs+ySK765yI6JOqQfsNedJuhnfbL340HSYQRPSWzYp0pux1Yem6+Lp4+B/7XzfjR6ZyuH+9RyG/Rl0/RYTFIl2BktMPEZai6sEZm4o/8IPWYVyuegHuzuyUECX/QlMlDOi8YDy6hoUOdivScvmx/I3deDA22pHBgYamZRDF/7A0p6SJqI6j9gGV/bVtzbu9bg1lnnuRFIKCYgGs88vpkpw9OSMz/UKKO6QAet0iVveRhaxjzkM4oDDbfJ772j4fK2/oNGsmaoKngJr7xFGW7ifwCsh+MynyGA0erJ1hz2BImr0XuiitdbxNC7yiIHzgY1DyuDOahagTHl61imiI1NQ78XkOj5vlaOHsnd2R5YC+zi9yrD1nRIiMULmhnqg4y3ZjtLUILsyn0vOKYmmzJeKnKjvQcI20Vvvx2dvAy83FDBoGJ7DZkA81YbjeLjWX8XHdzeUxVFwyz8zIkS1T603O+dTBbsJ8INEGzreYaAKOAzq18RxWcrt2z1TPqk2WhCbQ3xFr9WZw798NEVkeWFOo3aSFv4bI5PFH8i1HtnguowscNZgqM2Z+Wred/Drss+p21ptND9h5PdwrLzBtEfoI2nvPSqypF4aeDl2DZ+AekXBLAVx70XWGwV3MV1Sb71iy3hCFYUhduJrKr0K5mJo4bOOlB/wU/QXB56R3v5wmetrBow+wS1AEp3eyVkzrf+UuTDxVzD0U6ZKueCRBWxZ0NTyhsLQN30pn2vg4VvZ49MAIXwMWoDX0xCcO3DmWXGXfoDrDchMA0IbGeqTOD5HBSXtQxcPK+3FdmW8cavOYZRBbyg5ZzeRr9IAnDjsCKSRwkN2piOn8GDuTkDQ3HJNZ/mS5Rc6u1Ljjbfm73+ZYKbydMozTdCee2pW17aVpYOsUVjvhsxTPJqfDjk86JAtLUzWhgAiAgNwz6ZQbvQ6Si0chzjatj5UdQQ1B4oxJ2DRBGV9va2WLBhzxdoKVAAAgAEAAIAAAACAAQAAAAYAAAABAwiAMwI7AAAAAAEEFgAUgBae/BBuAYfqD59MMQv8vXi8P+0H/ARwc2V0AiADz91GzxnxEIsb+SQhEtW4GHzV3A7i0f/La05mix0nwwf8BHBzZXQBIQlvE1uiKGEfj1uvL68qMnjkKuvM1a/RRgA6y9j8C9j23Qv8CGVsZW1lbnRzASDsW7Oho1ZqL7N61KZgQaHJTvjxKZUkBeYf2uG28Z2DsAf8BHBzZXQDIQq25tpy9B0rky2oc14UUM96qR1R70rgNO3pExJuuz2NJAv8CGVsZW1lbnRzAyAfFQBw/QD5/BgXcfGwC1zMUYsPLSjkSoeLTqy6cuYC1Qf8BHBzZXQGIQOGDl+vjfxGNWudl6wc9g7un30h6JjEIdB1dtHCEnlC6wf8BHBzZXQHIQLmltwT3ltY5sC9/R6d3BU2SAJ9FIFDL0X9jhMoqsxrRwf8BHBzZXQE/U4QYDMAAAAAAAAAAfXgvAGXt547SUmsLwKq4i/ZF6K29cPiV0yE4exf4KYjn9rV0I65dBqvDSX/rnlz509CbdZuPFCOHv/04oXA6u0S/GQbT3rBiqVs1ML4IWPVAPeIZfsYz058CgYtHdBJrVzsFaeAwb8BDk20CR3hut+rtlzG4pu04i7XrkiQFfnGnD27yhNLFtM1Z2FuL5AGfMAZQjC62i48w5IKhD+bOWB0M1nG4tuM4b1eg2z5dL19YPJLJ5x5D5W1tRyTjznnl3TCuqBWhjiKllthV1g/1GqgJPGawg38wm5u5kOOZUfHCBq9dzFC+8CywcoKJ5G6mNwSHmFRYiTiTflXX8BnoMODjWL+0sFIfkrX9u5RGPdZGJ8X1AnUlpatcmX5vCK9muSJ7ccZ1s6Ug545pwNyCbsqTL8zsPsShKo4b4ieuGxnoAlX+YxQku7THKtCHd3nHMbT4QK1XxDv14uYwZU6AtRiBJMgJGW+j/pt9SuVhVZWfea80eI6JjiuE24Say0UgIufSSxfVFsywQ2lIGfzb58Of1P9ODukYw32prLfPs1Wv1IoIRw2pg4w6BdVp2IjJ32iX7uzVQsev7IlSDVa1Jnum4fh/A0LXtq/r4qHsynCDrclzEPezxDdQCOTS43LmFqeYT+bZq8e1zwIMPO39CeX86Rctc6srXVydC71vEVlr+O5N7Xd/5GTHJST6olnpAfl8a0V2sLd8UP2liWarfhhwYdgGkTyqHrnWzv3ybC1njtwkepbtIue84GvybyacusDuh3nS9dYa6EUfMwFUK+kQnHkLNk32YvaiMO0oOhQaZtewDZUW3K2bNCZmvYTFhpuXYzNnydvSMraTTTIsctuzyr2hcnxZKeuttw7rP5OF7eUPJfDRqc60R0KLp4jGyHTIMuFg76D5YiHpTVze/XnUuFUltlJCanbivwVNLYpffae/svDzYjT6oivkhnR5V2q6IDzpp6OcT7vGsqcimZ80x9hSFUFen9Z44RLghotRgRmJ74f9sP7/lKbmjnm9Yhzr2wgRxtP/uDaoicT7YF6GA3sVkhsKekdiMFYpJXnk7eghhklG6RMUAjyddC0C6AwlpihglkZPTfukmMIBMRf64jG6cZ4abae2lnX0A5HUROzYR3HV/YaS93ZkZDz+m6m3UcVc0SjVi44PwxnaW5KQe9Pn9zJ/CPWpyYSR9LTp0VpIaJEKwG+WCPsyiUw2Opq8BXxAt89CoL6SpDLj7HEBMQEN0N/wisfjeb9q0LAqIyO1cnnR29xy3vs+HJEGlC87TrpVsJ3M5aZSfYCE5zfhRUq2oh9pz5xNi+8RHtkJ9ikx7rYNbd/LPBDDZg6Jg3mupqjTaOtgftBzyccqq4xabppL8EhFoO582i4i1u2E5cnbe+F/touv05iC0eTtPam5hwKl+PKTuse3Plll3AtyJaAGW2Y6yOyMajhEdVnwqVmpDXbaCTZ1pyDXqk5SPDjhn1bHVn01eW9zV4LP1jJFl/rmf0BlASIZInYmppjQnxL2ACVSbi+C2atrUlzRoyTZI8WDLuEmRHPqQpV53vIoCwWxPK5iZ+84vA55SW3R4o1jwUCPXR+1DepHJfxWeB/k1xkkzhODleQSgi1R/zlj+EJHyY0hgewtbBVLe0Wz76IXyb1la7mvixl0uJkbLefJ2le/vimkdXsAzgev6HCNeU27TWISdY/FFGT2ZUK/95Fg9WEei3m6lg42uGaq+YZqdp0AF2DWnapxzBFKwh5GufR+XNeS2Gm3Cn5A1PStj+JCLw9NdkWiCxhM/Cb07RZYgmxvIurDTZiDv8jgZInTxIQiJqVQlQLEFRFGE5+MUlg4wGB5HT5ay2haVRkPJrVLjl7jUyMFYEmGS7z9z4zzbWHhfZuiRMMM31UPb5X9q7389b6EddR8otWFp6rJiKVde9kT1kdJJTeByvWCoBvNA+bIqhduL7X7yeg7TXz3cdIbYvjpbJKLU8ZiYTmEybSMZF3pPE6b5qkC63CjLtrePgjo3BK/g3T7hDuZ4yXzGnul8+xDDo1Nrabqkf/UzhZEyKcvbVwglwE4TPQ9+iREVjl3DFxeti6hBuF5IceKPn2qylItmU+ZtoZHlQnB0zq2bM4s3I2BkobTf8dhEj81G6dpsU5Q44bayVX/QPzNk1PvSvB43hSMDjldx+ooypn1FWG7VoonZSSrVprkSe1KwTxnZFsYTMLVaZ28dgiXNsTYofVJmQ/2vBK1wxEo24GXvpcd6+/AdZFh1OxORIo+4kgRKhisoGRKMv+VUvFjAKFjiXJPf+GRg5QU+O30JwOP0j1BbXDFPwZ/z3pJfAwgjgg2TzoGV5m5iHZJwQDFM1T6fVxn8zkI9u2uc1fjwamIulXPElAJHlZjWlUnKvlrf3Z5VlB16ComB0oWQvrh5JL+fleKiH+W1yNtp/s7r2ABO7SG+E22gszhknpuJaCc2RH4sTpFEaxkyY69YChyDJDcl+D2OfK1bH8XImo3nM4TdryUifOfndWyDMpopHL5/+PCv3H9yAROqeBSaBcuxHfTDR8HnNKxMntJVWf+xGFv3dOIHspKjUQpUpKmt0kr2s2mYdFHCGv4DBoArL9J0iubt2jvTeQ9xI8RshqVQbz8EmszO1nBYyfXkb+25y8kKVsdJNVCg9jRghxOBqXY3ibO7Z7tIytsck1/BO51TaL92Z6A4foQGBkbEjNOocCEtx7pjHipGRyzgTU6UVrKziJhRdChU4hvBl/mMPgiT7jRtKbTJpelYMeYlD2w3yAKcKRP9i6QNwWVfJkhZTlXQur/vEpeQ95pmmmeXz7sTNXKKmpYWGaUWpR/t0BCUc9L5852T+bf3VQ20u4sf0jJX/jVd0MgdDbiBWqdpxPkhp8p+2x+DqchPt3mg0A4VYgWgCCaRs3Dcv8UlStbfzSlx1UtUV/RCl9YSjyS79eClg4PuOfF7iJqqPPvTh6L+4BaCebDRVD8d8OvpJvE2gQD8NzygS5/cScJtIRZ5OAmchgsnwal+Z2vo/qVf/voAOCrNlI3cK7S0bNXLHBO00q6+s6C54O2qLT1aeeLbrYpECzBPpTZ9oJcqr1KO3aMoYUa4U2PGndXcpLoqtJ1IjSpyx5xpi9maUG0ULEl+zisC+f/IHnlEOSpxG7+MBSZITi1RK125KOTCVdOLjFmi44wp6Ei3poQPJg3/l74uZPTIbr+rRrgjlj0N5jeIq56BdfpFoJzqusABI1t4WLhZ4RV3BNgbkeAIdhkBro5Q++e2QxsRsMRVL3ShWihN1+Xwf4BGZpop8b6EoQLGEh1uVXMYnLGn9fbqmPUN28h6YysWcgcb3uEUH7G7D/PRaYM/6B8+X+vgUVwjghEVe0sfcxvbuQXPN4tbJdJy/TigoNWcnN8GHnIHPEt2Z+2D3VeNt8/c9LnT9vLaaeGgpjRMnlw+AZGyhkeXR2SwEjTOgyENuYY1o7D6cjRWcCGjcpyPPRC5hTqnyZtKDFwpLCfwqY/iN4OCKRl4Wk7FIDysTMM3FQpoL56nIHDrPb2TXUuunx7KUJHzX54pNedLsXu3iZG3/OHHpeH/vvtUKfUMS7D21B1g6mYbp9azXmV1x4PxplH5kKnnqjXQNR+etnJYj+EXnglEVJPechBI97rQp9WRaJ0vl9GelAtj6rPsT5D+xNwNMVNDy4tNkDH7pvrl3s4dyMG8aZ0qEKsO6gt+SWzDE0IizeDT2CLybPUP4xRvvpHciWAYzxdoA716aE/pe66YAJLkr4tuQOk78YQRL/yA2Rv4iqIjSNPDhUrdARRXDwHyQmtckWWGYrGlWtnKxMHtNfT15UGb1DWnMJ4PTjYHVFs0bdyXs27vvX7XbV8kAS8wYOWvLDm8uN7rd4hRG4/jQPJFbhaCWUHrTt2wTCCjqr/B/QWthoLsofINEHGzEIlOyrgNc8XYPTPoiSK0omecDw+BvE2xwBpDoIq8W7s5b5kvyL9nEGSsfLxzFysC4Ob5SI6JfUjoTRWzm2zjqDu/bfvvJ18YK8frHmDGRpMgjWvFpiw939rOdI1cqUjz2VoWqGhc5H0C3K0QNkcFt1ywlbXouEgslRvjLDXop0J/FgAOmPUZajFW8SVqZUn7VdUwZMFQSbI/lJXnT2ODtX/TFhd6oucqH7mm4mWVgx4OYDhc/6+NVxxNB/Cz4+oGnW/2vu2FUa4eTb3YfJ6wksnmnP4+7+Lsogb0m3pZj39QbLAYD561UMKok42wR7kXpvPJ+2gSI/qW0jhYDxTYdH7FNlGXQgAp7DVGYwTEWJ23DejFshLJngzjfjdCeu1v8iJVdoc0wtrLkkJ2iVLE/0+XZAsc/B0/Ifp202UOo2fUpv+cpTVASFt8EIujmfwbCgBtFyWIKWfa6WB0T/FOWnYd1C3bc1vpsS1B65/O1RS1r6W8ncbguxZ21nwEMFwF47r/CKp7/AceqKLZFY+fKThUKylhu2WmZMPuSEljPW8uHyV4SNjSmDrUBYPvoFCfdoDMnG8JXgfJbp6TWumkd94fm5S1ZPJ1C9yZGNsojhr8nI8NEFRISrQZ7IOENU7hS+TUBNlXk8yca28Ljx4i8IxM5OWktVc/VlPGMT+er4L1UBSjx0jrw8qUCer2X+LAmyU9gVDyWmnMXTuNal8OSdZDQC5FOlfjBPV4yfvS9dD+M6TzRuZnbU0Zple6Al3d02PBmWgZBZ9al2RWKOymqctoPN0ajKtCSPBk94DHZq/Di2fHiCa6KFnAnQqDqDJd1z1s37luOf9KCahdc8lEGlLyEp3Zxzwwy5+RZ2NWrAVJYG0jx4Itwfm0uZqbBJKb7GlxUk8q+kj1x5vf0K6v7JZe8FEu9ExEADTIWsqLYI6bf41CUXVKt0CiKtdgkHvAEozbLT0epem9wW/MOA/uYDCF0hmtnk0OYVi4MYhGVI9qf0UEso0MUaoYmJyBDKQBsH0bkYSOsFtAT89Ks2yhfc80sWS1pnGvAZxfA6xkGcaq1FHi13j23VQc1vu948QbQu+M6AJGAZ0C5XJyT3xilfuFGlnPxWzzmbgoLuTYwI51SAP4ldbwzv7CvFoLeMJ1bCuwh6vLX2O3dDjSMx4LRZc3BwVjHFfrFDUngcSM6GWfGFVCPna9PNcE+lTswrEfrIUdKr6PUhzKWofHkOz3309QlBOYV9hize2cxAOjFrr7rP0aRTDC7i75ypBwFLpISzOO03NMq3/Fs58GR20UAkI+V9Q9tc1mNc4tMvNYdli7Bix4SixZsYANcKB/WbF/tK/q6byCsZm4WLIUzD04t2aX4y65ZR3BkxHKWpGgHPSuR2PYc4Ct5ZOHP/P8kKUfDl+QP/299SGHEl66LEKLH3Te9EHALHbc6fuYD2rruy7lbTEXCgt0KedfLXlI6EgjBYqRzKdXa+ErzJEtEh0Nh7FzkCij7WDwqRzuzIpJw6nW2jUh46gR3w1XVtyEzyD8rIcEmgsoU5CmxCEl/54Yhu/0xKXpnCgj0uhWSaprn/MiT2j+BH0c6TzMDi2CuSRzBrWEiLcviEJL3Win6ZIhFSiwlLjQf8BHBzZXQFYwIAA51iqy0oc87EVYugNxRH1rBjqM9nvVHA/opn1dimAECFIoRxsC+b96HZqEZkpSwDBj/egjFT5rjiA6gvSko+43+XCDF5q8/cvbtCHrn+R/fuWtfIAE6TRP+XOpJ0f80N1Qf8BHBzZXQIBAAAAAAAIgICtnzNQ/FEKlH6zLX9ykQkha4BGwHSuc/ymM6yFZglAfsYc8XaClQAAIABAACAAAAAgAEAAAAHAAAAAQMIct/1BQAAAAABBBYAFDHUORI3eUFKBYByM9r5KsMZw49aB/wEcHNldAIgGGls8j4yCaboRHnQHxP+eckBNWrlOQfMiCz/0+JkASMH/ARwc2V0ASEJmghZLhYbEAGv6nOosRyEuyoZ8TsfAxipRf7SjuEtzmUL/AhlbGVtZW50cwEgZQ8asG/IPC+vaicq8TGfLYoF/fImx0kLI4hka6ryBRIH/ARwc2V0AyEL3zw0iObOXjX35vrIBpw1vm1jEfDXMuEavDmdQ54qBz8L/AhlbGVtZW50cwMgSiMYbpc3xgb4SIAu4rsR3zYtzIK/Iypa344UwgmFw6oH/ARwc2V0BiEDoAHNF1/99wkb65+cTZBod6eMN2ZNyaBLQd29kzdptVQH/ARwc2V0ByECzVmFgDl3XzJPFu/sXOZTjHe6orkNqAWbpT+bvQ6qbUIH/ARwc2V0BP1OEGAzAAAAAAAAAAEXcWUAsPjQwCH/+LmEKd9uUpdlVKGjUCrkHL53FrY6feRLy6NLWsFuQMtHEhQxvpRc7SCVVrIf6WdYlnzJJesNhBHQ0Kjco4rlIFb8DSiKVfOxQYv6dfDASKF5kGPlDeJyZ3AFi072pRax7upye2E53XXeYUyG9hA2zxW3IO2S2zRMOlUkH1F1yPXn8Ga8mQG352cNPPp/c9XbBcJy1bMXQu74ih2P3udfDhGASIFOgsl2Z0zl5dMYq9SBpMSfaVdR+i6j/WWo2SNTJXbVZpVE8covoOowFNtwz6v25xdGgrEYtPvAM5lXIubrVA1lUWIHVCasU/tRS1TA3rvXsRCfK9uAmWQ5xPCp5Sb+bqFo35waPCRaGmhH9TAwqbZcvmZvmRu+TcSguFC8h5oOesYnLZYlCHuXmXSEv3XI/nGoA5grLwWgAHyOwduhzfjBZ8z6u18dXtY/PvaceKfSiy6vA0LTY1/yz6wezosjEERxpDEN0zUKGoerzpsC7/UsJhcCC/+474fvC8ItzOb4fs9HxbXNGKFiQ9PgC/h2RhmiDXTYhKdSsJ7a0JzQZdGGMWcrU0xcGpz6UoT7ibGIpsH+0JkjtR3IPVboShj4MeMpgVg19gOzPYOdSZOioNdww3DHGIooluXuk3qd5PTn6N8pGTUaQKBiy+H/qDU5QauZaCQkuYL/oQGJAREPyRRx9FTf/rq7Q+2+MR1iPirSAzlftU3+NaY9TITUwinty02s4oge0tb3p3DMzsirLO0HXXpJMfhaDCD1ZlzZDo9o+WpoovYq5n8+gaTJGJfINW0r5x+lxQffvtUBtd1bTf3b5nKtZfi/WmcWj1tzq4KWWDiXEB4WYHjxKXLjPuJUlx+Kdu3NF4EjZm9NiFYgA/cOKcgfxsWqDnoVFIkxcWS2Lmwb8biHF2NHu6aai7tabedi3hOIUoL5/O/fqO1JJ2CpCEmL8+LRK3ugRh2dH+yCV8ZKyUrRqDqofOv+j5hywJuMK2NoQersF+hMtUsJQIV7YC4jMvf1Wd7nfjt8/58GlSbHxJ6fIr2qUoZvLuHiqUXwAHx12lm7odIDDKW5ey2CZp7EK0RF011nPQhphp+I5yKi3SUnRzRjuvZFSrRTRcdzEsjkarUaeD0wZ+/GN2qlGlSaQV3p5mpZawEICdfV29Zv2QLBVEtCd0ry52TfcVqP5fqgx+T/7VEnq7RpNkAiVqrUjzOwCGjRHNj71/E2mGBd8SUmLBw1jjudjLG1NX4kQ1ZrktLc/TpQ+6ArlAO+wUL4AA2hjrZ5qYL6kSSoDll/KSpD+11DstBUlDkMecdbBP9af8I4NEEVCufhbRAvo3tpOFv7SyaMHZ0dRNkXkBnTLSHkYJfsFj/a9dEGjvfEDBqEYKeqSURvfpio3r/b2ea7gcX+pPPdqxQE+BKoKXNaD4DA/PlRatnCit1PYw+V0IrfRc43IugqV61mNJmXu04/9sLp4JNyQToDaWL4jMds3JPTT7NJJR0kT/fgqBixivqA7b84F0qS8OF3OPmcmbikXXfcHmc94IkWyyQT8o5TWP9V/s/ga7YLDe7xO1xyqTjOue7nhYSGo3WAazsppPNnB8gYJnIV/bxpgvZ1V+0Ozb9ImCAQw7xl9xOL43f94BD2DDQwl6I6QamCjsD7WWhUv4sF73Q7M2TQPS37jM5MuF1awALXfnCiaaqXdgQqDVTMPrHXO4LWRUFOaQNN2fiwNcaN3Z5PsMWbcbgAFqnta4Vk3rAgNbb2ZvVFOg4yqQsxvsxKL9Tow5EZOLYqYsf6zEVeqJoEOhmq4PSXa2kRgVwMwufNsqewYRNDkbPuzNnU0Bh663soSjTkjrIo5GY3QZV7/PQ4mdP2ReCb9shGRpD4/tcZBTyn9CwYRQn4UsoJoVl/idsZVUYxkwm519lcLsXG1VPog8hZ84x/bplzUaI/V4aNrOsZuiNxBktZ2iHhk4Atg8T7y0L+FPMvh6+QcGLFJlYaXCKqpEGvFyVKzoAaAiCXdct1+dDZl+stxKo9UMk+91+D/QcFBA9sGCZxmZae7daolLHmCl+LMSW4GnF3lktembbD/yVLY8YPfeHbf5N4WSMLq5j6Ebquu0egg4oG9P7wyfU8RMlLITOxx2j5ft0/i+KUD7qNC1kTjzduSJMsmHejUvWVZ3t7IpOLJ1yACkMdboAQsjr8dfKAwCGWmIsyYR0kMKloj5HY8/Wuv1ErDpmBAv5auxPx0BrKVLYhYvthDWJDRheMU+uT35s4ppnuKw/xelmlGzDWWd0g8CCxwFmkHM2WxQaM3jOh8mp7BNlDq6NJByB5fzwOzDm5LVRiBo4DKOAHVQUYp90krHQTNcCOSEU8B4Go47o0uxEGGzNOtx+Y8Cz0xqF8vWwYaPyfdrA06lTA82gNUWghYtqnQcM05AxmC7Vy3g3iF5+UZZ05yGCYKI/iNkwqbAVri85/8R7/wI/ryW6Fms8D8A9QTeEGORtsxdYd1IXxX8NpT0rWFduI4+gEIofjBfMlUAMG8QQ//A3lsUg3hsSEfeu008QEAJKM6KiH0jecM2q6v5DvEstaNw5B/lxa+4NSNoEajJEJVEvDIBEh028FJpIhTkEfHM0f8++9dZQkppcY0AMKNHviYObDVNMm6M7sHKsiL8zuPa+5bjFm/xn1FrQeN6udNoGZY2LEeoG8Vl6qu4yXnhFeRSsWdm28aN5IfnDTbWBqFGy3TMHS6fKhnWrWk+Xe/UxNPk5JUVqG/7kueozjM8Shi33m/+3uzs8fXkR9qbM35GRBaS3ZXLDDc70H6NCN75iEJExr78o7Kh/hckkqnbdtfhQU7STdZZVZeUiDd4TpWYcFpl/XZ4fnKqkTMCeobQ3u/dQvsc9ReDOIa9ey1NKigs8AEyJZIXfV2oUjGQsLk3xFuIdTZ4hGQiBR33U2lCnFkL6v9jsfN+EImS5D+J3MAQPYa0DNpT9Cc+hpa1FcvCBI8ZxDjDQfpZ/dXKIiltN+c7nVbX6aYB+sBcvx56aDIT4cguATqMUkuNTOqKNHC8nYtwWbVggMslGLhm0mx2aNFJH4ThJQPsBwKtNCwNWZEQtPqs+94sifKU4r2xabzCffZngrDNuijZrV5AgB8DwTnCnEDPLdqfnZgQKknwgEFnbTwEyQ6jk6e/m8EjVjSLciLx5ZjICW5Zbo3lvj//Uip7597PAy/yNyNDju9j3g3dHFr4d0LtEBDUj2eETyN59PB6HdDSgkiSWxj2d4yxFLsq7zUoDAR/Q0hUncluQ0jHDweOe8H55oMoWtJHWwE4aPiMm0V2BtwHUlP8P7rt7HDDvEijC3nzzao6FRrmRNErkrqAll1gZE2In03RIYoFuoO70pp2fTDB3TIOh+B4k01s4AR79n2+icvu1ZT+bAuoVUQqcol0PhfV2ZTE/+1RWszdyZ8pZAwzKgpobAFKqa6q8qbNZFwqnxNELzro5llDXaE7ECizjo+E0e+Kn8TXo2dX1Ds0/MlRtMh9SpNKsB6ilOboKGFjG/+vk44ucCwJ93VqhgdBo8T0yiyiNxB4vXVCCjvvGntAUvuMBqL/p8J0PAZDXgPplBPYGIrc3GeFrP6PlYeDlRy2LBjKdAExPpeHK33SNeTpY8AAA7p1nECHmr1WQCebE4iMDQt2ALLphrtaGd3KPt7Te+61UfZAKkdshovylM+ssfPPW5CSqb0BZbsetH82UueI0L4+Ed+okGJ4ppx/C2BRUn16uMKmiHLEQEpNaI73H3bsuhy0j26eYTMkX1aWwRjA+K4+3yB1ZVd/UwuwEzWMtcCljhtfBfwn1VGxeGo2VJ+FkedQlW87Exe8kZdPyndd0aq9pouJ4aRo0XpF/Y+bIJZqGc4RDmL9Zs/wOxg0PhQXdsWXJhNc40coIqDmpFZVYZSwVu9+oHry4Qrfg9UtmgePIieUY/NKsndp+lF5t6hZUs+kCV31GCSb/dRKhwBf+qdFJJOaUqwlPSuk+P/+h5GAYd0CYUjxzTel2RY0dmGK/dEwwafNjEF0GS5i8k3Qvo5+zNkqeU8z6q++zuTkIDXSRqKr40D97dLDwbXqtPR4gO330mprVgj/I+6ilQ3wT9CB0MUFbG6lisRU/1/Bkor4FfL9ENSHTUqvYYxujr8htbu2DHV/4OFcGm/6dWn+i7OGJtTuHa0rGqQvhRGB0+cCdsehfnwK9Om4iJz3BLZZjOtWPd/aH479DgS6xsHZoWSoqQMviHM+KM7FzFpWpq0+faRCMtSurtsMy0UFJhq/bcEpJ6UFVjWLlybMFaT3mtjLQa4QfBanchcmLHMtTTpowkxUFzPMnZvL0FRrV7J2vay83pxzizymVrXF3K4CdkYVTTWcdbpFwqwZXgmB+/uZx+lrfX+n47YNgEjfA/I3PFgpq6ZDmvWAQsGh8JZgXb4sx0H/VLKylEZtsFcLW5os9M6eySf1f7vxeiJVfo84Oyd/c9QK+7VAULbKkSg+FZgMvrVedHgEOlJhhAOYJl9c8S1Y77EEFQphHNmo/s32U2zneeTGnOjiqOquQmkahMGlh3NNCklTzR49QVoFKAz8VdBMd2DxN0TEIPPp9Lbvew+u+M6VsrGCn1c7/xK6M7qK5kTQp9j+NZQDRH3IItN/k5HQSezL+TGOyJ8qcPgXkxuh9Rx3CuW+toL6ESAl3oJVLAX23gMokaL2qvb2xge9gs5FfwYRwlkwchfU4gJn5UOW6ZsojmeSuyxokqRuBvDWfEWCAhcGnwMLYqZqM5+9FhGzOFFp9EB6zc4xRDCvW9p/sG5IH2MTslC/IF1bxxtq2GRbUcD6N71vSW6t9gXeXpuVY7yh9uljEaGHKV62ysGoPW02Zp1AWBL2UHhBf/KkO+LWhn7tyiDQxH+1Cu/dBkiT7HE3MQpwOJtCcnmrpWoWRvoMd/tCe1JLincgrFucivCO9OlNdcNL9Qg5m1XtKNOShfdijprEFOPROTDeccfIncUWXVZmFbpPeWf8Y92mKKrvGphSd2kTm9En7zH0VGyo07LnLee7/vWIlZDrQdU9QFMlhpm3mUt3obrs2I5LxnklE973AWWyiYu5Wyd6K8dv2aDpmMDRqJKoKJ/rerahgtO9G7Y5uwd8NM3k/EjW38rX9fBgmBBxj7ax6wqVyoi8Li5puX67xBU2YYH967QWAqxv1ufP2lE2+JOXyvt3Xd76QwYZaNC5CkdQEUN7AIMe3Ch1Tx9RsSw3l5Aeab9Ez/tPXYQ/CWDaneTz/oEtXNFu8ikxTYaEtA3oG+AJiQu7S6ufldc0xKglLOiBr6TfFsZT1AuiUAaXdcwP2oZ6MA4Z+xIX9tRdUFcR23Oy40NzDYh/2dSo6iv75bGyoPoGPrcovggcJdvEZvbn0a4ln22ksU8KX+yAqAA1YFgvO0xpvr8gVokfPfjb1KXaYysbPdXe08WM86IVQYmEFOYmt1PdG29L1VIxk7BXWOeqtbZ+Zq2r1QQrzwX3/S7qlz6oA4xNou+vLKG2mNLvfjWiIHq/Vtvv7KfXJIyYfwExASswQB/yYq3ia4lxUH/ARwc2V0BWMCAANu/jSF+UOqEIDq4ICZChsT+q2cAX6L2yVJo/CpHCczDRrxsct5fwlZwi1sKmhHLoArt7hn4+BhMmpZXmo3bLS7X2pB94LslrbMu1ZTBb5jZ1c1yW1NAXIN5wGAao8SQWAH/ARwc2V0CAQAAAAAAAEDCICWmAAAAAAAAQQiACCHiaDbtsYzl+do6EgG78zG/3pwyGilJFg1WCmGNOgCKAf8BHBzZXQCIAPP3UbPGfEQixv5JCES1bgYfNXcDuLR/8trTmaLHSfDB/wEcHNldAEhCCfZwx9dBnL3SB86hTijSrGKWX/5qYc/lVtK7x/wnZw/C/wIZWxlbWVudHMBIBZUMEd5a08HcEs0UBKr2Cc/sNxSTy5ivLY8sPD0njYqB/wEcHNldAMhC4sz7Tjlw3nORjxGF5aVAlHw9J38hiZvSEFVlksY/FK4C/wIZWxlbWVudHMDIIzZwAn7wS9MYZwgZztvlaNcMXSJBFNjW73EF517vxY+B/wEcHNldAYhAoNp6chO9RrYMnBivw3fP18Ug+0maDvTO1iy35dUJPd0B/wEcHNldAchAs4x+BcTnIXxny7aH3GpzrTgv1taxcv2qFWKeHRgRfEwB/wEcHNldAT9ThBgMwAAAAAAAAABCoTwAeIZavZDHyFo7RBMeKJBm0e1Vi5kWSj3rSxLiE1oib0NjiY5C82ISfnqJwl633dUCwbXz8d7S33QBtJ4R8yxqueDnvvKfi3F6TiwplBXmumD8hpid1AT71bhSRg298mq8QAw0605IUQjl9BqQQWDO1aV7HrZuPe4QG6k3d52jn4pzrBzJRW+DhU7KwTfrCgMG9F6HhT9szBRu2cEH3RSuUDWxQ5IUQAj/cR4FwJguoIF9QForjBC2nLgBo34cp/QpZs1qNzfAtf/M+GsA0/ZlXQaCyc8InHC47d5aZ7VNw5v2mB9Jfqb8BapvSGvi9/719yPAZWZ76BNMW5XWO5uXAbGNwqM3aOUFjhh5mFY7QpmlVfmPlvA/vvyQyIwvniYMtNpug21oYnGb3UUzYxJK5pksD5RNDwcJO8Ln7NEV/zTFy+R7DPIJRqyHOoOfpZ4DmeyXvMS/iDxkwV2V3fznKKZeF1x6Ui5OSmTCX7PQLNUrKrkWd20C2xmThBQv+ee7TI8YUMH6imG0GZ/Vs5w/DfEKdyte7mPIw71QIIqjwmd/zm/9PqwQ94Vkq4wLPrrCLtDoXBpATGZviaLiyIgcdD/NdA2WXr+XDAvOlFIhTgE6hTGkQL7K6qo2z81gL1h3s3+nSzN+lcOeNVmQ7eZcnWYv4Wqs4Ow2fJMbh33u246B8XhmveuHyJwi1Fox6vEUbzCXRFdCPJXxMRonR2kdvzO1smfQp0NSYdz06bcOiJBP2aoyjdaUZjFqZ1U9dWmE/ykhTFGqK2mNZwy9no4Qm2T4nfMW4LpsG93uxlDueF++fSaKrFQfs1o00c+f+tHGCMkIbkL60WOfxkUW6IgGm8enrG0S0qrw6cgvK4KNRgsRykE0K10ZSM3SLxRIFZy9hnGX18h8DbpyRrxE1jRr2yuL3yZpJTckDaFrH02t/caz0chO+mbMtfw3m/PKVciAGSZFc+gFB1Pshm62Q/GIuKuZM7FnvTzAgCbgUHnDxc8ZppgrdIYqubhLVkFm/HNjYPnMEwJ1Of0qU+AW06fnRm+1FlMGDgLODRkO+TZfeegd0G0eZF4UX1JKrGNgKzHnxFy7Pb5MNrIsZZTUG6hLwxlxJVE6piPiGy1SFVs/JsVHCA9lKmK85b/qtyfD+q556E8hHhMnjuZVE5fpYs1asyXWtpkBwaOUH8MTMGd3TGlE6UT0Dg9CnV6kClEktH8Ocz63j7Xr6+Sc7lF6CnjB2MFAtMBtLHpxlROalZ+kLShpJQw+JeBKpqIDEfjjm5yONXbJCIjjUW6k6att9OmXw6d4Lol2x8eS60S79VENC2n+kpXYGIZznPntZf4ZGOprKFaHaWuy9yjacyxnGqrRgvzPw6W/5okxdfzrCBybkxk80oxL4q5YW4PlcjWoxsRETaBR1wnIUTLp5MERqqiD9+mF03ZFIFsgaJZNM4aTzhFlUJbLqfbyXOsDJcSZZbBa8rJtQKvRMFDdY2CtakKDlEVfldVprGdwCj069t7VdiVl247LHK3+x3/Fsy21saBCdWMpKOMqer7jAEBPA9edF/RNieavS1NLgA6NCkbp0NKOkaKZw+Z55SdCqzeCLmEpTkP1P3kKlUotFdNBT7/1mOjTO7OM/0s9apfGXOQhrad6rejoEV7f1Qt3QKKVz9tsil9QVFiiBWl6tdSYxQF6Yjq0WQZt64BDlbBOAEjJWJf2S2m/els0T1bgUCPBkBkq4h5Thls5P4eM//KBrr+nh9M7y2xxUOad6Dxn5+ZcRgJQUaKvxva5CwlBkHWDew8fUOReUWWWRyK1zLpsyjWga2UzAMDnHmXGId78mjfKlQ2oItyueldTOcKZDZXpVR4NHtrbskC85a+k2kkvF8VofGmh3PTe3t37OSaRnNNvrug+lnnU4I+drEre8ZxzS+R2bTcE/okpboQ46oFncuhDATd8OjnIg2q36JtFZeQTzk8oABBiEYxA7HCDUaaKkG3msUmBkuIleZbr3ysHlXWAYTIdu4XUFPUnfIJ19ibfzL98gNYrnRV4ZIk1mNOIEZPPqcJvm+O2b4TC4oZD3k4ug00JNsz7FKcDrKug0vzaqp2ip4JLvpPL2wo/5OkxMGSGcS8QxKTW4/WInxvhncFdhp0oUYVD6TtcvGtE3SCwE2kOFTZX8F+rN2+gX2qv/2RTFuXnt/CIDLhqbHQUAsv8+KRvfZh8DSTG0cXBDmRLaFy3Z09O4xJP+Iy8GEbFJesjkijf+aDeSZb+9uvGG1soOPlzZN4NU2TJwbtg3v2JX8m98GetS1kbRcfWqE/I5a9wtcKpXDJnn/752Wydp4YtqtrgGfPEny1FO7on5kzf0hgLvTiWm/eZgCcw7xapa8m5GCdjdxTrdct9ILA0bcs4+R/fjQCNyR4mz9yp7CP6ssNCMUFRBBjUY7I/FM8IDnlKxdAFgXKG3BDFawd2CpVfpnBl1SAMmuYaCzHzSpklu/m4Bg6sdlVdUI/3iEoJq9lfWcHfoSxm4sy2sZQWuUPubLfmMsU8NId7EhMjGxwXFEJbmKBUm6sfzTxYpvlaUNToMwHbeP+LWVtBwsS44G4DaH+8tjaUteQ1xpE38X7+GzX6W5sbJDQjNky3IgzNgt3wjR/8DQs0DhK30IaBxCMewQYOe773veHBxK1hpD8NugiKafkkt40521niWHX1dqzXX31i74s/BOa5rxMs+oVilhRxBWocZlylCKwu8pNnM6KZzBaI2xlIs+L7xdndiAfuG/Xb9BRHyaSeFN2zAW73hNwFlZvDNEemUPTpHiuH4mzJ8OHwo0pz7RqE0hp4327cZgwrYqW6Q2vmHgFQdOI/oZvc8hQuij9hcsPI1ySWPlYuSZQzaEubwQXM5xf5niCkJ7+fyVq2RjtOywjgKGS8VososhYdu7kxokPOqW0LpZywtddFFjb6AFbZOcn7a1v3WunLk16W3RGavACsb9jt0AKfDXZpDPy/jTFXjA0xigs7mowsLxxEWnOyvAS0YR4L1NydHdTl8mMATFxiu5FbksKVsELjs5qMKtD3ma0YI3tQAmAe8KeeqMovLOLOfq5hn+8plmkR4nNAHEeRsUBcTbWLkufHfw+VbTLzY/H8GosDHREY21cXSkwizeh8dC90vz6CuSSDV0VF9MsR5Xy/U03IF21LB9mcp8zoLhOnCGecTWdr6Az5jjfD2e3dXr5PvBQFos1990dvFVLbQkoQM/kW//rcgoRxYiTMon1u1ZGqjDCEMDlYc9HrSRgZ0N4WhchivaTfBkHYs0xvdyYsKM8Wqcrvr9Jb7AEU7B6k6X92J4zfk8se3zijXB0SMhLV3qCGS7zbzMl9GlelqSnJwEUCKpgetrogs+MHhKYEnNGcoXyCqakB0TBW5NMm4lc1xf300Z9j4FwWySNbk6ryeYOEDP42KImMKnzrcbKDJSbloesQ5bUlQ/41DqmSE8ZPGByr359NPDFQMpTJgOFOibpvAyUqE48W+9uqbMZ2SI6XIxN6bfekZtBJeCSCrpI1gNPlDtKhneSUa5EJe30dLc2QuUYSHqMmztZB70vDRt/f7j0SvcUFMDHC4a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"
+
+class TaprootTest(TestCase):
+    # run blind-unblind in a thread or multiple threads
+    def unblind_thread(self, pset, i, unblinded=None, blinded=None, reblinded=None):
+        # check we can unblind
+        pset.unblind(mbkey)
+        for inp in pset.inputs:
+            self.assertIsNotNone(inp.value)
+            self.assertIsNotNone(inp.asset)
+
+        if unblinded:
+            self.assertEqual(str(pset), unblinded)
+
+        # check we can blind
+        seed = tagged_hash("liquid/blinding_seed", mbkey.secret)
+        pset.blind(seed)
+        for out in pset.outputs:
+            if out.blinding_pubkey is None:
+                continue
+            self.assertIsNotNone(out.value_commitment)
+            self.assertIsNotNone(out.asset_commitment)
+            self.assertIsNotNone(out.range_proof)
+            self.assertIsNotNone(out.surjection_proof)
+
+        if blinded:
+            self.assertEqual(str(pset), blinded)
+
+        # check we can add extra message into the rangeproof
+        txseed = pset.txseed(seed)
+        nonce = tagged_hash("liquid/range_proof", txseed + (0).to_bytes(4, "little"))
+        out = pset.outputs[i]
+        msg = b"I can put any message here"
+        out.reblind(nonce, extra_message=msg)
+
+        if reblinded:
+            self.assertEqual(str(pset), reblinded)
+
+        # check we can unblind this output and extract the message
+        vout = out.blinded_vout
+        bkey = slip77.blinding_key(mbkey, vout.script_pubkey)
+        res = vout.unblind(bkey.secret, message_length=200)
+        value, asset, vbf, abf, extramsg, min_value, max_value = res
+        self.assertEqual(extramsg.rstrip(b"\x00"), msg)
+
+    def test_threading(self):
+        # reverse order for pset inputs and outputs
+        pset = PSET.from_string(B64PSET)
+        pset.inputs = pset.inputs[::-1]
+        pset.outputs = pset.outputs[::-1]
+        pset2 = PSET.from_string(str(pset))
+        pset1 = PSET.from_string(B64PSET)
+        self.assertTrue(str(pset1) != str(pset2))
+
+        tarr = [
+            threading.Thread(target=self.unblind_thread, args=(PSET.from_string(str(pset2)),-1))
+            for i in range(5)
+        ]
+        tarr.append(threading.Thread(target=self.unblind_thread, args=(pset1,0,UNBLINDED,BLINDED,REBLINDED)))
+        tarr += [
+            threading.Thread(target=self.unblind_thread, args=(PSET.from_string(str(pset2)),-1))
+            for i in range(5)
+        ]
+
+        for t in tarr:
+            t.start()
+        for t in tarr:
+            t.join()
+
+        self.assertEqual(str(pset1), REBLINDED)