*lsp-log: mspyls:4885*.txt

[Trace - 12:41:21 PM] Sending request 'initialize - (3489)'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:22 PM] Received notification 'python/beginProgress'.
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[Trace - 12:41:22 PM] Received notification 'python/reportProgress'.
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[Trace - 12:41:22 PM] Received notification 'python/endProgress'.
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[Trace - 12:41:35 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:35 PM] Received notification 'window/logMessage'.
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[Trace - 12:41:35 PM] Received response 'initialize - (3489)' in 210ms.
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[Trace - 12:41:35 PM] Sending notification 'initialized'.
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[Trace - 12:41:35 PM] Sending notification 'workspace/didChangeConfiguration'.
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[Trace - 12:41:35 PM] Sending notification 'textDocument/didOpen'.
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    "text": "import torch\nfrom Batch import nopeak_mask\nimport torch.nn.functional as F\nimport math\n\n\ndef init_vars(src, model, SRC, TRG, opt):\n\n    init_tok = TRG.vocab.stoi['<sos>']\n    src_mask = (src != SRC.vocab.stoi['<pad>']).unsqueeze(-2)\n    e_output = model.encoder(src, src_mask)\n\n    outputs = torch.LongTensor([[init_tok]])\n    if opt.device.type == 'cuda':\n        outputs = outputs.cuda()\n\n    trg_mask = nopeak_mask(1, opt)\n\n    out = model.out(model.decoder(outputs,\n    e_output, src_mask, trg_mask))\n    out = F.softmax(out, dim=-1)\n\n    probs, ix = out[:, -1].data.topk(opt.k)\n    log_scores = torch.Tensor([math.log(prob) for prob in probs.data[0]]).unsqueeze(0)\n\n    outputs = torch.zeros(opt.k, opt.max_len).long()\n    if opt.device.type == 'cuda':\n        outputs = outputs.cuda()\n    outputs[:, 0] = init_tok\n    outputs[:, 1] = ix[0]\n\n    e_outputs = torch.zeros(opt.k, e_output.size(-2),e_output.size(-1))\n    if opt.device.type == 'cuda':\n        e_outputs = e_outputs.cuda()\n    e_outputs[:, :] = e_output[0]\n\n    return outputs, e_outputs, log_scores\n\ndef k_best_outputs(outputs, out, log_scores, i, k):\n\n    probs, ix = out[:, -1].data.topk(k)\n    log_probs = torch.Tensor([math.log(p) for p in probs.data.view(-1)]).view(k, -1) + log_scores.transpose(0,1)\n    k_probs, k_ix = log_probs.view(-1).topk(k)\n\n    row = k_ix // k\n    col = k_ix % k\n\n    outputs[:, :i] = outputs[row, :i]\n    outputs[:, i] = ix[row, col]\n\n    log_scores = k_probs.unsqueeze(0)\n\n    return outputs, log_scores\n\ndef beam_search(src, model, SRC, TRG, opt):\n\n\n    outputs, e_outputs, log_scores = init_vars(src, model, SRC, TRG, opt)\n    eos_tok = TRG.vocab.stoi['<eos>']\n    src_mask = (src != SRC.vocab.stoi['<pad>']).unsqueeze(-2)\n    ind = None\n    for i in range(2, opt.max_len):\n\n        trg_mask = nopeak_mask(i, opt)\n\n        out = model.out(model.decoder(outputs[:,:i],\n        e_outputs, src_mask, trg_mask))\n\n        out = F.softmax(out, dim=-1)\n\n        outputs, log_scores = k_best_outputs(outputs, out, log_scores, i, opt.k)\n\n        if (outputs==eos_tok).nonzero().size(0) == opt.k:\n            alpha = 0.7\n            div = 1/((outputs==eos_tok).nonzero()[:,1].type_as(log_scores)**alpha)\n            _, ind = torch.max(log_scores * div, 1)\n            ind = ind.data[0]\n            break\n\n    if ind is None:\n        length = (outputs[0]==eos_tok).nonzero()[0]\n        return ' '.join([TRG.vocab.itos[tok] for tok in outputs[0][1:length]])\n\n    else:\n        length = (outputs[ind]==eos_tok).nonzero()[0]\n        return ' '.join([TRG.vocab.itos[tok] for tok in outputs[ind][1:length]])\n"
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[Trace - 12:41:35 PM] Received notification 'python/languageServerStarted'.
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[Trace - 12:41:36 PM] Received notification 'python/beginProgress'.
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[Trace - 12:41:36 PM] Received notification 'python/reportProgress'.
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[Trace - 12:41:36 PM] Received notification 'python/reportProgress'.
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[Trace - 12:41:37 PM] Received notification 'textDocument/publishDiagnostics'.
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[Trace - 12:41:37 PM] Sending request 'textDocument/hover - (3490)'.
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[Trace - 12:41:37 PM] Sending request 'textDocument/hover - (3491)'.
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[Trace - 12:41:37 PM] Sending request 'textDocument/hover - (3492)'.
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[Trace - 12:41:37 PM] Sending request 'textDocument/hover - (3493)'.
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[Trace - 12:41:39 PM] Received notification 'python/reportProgress'.
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[Trace - 12:41:39 PM] Received notification 'telemetry/event'.
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[Trace - 12:41:39 PM] Received notification 'telemetry/event'.
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[Trace - 12:41:39 PM] Received notification 'telemetry/event'.
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[Trace - 12:41:39 PM] Received response 'textDocument/hover - (3493)' in 132ms.
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[Trace - 12:41:39 PM] Received response 'textDocument/hover - (3491)' in 138ms.
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[Trace - 12:41:39 PM] Received notification 'telemetry/event'.
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[Trace - 12:41:39 PM] Received response 'textDocument/hover - (3490)' in 142ms.
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[Trace - 12:41:39 PM] Received response 'textDocument/hover - (3492)' in 137ms.
Result: null


[Trace - 12:41:40 PM] Sending request 'shutdown - (3494)'.
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[Trace - 12:41:40 PM] Sending notification 'exit'.
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