Transaction
ID: a434a5b427...9e48
Inputs (5)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.004 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.00025568 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.00004032 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
10
Spent
Address:
Output transaction:
Settlement height:
Value:
69.1 ERG
Tokens:
Loading assets...
Outputs (5)
Unspent
Address:
Settlement height:
Value:
0.004 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
69.1 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.00006408 ERG
Tokens:
Loading assets...
Transaction Details
Confirmations: 169,160
Total coins transferred: 69.11 ERG
Fees: 0.001 ERG
Fees per byte: 0.000000155 ERG
Raw Transaction Data
{
"id": "a434a5b42758cd85e264e29940c1737c0f557d6b553e963706a31568b3eb9e48",
"blockId": "291fcc99e2a3f1b4b2669d75606e19306feb60e5a67942f7ba2763ae30708a89",
"inclusionHeight": 1600409,
"timestamp": 1756395520605,
"index": 3,
"globalIndex": 9409884,
"numConfirmations": 169160,
"inputs": [
{
"boxId": "5ae274d45f75241f1bfb569ba7f4d3bfd77f0076551d410722503e1e939b548a",
"value": 4000000,
"index": 0,
"spendingProof": null,
"outputBlockId": "13bbab258bcd439d052b79854dcd597c50171b56caf91b8bde3b31540a391902",
"outputTransactionId": "551d9e14005ba156d9e0d244422968ffa045dc837ed95b4c975eba12ec7a31bd",
"outputIndex": 2,
"outputGlobalIndex": 49686306,
"outputCreatedAt": 1600230,
"outputSettledAt": 1600235,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: Coll(31,-25,-114,14,104,-44,-80,88,108,76,92,29,-108,23,-56,-11,31,-72,-2,-77,-41,-6,60,35,-50,10,-104,64,-123,-44,124,-3)\n2: 0\n3: 4000000\n4: 0\n5: 0\n6: Coll(82,-51,-84,78,-82,-22,-34,92,82,5,107,26,94,108,-53,93,92,4,-8,25,-120,-79,90,-6,39,-71,61,-45,-75,109,76,-7)\n7: 1\n8: 1\n9: 0\n10: 1\n11: 1\n12: 2\n13: 3\n14: 2\n15: 1\n16: 0\n17: Coll(48,46,-109,-24,-93,121,-5,123,-41,80,86,121,71,-48,-93,-106,-14,-79,56,-75,23,-127,-25,67,69,126,-30,6,-27,-72,-20,-64)\n18: 2\n19: 1\n20: 1\n21: false\n22: 1\n23: 0\n24: 0\n25: 2\n26: 0\n27: 0\n28: 2\n29: 1000\n30: 1\n31: 0\n32: 1000000\n33: 0\n34: 0\n35: 0\n36: 1\n37: 1\n38: 1\n39: 1\n40: 2\n41: 0\n42: 0\n43: 0\n44: 0\n45: 0\n46: 0\n47: 0\n48: 0\n49: 0\n50: 1\n51: 1\n52: 1\n53: 2\n54: 2\n55: 2\n56: 1\n57: 2\n58: 1\n59: 2\n60: 2\n61: 0\n62: 2\n63: 1\n64: 1\n65: 1\n66: 1\n67: 1\n68: 1\n69: 0\n70: 1\n71: 0\n72: 1\n73: 1\n74: 0\n75: 1\n76: 1\n77: 1\n78: 0\n79: 0\n80: 1\n81: 1\n82: 1\n83: 0",
"ergoTreeScript": "{\n val coll1 = CONTEXT.dataInputs\n val box2 = coll1(placeholder[Int](0))\n val coll3 = box2.tokens\n val coll4 = placeholder[Coll[Byte]](1)\n val coll5 = SELF.propositionBytes\n val box6 = OUTPUTS(placeholder[Int](2))\n val coll7 = box6.tokens\n val coll8 = SELF.tokens\n val bool9 = SELF.value >= placeholder[Long](3)\n val box10 = INPUTS(placeholder[Int](4))\n val tuple11 = coll3(placeholder[Int](5))\n val bool12 = allOf(Coll[Boolean](box2.propositionBytes == coll5, tuple11._1 == placeholder[Coll[Byte]](6), tuple11._2 == placeholder[Long](7)))\n val tuple13 = (Coll[Byte](placeholder[Byte](8)), placeholder[Long](9))\n val tuple14 = coll8.getOrElse(placeholder[Int](10), tuple13)\n val tuple15 = coll7.getOrElse(placeholder[Int](11), tuple13)\n val l16 = tuple15._2\n if (if ((coll3.size == placeholder[Int](12)) && (coll1.size == placeholder[Int](13))) {(\n val coll17 = coll1(placeholder[Int](14)).tokens\n allOf(\n Coll[Boolean](\n coll17(placeholder[Int](15))._1 == box2.R4[Coll[Byte]].get, coll17(placeholder[Int](16))._1 == placeholder[Coll[Byte]](17), box2.R6[\n Long\n ].get == placeholder[Long](18), coll3(placeholder[Int](19))._2 == placeholder[Long](20)\n )\n )\n )} else { placeholder[Boolean](21) }) {(\n val coll17 = INPUTS.filter({(box17: Box) => box17.propositionBytes != coll5 })\n val coll18 = box2.R5[Coll[Byte]].get\n val coll19 = OUTPUTS(placeholder[Int](22)).tokens\n val l20 = coll17.flatMap({(box20: Box) => box20.tokens.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }) }).fold(\n placeholder[Long](23), {(tuple20: (Long, (Coll[Byte], Long))) => tuple20._1 + tuple20._2._2 }\n ) - coll19.filter({(tuple20: (Coll[Byte], Long)) => tuple20._1 == coll18 }).fold(\n placeholder[Long](24), {(tuple20: (Long, (Coll[Byte], Long))) => tuple20._1 + tuple20._2._2 }\n )\n val box21 = coll1(placeholder[Int](25))\n val coll22 = box21.R7[Coll[Byte]].get\n val l23 = coll19.filter({(tuple23: (Coll[Byte], Long)) => tuple23._1 == coll22 }).fold(\n placeholder[Long](26), {(tuple23: (Long, (Coll[Byte], Long))) => tuple23._1 + tuple23._2._2 }\n ) - coll17.flatMap({(box23: Box) => box23.tokens.filter({(tuple25: (Coll[Byte], Long)) => tuple25._1 == coll22 }) }).fold(\n placeholder[Long](27), {(tuple23: (Long, (Coll[Byte], Long))) => tuple23._1 + tuple23._2._2 }\n )\n sigmaProp(\n allOf(\n Coll[Boolean](\n box2.R4[Coll[Byte]].get == coll4, allOf(\n Coll[Boolean](\n l20 + l20 * box21.R4[Coll[Long]].get(placeholder[Int](28)) * placeholder[Long](29) - coll1(placeholder[Int](30)).R8[Coll[Long]].get(\n placeholder[Int](31)\n ) / placeholder[Long](32) == l23, INPUTS.filter({(box24: Box) => box24.propositionBytes == coll5 }).flatMap(\n {(box24: Box) => box24.tokens.filter({(tuple26: (Coll[Byte], Long)) => tuple26._1 == coll22 }) }\n ).fold(placeholder[Long](33), {(tuple24: (Long, (Coll[Byte], Long))) => tuple24._1 + tuple24._2._2 }) - coll7.filter(\n {(tuple24: (Coll[Byte], Long)) => tuple24._1 == coll22 }\n ).fold(placeholder[Long](34), {(tuple24: (Long, (Coll[Byte], Long))) => tuple24._1 + tuple24._2._2 }) == l23\n )\n ), box6.propositionBytes == coll5\n )\n )\n )\n )} else {\n if (allOf(\n Coll[Boolean](\n (SELF.id == box10.id) && (coll5 == OUTPUTS(placeholder[Int](35)).propositionBytes), tuple14._2 < l16, l16 != placeholder[Long](\n 36\n ), tuple14._1 == tuple15._1\n )\n )) {(\n val coll17 = INPUTS.slice(placeholder[Int](37), INPUTS.size + placeholder[Int](38))\n val coll18 = box2.R5[Coll[Byte]].get\n val box19 = OUTPUTS(placeholder[Int](39))\n val coll20 = box19.tokens\n val coll21 = coll1(placeholder[Int](40)).R7[Coll[Byte]].get\n sigmaProp(\n allOf(\n Coll[Boolean](\n box2.R6[Long].get == placeholder[Long](41), allOf(\n Coll[Boolean](\n coll17.flatMap({(box22: Box) => box22.tokens.filter({(tuple24: (Coll[Byte], Long)) => tuple24._1 == coll18 }) }).fold(\n placeholder[Long](42), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll20.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }).fold(\n placeholder[Long](43), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) == coll20.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll21 }).fold(\n placeholder[Long](44), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll17.flatMap({(box22: Box) => box22.tokens.filter({(tuple24: (Coll[Byte], Long)) => tuple24._1 == coll21 }) }).fold(\n placeholder[Long](45), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ), coll7.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }).fold(\n placeholder[Long](46), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll8.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }).fold(\n placeholder[Long](47), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) == coll8.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll21 }).fold(\n placeholder[Long](48), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll7.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll21 }).fold(\n placeholder[Long](49), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n )\n )\n ), coll17.forall({(box22: Box) => box22.propositionBytes == box19.propositionBytes }), bool9\n )\n )\n )\n )} else {(\n val opt17 = box2.R4[Coll[Byte]]\n val opt18 = box2.R5[Coll[Byte]]\n if (box2.R6[Long].get == placeholder[Long](50)) {(\n val box19 = OUTPUTS(placeholder[Int](51))\n val coll20 = opt18.get\n val coll21 = box19.tokens\n val tuple22 = coll21(placeholder[Int](52))\n val tuple23 = coll7(placeholder[Int](53))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box19.propositionBytes == coll5, allOf(\n Coll[Boolean](opt17.get == box19.R4[Coll[Byte]].get, coll20 == box19.R5[Coll[Byte]].get, box19.R6[Long].get == placeholder[Long](54))\n ), allOf(\n Coll[Boolean](\n allOf(Coll[Boolean](coll21.size == placeholder[Int](55), tuple22._1 == coll20, tuple22._2 == placeholder[Long](56))), allOf(\n Coll[Boolean](tuple23._1 == box10.R7[Coll[Byte]].get, tuple23._2 == coll8(placeholder[Int](57))._2)\n )\n )\n ), box10.tokens(placeholder[Int](58))._1 == coll4, bool12, bool9\n )\n )\n )\n )} else {(\n val tuple19 = coll7(placeholder[Int](59))\n val l20 = tuple19._2\n val box21 = coll1(placeholder[Int](60))\n val l22 = box21.R4[Coll[Long]].get(placeholder[Int](61))\n val bool23 = l20 == l22\n val coll24 = box21.R7[Coll[Byte]].get\n val bool25 = tuple19._1 == coll24\n val l26 = l20 - coll8.getOrElse(placeholder[Int](62), tuple13)._2\n val coll27 = opt18.get\n val coll28 = opt17.get\n sigmaProp(allOf(Coll[Boolean](allOf(Coll[Boolean](if (bool23) {(\n val tuple29 = coll7(placeholder[Int](63))\n allOf(Coll[Boolean](tuple29._2 == placeholder[Long](64), tuple29._1 == coll8(placeholder[Int](65))._1, bool25))\n )} else {(\n val tuple29 = coll8(placeholder[Int](66))\n val tuple30 = coll7(placeholder[Int](67))\n allOf(Coll[Boolean](tuple29._2 - tuple30._2 == l26, tuple30._1 == tuple29._1, bool25))\n )}, OUTPUTS(placeholder[Int](68)).tokens.filter({(tuple29: (Coll[Byte], Long)) => tuple29._1 == coll27 }).fold(placeholder[Long](69), {(tuple29: (Long, (Coll[Byte], Long))) => tuple29._1 + tuple29._2._2 }) - INPUTS(placeholder[Int](70)).tokens.filter({(tuple29: (Coll[Byte], Long)) => tuple29._1 == coll27 }).fold(placeholder[Long](71), {(tuple29: (Long, (Coll[Byte], Long))) => tuple29._1 + tuple29._2._2 }) == l26)), allOf(Coll[Boolean](bool25, box21.tokens(placeholder[Int](72))._1 == coll28)), allOf(Coll[Boolean](coll28 == box6.R4[Coll[Byte]].get, coll27 == box6.R5[Coll[Byte]].get)), if (bool23) { allOf(Coll[Boolean](box6.R6[Long].get == placeholder[Long](73), coll7(placeholder[Int](74))._2 == placeholder[Long](75), coll7(placeholder[Int](76))._2 == placeholder[Long](77), bool23)) } else { allOf(Coll[Boolean](box6.R6[Long].get == placeholder[Long](78), coll7(placeholder[Int](79))._2 == placeholder[Long](80), coll7(placeholder[Int](81))._2 != placeholder[Long](82))) }, box6.propositionBytes == coll5, bool12, bool9, coll7.filter({(tuple29: (Coll[Byte], Long)) => tuple29._1 == coll24 }).fold(placeholder[Long](83), {(tuple29: (Long, (Coll[Byte], Long))) => tuple29._1 + tuple29._2._2 }) <= l22)))\n )}\n )}\n }\n}",
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"ergoTree": "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",
"address": "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",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "1105148cfdc501dc01c0d1028084af5f",
"sigmaType": "Coll[SLong]",
"renderedValue": "[10,1621830,110,21600,100000000]"
},
"R5": {
"serializedValue": "1a020b74657374207369677273760b7465737420736967727376",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[7465737420736967727376,7465737420736967727376]"
},
"R6": {
"serializedValue": "0e240008cd0267aefd87068bc22ce5217e8b8539b3460a5bfe9e688bae23a15afe480b0a2921",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd0267aefd87068bc22ce5217e8b8539b3460a5bfe9e688bae23a15afe480b0a2921"
},
"R7": {
"serializedValue": "0e20003bd19d0187117f130b62e1bcab0939929ff5c7709f843c5c4dd158949285d0",
"sigmaType": "Coll[SByte]",
"renderedValue": "003bd19d0187117f130b62e1bcab0939929ff5c7709f843c5c4dd158949285d0"
}
}
}
],
"outputs": [
{
"boxId": "77878c555d2b91dfde94f9f51a665a3d4fed6f2f3a95db498a315d478f356f8f",
"transactionId": "a434a5b42758cd85e264e29940c1737c0f557d6b553e963706a31568b3eb9e48",
"blockId": "291fcc99e2a3f1b4b2669d75606e19306feb60e5a67942f7ba2763ae30708a89",
"value": 4000000,
"index": 0,
"globalIndex": 49689341,
"creationHeight": 1600408,
"settlementHeight": 1600409,
"ergoTree": "105404000e201fe78e0e68d4b0586c4c5c1d9417c8f51fb8feb3d7fa3c23ce0a984085d47cfd04000580a4e803040004000e2052cdac4eaeeade5c52056b1a5e6ccb5d5c04f81988b15afa27b93dd3b56d4cf905020201050004020402040404060404040204000e20302e93e8a379fb7bd750567947d0a396f2b138b51781e743457ee206e5b8ecc00504040205020100040205000500040405000500040405d00f040204000580897a050005000400050204020402040204040500050005000500050005000500050005000502040204020404050404040502040404020404040404000404040205020402040204020402050004020500040205020400050204020502050004000502040205020500d810d601db6501fed602b27201730000d603db63087202d6047301d605c2a7d606b2a5730200d607db63087206d608db6308a7d60992c1a77303d60ab2a4730400d60bb27203730500d60c9683030193c272027205938c720b017306938c720b027307d60d860283010273087309d60eb27208730a01720dd60fb27207730b01720dd6108c720f029595ed93b17203730c93b17201730dd801d611db6308b27201730e0096830401938cb27211730f0001e4c67202040e938cb2721173100001731193e4c6720206057312938cb272037313000273147315d807d611b5a4d901116394c272117205d612e4c67202050ed613db6308b2a5731600d61499b0dc0c0f721101d9011463b5db63087214d901164d0e938c72160172127317d90114414d0e9a8c7214018c8c72140202b0b57213d901144d0e938c72140172127318d90114414d0e9a8c7214018c8c72140202d615b27201731900d616e4c67215070ed61799b0b57213d901174d0e938c7217017216731ad90117414d0e9a8c7217018c8c72170202b0dc0c0f721101d9011763b5db63087217d901194d0e938c7219017216731bd90117414d0e9a8c7217018c8c72170202d19683030193e4c67202040e720496830201939a72149d9c72149cb2e4c672150411731c0099731db2e4c6b27201731e000811731f00732072179399b0dc0c0fb5a4d901186393c27218720501d9011863b5db63087218d9011a4d0e938c721a0172167321d90118414d0e9a8c7218018c8c72180202b0b57207d901184d0e938c72180172167322d90118414d0e9a8c7218018c8c72180202721793c2720672059596830401ed93c5a7c5720a937205c2b2a57323008f8c720e0272109472107324938c720e018c720f01d805d611b4a473259ab1a47326d612e4c67202050ed613b2a5732700d614db63087213d615e4c6b27201732800070ed19683040193e4c6720206057329968302019399b0dc0c0f721101d9011663b5db63087216d901184d0e938c7218017212732ad90116414d0e9a8c7216018c8c72160202b0b57214d901164d0e938c7216017212732bd90116414d0e9a8c7216018c8c7216020299b0b57214d901164d0e938c7216017215732cd90116414d0e9a8c7216018c8c72160202b0dc0c0f721101d9011663b5db63087216d901184d0e938c7218017215732dd90116414d0e9a8c7216018c8c721602029399b0b57207d901164d0e938c7216017212732ed90116414d0e9a8c7216018c8c72160202b0b57208d901164d0e938c7216017212732fd90116414d0e9a8c7216018c8c7216020299b0b57208d901164d0e938c72160172157330d90116414d0e9a8c7216018c8c72160202b0b57207d901164d0e938c72160172157331d90116414d0e9a8c7216018c8c72160202af7211d901166393c27216c272137209d802d611c67202040ed612c67202050e9593e4c6720206057332d805d613b2a5733300d614e47212d615db63087213d616b27215733400d617b27207733500d19683060193c2721372059683030193e47211e4c67213040e937214e4c67213050e93e4c6721306057336968302019683030193b172157337938c7216017214938c721602733896830201938c721701e4c6720a070e938c7217028cb2720873390002938cb2db6308720a733a00017204720c7209d80ad613b27207733b00d6148c721302d615b27201733c00d616b2e4c672150411733d00d6179372147216d618e4c67215070ed619938c7213017218d61a9972148cb27208733e01720d02d61be47212d61ce47211d19683080196830201957217d801d61db27207733f0096830301938c721d027340938c721d018cb27208734100017219d802d61db27208734200d61eb272077343009683030193998c721d028c721e02721a938c721e018c721d0172199399b0b5db6308b2a5734400d9011d4d0e938c721d01721b7345d9011d414d0e9a8c721d018c8c721d0202b0b5db6308b2a4734600d9011d4d0e938c721d01721b7347d9011d414d0e9a8c721d018c8c721d0202721a968302017219938cb2db6308721573480001721c9683020193721ce4c67206040e93721be4c67206050e9572179683040193e4c6720606057349938cb27207734a0002734b938cb27207734c0002734d72179683030193e4c672060605734e938cb27207734f00027350948cb2720773510002735293c272067205720c720990b0b57207d9011d4d0e938c721d0172187353d9011d414d0e9a8c721d018c8c721d02027216",
"ergoTreeConstants": "0: 0\n1: Coll(31,-25,-114,14,104,-44,-80,88,108,76,92,29,-108,23,-56,-11,31,-72,-2,-77,-41,-6,60,35,-50,10,-104,64,-123,-44,124,-3)\n2: 0\n3: 4000000\n4: 0\n5: 0\n6: Coll(82,-51,-84,78,-82,-22,-34,92,82,5,107,26,94,108,-53,93,92,4,-8,25,-120,-79,90,-6,39,-71,61,-45,-75,109,76,-7)\n7: 1\n8: 1\n9: 0\n10: 1\n11: 1\n12: 2\n13: 3\n14: 2\n15: 1\n16: 0\n17: Coll(48,46,-109,-24,-93,121,-5,123,-41,80,86,121,71,-48,-93,-106,-14,-79,56,-75,23,-127,-25,67,69,126,-30,6,-27,-72,-20,-64)\n18: 2\n19: 1\n20: 1\n21: false\n22: 1\n23: 0\n24: 0\n25: 2\n26: 0\n27: 0\n28: 2\n29: 1000\n30: 1\n31: 0\n32: 1000000\n33: 0\n34: 0\n35: 0\n36: 1\n37: 1\n38: 1\n39: 1\n40: 2\n41: 0\n42: 0\n43: 0\n44: 0\n45: 0\n46: 0\n47: 0\n48: 0\n49: 0\n50: 1\n51: 1\n52: 1\n53: 2\n54: 2\n55: 2\n56: 1\n57: 2\n58: 1\n59: 2\n60: 2\n61: 0\n62: 2\n63: 1\n64: 1\n65: 1\n66: 1\n67: 1\n68: 1\n69: 0\n70: 1\n71: 0\n72: 1\n73: 1\n74: 0\n75: 1\n76: 1\n77: 1\n78: 0\n79: 0\n80: 1\n81: 1\n82: 1\n83: 0",
"ergoTreeScript": "{\n val coll1 = CONTEXT.dataInputs\n val box2 = coll1(placeholder[Int](0))\n val coll3 = box2.tokens\n val coll4 = placeholder[Coll[Byte]](1)\n val coll5 = SELF.propositionBytes\n val box6 = OUTPUTS(placeholder[Int](2))\n val coll7 = box6.tokens\n val coll8 = SELF.tokens\n val bool9 = SELF.value >= placeholder[Long](3)\n val box10 = INPUTS(placeholder[Int](4))\n val tuple11 = coll3(placeholder[Int](5))\n val bool12 = allOf(Coll[Boolean](box2.propositionBytes == coll5, tuple11._1 == placeholder[Coll[Byte]](6), tuple11._2 == placeholder[Long](7)))\n val tuple13 = (Coll[Byte](placeholder[Byte](8)), placeholder[Long](9))\n val tuple14 = coll8.getOrElse(placeholder[Int](10), tuple13)\n val tuple15 = coll7.getOrElse(placeholder[Int](11), tuple13)\n val l16 = tuple15._2\n if (if ((coll3.size == placeholder[Int](12)) && (coll1.size == placeholder[Int](13))) {(\n val coll17 = coll1(placeholder[Int](14)).tokens\n allOf(\n Coll[Boolean](\n coll17(placeholder[Int](15))._1 == box2.R4[Coll[Byte]].get, coll17(placeholder[Int](16))._1 == placeholder[Coll[Byte]](17), box2.R6[\n Long\n ].get == placeholder[Long](18), coll3(placeholder[Int](19))._2 == placeholder[Long](20)\n )\n )\n )} else { placeholder[Boolean](21) }) {(\n val coll17 = INPUTS.filter({(box17: Box) => box17.propositionBytes != coll5 })\n val coll18 = box2.R5[Coll[Byte]].get\n val coll19 = OUTPUTS(placeholder[Int](22)).tokens\n val l20 = coll17.flatMap({(box20: Box) => box20.tokens.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }) }).fold(\n placeholder[Long](23), {(tuple20: (Long, (Coll[Byte], Long))) => tuple20._1 + tuple20._2._2 }\n ) - coll19.filter({(tuple20: (Coll[Byte], Long)) => tuple20._1 == coll18 }).fold(\n placeholder[Long](24), {(tuple20: (Long, (Coll[Byte], Long))) => tuple20._1 + tuple20._2._2 }\n )\n val box21 = coll1(placeholder[Int](25))\n val coll22 = box21.R7[Coll[Byte]].get\n val l23 = coll19.filter({(tuple23: (Coll[Byte], Long)) => tuple23._1 == coll22 }).fold(\n placeholder[Long](26), {(tuple23: (Long, (Coll[Byte], Long))) => tuple23._1 + tuple23._2._2 }\n ) - coll17.flatMap({(box23: Box) => box23.tokens.filter({(tuple25: (Coll[Byte], Long)) => tuple25._1 == coll22 }) }).fold(\n placeholder[Long](27), {(tuple23: (Long, (Coll[Byte], Long))) => tuple23._1 + tuple23._2._2 }\n )\n sigmaProp(\n allOf(\n Coll[Boolean](\n box2.R4[Coll[Byte]].get == coll4, allOf(\n Coll[Boolean](\n l20 + l20 * box21.R4[Coll[Long]].get(placeholder[Int](28)) * placeholder[Long](29) - coll1(placeholder[Int](30)).R8[Coll[Long]].get(\n placeholder[Int](31)\n ) / placeholder[Long](32) == l23, INPUTS.filter({(box24: Box) => box24.propositionBytes == coll5 }).flatMap(\n {(box24: Box) => box24.tokens.filter({(tuple26: (Coll[Byte], Long)) => tuple26._1 == coll22 }) }\n ).fold(placeholder[Long](33), {(tuple24: (Long, (Coll[Byte], Long))) => tuple24._1 + tuple24._2._2 }) - coll7.filter(\n {(tuple24: (Coll[Byte], Long)) => tuple24._1 == coll22 }\n ).fold(placeholder[Long](34), {(tuple24: (Long, (Coll[Byte], Long))) => tuple24._1 + tuple24._2._2 }) == l23\n )\n ), box6.propositionBytes == coll5\n )\n )\n )\n )} else {\n if (allOf(\n Coll[Boolean](\n (SELF.id == box10.id) && (coll5 == OUTPUTS(placeholder[Int](35)).propositionBytes), tuple14._2 < l16, l16 != placeholder[Long](\n 36\n ), tuple14._1 == tuple15._1\n )\n )) {(\n val coll17 = INPUTS.slice(placeholder[Int](37), INPUTS.size + placeholder[Int](38))\n val coll18 = box2.R5[Coll[Byte]].get\n val box19 = OUTPUTS(placeholder[Int](39))\n val coll20 = box19.tokens\n val coll21 = coll1(placeholder[Int](40)).R7[Coll[Byte]].get\n sigmaProp(\n allOf(\n Coll[Boolean](\n box2.R6[Long].get == placeholder[Long](41), allOf(\n Coll[Boolean](\n coll17.flatMap({(box22: Box) => box22.tokens.filter({(tuple24: (Coll[Byte], Long)) => tuple24._1 == coll18 }) }).fold(\n placeholder[Long](42), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll20.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }).fold(\n placeholder[Long](43), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) == coll20.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll21 }).fold(\n placeholder[Long](44), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll17.flatMap({(box22: Box) => box22.tokens.filter({(tuple24: (Coll[Byte], Long)) => tuple24._1 == coll21 }) }).fold(\n placeholder[Long](45), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ), coll7.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }).fold(\n placeholder[Long](46), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll8.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll18 }).fold(\n placeholder[Long](47), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) == coll8.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll21 }).fold(\n placeholder[Long](48), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n ) - coll7.filter({(tuple22: (Coll[Byte], Long)) => tuple22._1 == coll21 }).fold(\n placeholder[Long](49), {(tuple22: (Long, (Coll[Byte], Long))) => tuple22._1 + tuple22._2._2 }\n )\n )\n ), coll17.forall({(box22: Box) => box22.propositionBytes == box19.propositionBytes }), bool9\n )\n )\n )\n )} else {(\n val opt17 = box2.R4[Coll[Byte]]\n val opt18 = box2.R5[Coll[Byte]]\n if (box2.R6[Long].get == placeholder[Long](50)) {(\n val box19 = OUTPUTS(placeholder[Int](51))\n val coll20 = opt18.get\n val coll21 = box19.tokens\n val tuple22 = coll21(placeholder[Int](52))\n val tuple23 = coll7(placeholder[Int](53))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box19.propositionBytes == coll5, allOf(\n Coll[Boolean](opt17.get == box19.R4[Coll[Byte]].get, coll20 == box19.R5[Coll[Byte]].get, box19.R6[Long].get == placeholder[Long](54))\n ), allOf(\n Coll[Boolean](\n allOf(Coll[Boolean](coll21.size == placeholder[Int](55), tuple22._1 == coll20, tuple22._2 == placeholder[Long](56))), allOf(\n Coll[Boolean](tuple23._1 == box10.R7[Coll[Byte]].get, tuple23._2 == coll8(placeholder[Int](57))._2)\n )\n )\n ), box10.tokens(placeholder[Int](58))._1 == coll4, bool12, bool9\n )\n )\n )\n )} else {(\n val tuple19 = coll7(placeholder[Int](59))\n val l20 = tuple19._2\n val box21 = coll1(placeholder[Int](60))\n val l22 = box21.R4[Coll[Long]].get(placeholder[Int](61))\n val bool23 = l20 == l22\n val coll24 = box21.R7[Coll[Byte]].get\n val bool25 = tuple19._1 == coll24\n val l26 = l20 - coll8.getOrElse(placeholder[Int](62), tuple13)._2\n val coll27 = opt18.get\n val coll28 = opt17.get\n sigmaProp(allOf(Coll[Boolean](allOf(Coll[Boolean](if (bool23) {(\n val tuple29 = coll7(placeholder[Int](63))\n allOf(Coll[Boolean](tuple29._2 == placeholder[Long](64), tuple29._1 == coll8(placeholder[Int](65))._1, bool25))\n )} else {(\n val tuple29 = coll8(placeholder[Int](66))\n val tuple30 = coll7(placeholder[Int](67))\n allOf(Coll[Boolean](tuple29._2 - tuple30._2 == l26, tuple30._1 == tuple29._1, bool25))\n )}, OUTPUTS(placeholder[Int](68)).tokens.filter({(tuple29: (Coll[Byte], Long)) => tuple29._1 == coll27 }).fold(placeholder[Long](69), {(tuple29: (Long, (Coll[Byte], Long))) => tuple29._1 + tuple29._2._2 }) - INPUTS(placeholder[Int](70)).tokens.filter({(tuple29: (Coll[Byte], Long)) => tuple29._1 == coll27 }).fold(placeholder[Long](71), {(tuple29: (Long, (Coll[Byte], Long))) => tuple29._1 + tuple29._2._2 }) == l26)), allOf(Coll[Boolean](bool25, box21.tokens(placeholder[Int](72))._1 == coll28)), allOf(Coll[Boolean](coll28 == box6.R4[Coll[Byte]].get, coll27 == box6.R5[Coll[Byte]].get)), if (bool23) { allOf(Coll[Boolean](box6.R6[Long].get == placeholder[Long](73), coll7(placeholder[Int](74))._2 == placeholder[Long](75), coll7(placeholder[Int](76))._2 == placeholder[Long](77), bool23)) } else { allOf(Coll[Boolean](box6.R6[Long].get == placeholder[Long](78), coll7(placeholder[Int](79))._2 == placeholder[Long](80), coll7(placeholder[Int](81))._2 != placeholder[Long](82))) }, box6.propositionBytes == coll5, bool12, bool9, coll7.filter({(tuple29: (Coll[Byte], Long)) => tuple29._1 == coll24 }).fold(placeholder[Long](83), {(tuple29: (Long, (Coll[Byte], Long))) => tuple29._1 + tuple29._2._2 }) <= l22)))\n )}\n )}\n }\n}",
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