{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":28479,"offset":28469,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u041d\u0438\u0436\u0435 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d \u0432\u0430\u0448 \u043a\u043e\u0434 \u0441 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u044b\u043c\u0438 \u043e\u0442\u0441\u0442\u0443\u043f\u0430\u043c\u0438 \u0438 \u043a\u0430\u0432\u044b\u0447\u043a\u0430\u043c\u0438:\n\n```python\nprint(gen_token_history)\nprint(prime_token_history)\n\nif not tokens_left in gen_token_history.keys():\n    gen_token_history[tokens_left] = 1\nelse:\n    gen_token_history[tokens_left] += 1\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nxs = [x.to(device) for x in xs]\nlabels = labels.to(device)\n\nif hasattr(top_prior, 'to'):\n    top_prior = top_prior.to(device)\n\nxs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n\nwhile tokens_left > 0:\n    print()\n    print(tokens_left)\n    t.cuda.empty_cache()\n    sampling_kwargs = dict(temp=sampling_temperature if sampling_level == 2 else upsampling_temperature, fp16=True, max_batch_size=[16, 16, lv2_batch][sampling_level], chunk_size=chunk_size)\n    xs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n    tokens_left -= max_tokens\n```\n\n\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u043d\u0430 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u043d\u044b\u0435 \u043a\u0430\u0432\u044b\u0447\u043a\u0438 (\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u0435 \u0434\u0432\u043e\u0439\u043d\u044b\u0435 \u043a\u0430\u0432\u044b\u0447\u043a\u0438 `\"` \u0438 \u043e\u0434\u0438\u043d\u0430\u0440\u043d\u044b\u0435 `'`), \u0430 \u0442\u0430\u043a\u0436\u0435 \u0440\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0435 \u043e\u0442\u0441\u0442\u0443\u043f\u044b (4 \u043f\u0440\u043e\u0431\u0435\u043b\u0430) \u0432\u043e \u0432\u0441\u0435\u0445 \u0441\u0442\u0440\u043e\u043a\u0430\u0445. \u0412\u0441\u0435 \u0431\u043b\u043e\u043a\u0438 \u043a\u043e\u0434\u0430 \u0434\u043e\u043b\u0436\u043d\u044b \u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u044c \u0435\u0434\u0438\u043d\u043e\u043c\u0443 \u043f\u0440\u0430\u0432\u0438\u043b\u0443 \u043e\u0442\u0441\u0442\u0443\u043f\u043e\u0432 \u0434\u043b\u044f \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0439 \u0440\u0430\u0431\u043e\u0442\u044b \u0432 Python.","behavior":"degenerate_repetition","conversation_hash":"022c008e63349296be6db776733b0311","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":2352,"n_turns_in_conversation":31,"n_words":231,"source_row_offset":28469,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:30:57 GMT","turn_id":"a3173166733ecf2b9d16ced5","turn_index":25,"user_text":"\u043f\u0435\u0440\u0435\u043f\u0438\u0448\u0438 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u043c\u043e\u0439 \u043a\u043e\u0442 \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u043e\u0448\u0438\u0431\u043a\u0443 \u0432\u043c\u0435\u0441\u0442\u0435 \u0441 \u044d\u0442\u0438\u043c\u0438 \u0430 \u044f \u043f\u0440\u043e\u0441\u0442\u043e \u0435\u0433\u043e \u043f\u0435\u0440\u0435\u043f\u0438\u0448\u0443 \u0447\u0442\u043e \u0431\u044b \u044f \u0432\u0438\u0434\u0435\u043b \u043a\u0430\u043a\u0438\u0435 \u043e\u0442\u0441\u0442\u0443\u043f\u044b \u0434\u0435\u043b\u0430\u0442\u044c  print(gen_token_history)\n    print(prime_token_history)\n \n    if not tokens_left in gen_token_history.keys():\n      gen_token_history[tokens_left] = 1\n    else:\n     gen_token_history[tokens_left] += 1\n\n    device = torch.device(\u201ccuda\u201d if torch.cuda.is_available() else \u201ccpu\u201d)\n\nxs = [x.to(device) for x in xs]\nlabels = labels.to(device)\n\nif hasattr(top_prior, \u2018to\u2019):\n    top_prior = top_prior.to(device)\n\nxs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n\n\n    while tokens_left > 0:\n      print()\n      print(tokens_left)\n      t.cuda.empty_cache()\n      sampling_kwargs = dict(temp=sampling_temperature if sampling_level == 2 else upsampling_temperature, fp16=True, max_batch_size=[16, 16, lv2_batch][sampling_level], chunk_size=chunk_size)\n      xs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens) , hps)\n      tokens_left -= max_tokens"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `NameError: name 'torch' is not defined` \u043e\u0437\u043d\u0430\u0447\u0430\u0435\u0442, \u0447\u0442\u043e \u043f\u0435\u0440\u0435\u0434 \u043f\u043e\u043f\u044b\u0442\u043a\u043e\u0439 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0438\u043c\u0435\u043d\u0438 `torch` \u043e\u043d\u043e \u043d\u0435 \u0431\u044b\u043b\u043e \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043e. \u0427\u0442\u043e\u0431\u044b \u0443\u0441\u0442\u0440\u0430\u043d\u0438\u0442\u044c \u044d\u0442\u0443 \u043e\u0448\u0438\u0431\u043a\u0443, \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 Torch (PyTorch) \u0431\u044b\u043b\u0430 \u0438\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0430 \u0432 \u043d\u0430\u0447\u0430\u043b\u0435 \u0432\u0430\u0448\u0435\u0433\u043e \u0441\u043a\u0440\u0438\u043f\u0442\u0430. \u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0438\u043c\u043f\u043e\u0440\u0442 \u0432 \u043d\u0430\u0447\u0430\u043b\u043e \u0432\u0430\u0448\u0435\u0433\u043e \u0444\u0430\u0439\u043b\u0430 Python:\n\n```python\nimport torch\n```\n\n\u0412\u043e\u0442 \u043a\u0430\u043a \u0432\u0430\u0448 \u043a\u043e\u0434 \u0434\u043e\u043b\u0436\u0435\u043d \u0432\u044b\u0433\u043b\u044f\u0434\u0435\u0442\u044c \u0441 \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u043d\u044b\u043c \u0438\u043c\u043f\u043e\u0440\u0442\u043e\u043c:\n\n```python\nimport torch\n\nprint(gen_token_history)\nprint(prime_token_history)\n\nif not tokens_left in gen_token_history.keys():\n    gen_token_history[tokens_left] = 1\nelse:\n    gen_token_history[tokens_left] += 1\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nxs = [x.to(device) for x in xs]\nlabels = labels.to(device)\n\nif hasattr(top_prior, 'to'):\n    top_prior = top_prior.to(device)\n\nxs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n\nwhile tokens_left > 0:\n    print()\n    print(tokens_left)\n    t.cuda.empty_cache()\n    sampling_kwargs = dict(temp=sampling_temperature if sampling_level == 2 else upsampling_temperature, fp16=True, max_batch_size=[16, 16, lv2_batch][sampling_level], chunk_size=chunk_size)\n    xs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n    tokens_left -= max_tokens\n```\n\n\u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u0441\u0442\u0440\u043e\u0447\u043a\u0443 `import torch` \u0432 \u0441\u0430\u043c\u043e\u0435 \u043d\u0430\u0447\u0430\u043b\u043e \u0432\u0430\u0448\u0435\u0433\u043e \u0441\u043a\u0440\u0438\u043f\u0442\u0430. \u042d\u0442\u043e \u0438\u0441\u043f\u0440\u0430\u0432\u0438\u0442 `NameError`, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0439 \u0441 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u0435\u043c \u0438\u043c\u043f\u043e\u0440\u0442\u0430 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 PyTorch.","behavior":"degenerate_repetition","conversation_hash":"022c008e63349296be6db776733b0311","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":1861,"n_turns_in_conversation":31,"n_words":199,"source_row_offset":28470,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:30:57 GMT","turn_id":"b71dd01091d47a3838f9c022","turn_index":26,"user_text":"NameError                                 Traceback (most recent call last)\n<ipython-input-23-820b4ca7b516> in <cell line: 39>()\n    196       gen_token_history[tokens_left] += 1\n    197 \n--> 198     device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    199 \n    200     xs = [x.to(device) for x in xs]\n\nNameError: name 'torch' is not defined"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `AttributeError: 'dict' object has no attribute 'to'` \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u0432\u044b \u043f\u044b\u0442\u0430\u0435\u0442\u0435\u0441\u044c \u0432\u044b\u0437\u0432\u0430\u0442\u044c \u043c\u0435\u0442\u043e\u0434 `.to(device)` \u043d\u0430 \u0441\u043b\u043e\u0432\u0430\u0440\u0435 (`dict`), \u0432 \u0442\u043e \u0432\u0440\u0435\u043c\u044f \u043a\u0430\u043a \u044d\u0442\u043e\u0442 \u043c\u0435\u0442\u043e\u0434 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u0442\u0435\u043d\u0437\u043e\u0440\u0430\u043c\u0438 PyTorch.\n\n\u0415\u0441\u043b\u0438 `labels` \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0441\u043b\u043e\u0432\u0430\u0440\u0435\u043c, \u0430 \u043d\u0435 \u0442\u0435\u043d\u0437\u043e\u0440\u043e\u043c, \u0442\u043e \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0435\u0433\u043e \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c. \u0415\u0441\u043b\u0438 \u0441\u043b\u043e\u0432\u0430\u0440\u044c \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0442\u0435\u043d\u0437\u043e\u0440\u044b, \u0442\u043e \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c `.to(device)` \u043a\u043e \u0432\u0441\u0435\u043c \u0442\u0435\u043d\u0437\u043e\u0440\u0430\u043c \u0432 \u044d\u0442\u043e\u043c \u0441\u043b\u043e\u0432\u0430\u0440\u0435.\n\n\u0412\u043e\u0442 \u043f\u0440\u0438\u043c\u0435\u0440 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u044d\u0442\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c:\n\n```python\n# \u0415\u0441\u043b\u0438 labels \u044d\u0442\u043e \u0441\u043b\u043e\u0432\u0430\u0440\u044c, \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0449\u0438\u0439 \u0442\u0435\u043d\u0437\u043e\u0440\u044b, \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u0435 .to(device) \u043a\u043e \u0432\u0441\u0435\u043c \u0442\u0435\u043d\u0437\u043e\u0440\u0430\u043c \u0432 \u0441\u043b\u043e\u0432\u0430\u0440\u0435\nlabels = {key: value.to(device) if isinstance(value, torch.Tensor) else value for key, value in labels.items()}\n```\n\n\u042d\u0442\u043e\u0442 \u043a\u043e\u0434 \u043f\u0440\u043e\u0445\u043e\u0434\u0438\u0442 \u043f\u043e \u0432\u0441\u0435\u043c \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u0430\u043c \u0441\u043b\u043e\u0432\u0430\u0440\u044f \u0438 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u0442 `.to(device)` \u0442\u043e\u043b\u044c\u043a\u043e \u043a \u0442\u0435\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0442\u0435\u043d\u0437\u043e\u0440\u0430\u043c\u0438 PyTorch. \u0412\u0441\u0442\u0430\u0432\u044c\u0442\u0435 \u044d\u0442\u0443 \u0441\u0442\u0440\u043e\u043a\u0443 \u0432\u043c\u0435\u0441\u0442\u043e `labels = labels.to(device)` \u0432 \u0432\u0430\u0448 \u043a\u043e\u0434:\n\n```python\nimport torch\n\nprint(gen_token_history)\nprint(prime_token_history)\n\nif not tokens_left in gen_token_history.keys():\n    gen_token_history[tokens_left] = 1\nelse:\n    gen_token_history[tokens_left] += 1\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nxs = [x.to(device) for x in xs]\n\n# \u041f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u043c .to(device) \u043a \u0442\u0435\u043d\u0437\u043e\u0440\u0430\u043c \u0432\u043d\u0443\u0442\u0440\u0438 labels, \u0435\u0441\u043b\u0438 labels \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0441\u043b\u043e\u0432\u0430\u0440\u0435\u043c\nlabels = {key: value.to(device) if isinstance(value, torch.Tensor) else value for key, value in labels.items()}\n\nif hasattr(top_prior, 'to'):\n    top_prior = top_prior.to(device)\n\nxs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n\nwhile tokens_left > 0:\n    print()\n    print(tokens_left)\n    t.cuda.empty_cache()\n    sampling_kwargs = dict(temp=sampling_temperature if sampling_level == 2 else upsampling_temperature, fp16=True, max_batch_size=[16, 16, lv2_batch][sampling_level], chunk_size=chunk_size)\n    xs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n    tokens_left -= max_tokens\n```\n\n\u041f\u043e\u0434\u043e\u0431\u043d\u043e\u0435 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0442 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0443 `labels`, \u0435\u0441\u043b\u0438 \u043e\u043d\u0430 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0441\u043b\u043e\u0432\u0430\u0440\u0435\u043c, \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0449\u0438\u043c \u0442\u0435\u043d\u0437\u043e\u0440\u044b. \u0415\u0441\u043b\u0438 \u0432 `labels` \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442\u0441\u044f \u0434\u0440\u0443\u0433\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u044b, \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u043e\u043d\u0438 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442\u0441\u044f \u0432 \u043f\u043e\u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0432\u044b\u0437\u043e\u0432\u0430\u0445.","behavior":"degenerate_repetition","conversation_hash":"022c008e63349296be6db776733b0311","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":2626,"n_turns_in_conversation":31,"n_words":298,"source_row_offset":28471,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:30:57 GMT","turn_id":"d7f730c07bc80ffb8e813de8","turn_index":27,"user_text":"{}\n{}\n---------------------------------------------------------------------------\nAttributeError                            Traceback (most recent call last)\n<ipython-input-24-838d2bc01c61> in <cell line: 39>()\n    200 \n    201     xs = [x.to(device) for x in xs]\n--> 202     labels = labels.to(device)\n    203 \n    204     if hasattr(top_prior, 'to'):\n\nAttributeError: 'dict' object has no attribute 'to'"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `KeyError: 'max_batch_size'` \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u0435\u0442, \u043a\u043e\u0433\u0434\u0430 \u043a\u043e\u0434 \u043f\u044b\u0442\u0430\u0435\u0442\u0441\u044f \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0434\u043e\u0441\u0442\u0443\u043f \u043a \u043a\u043b\u044e\u0447\u0443 `'max_batch_size'` \u0432 \u0441\u043b\u043e\u0432\u0430\u0440\u0435 `sampling_kwargs`, \u043d\u043e \u044d\u0442\u043e\u0442 \u043a\u043b\u044e\u0447 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u0435\u0442. \u0412\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u043a\u043b\u044e\u0447 `'max_batch_size'` \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0432 \u0441\u043b\u043e\u0432\u0430\u0440\u0435 `sampling_kwargs` \u0434\u043e \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u043e\u043d \u0431\u0443\u0434\u0435\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d.\n\n\u041f\u0440\u043e\u0432\u0435\u0440\u0438\u043c, \u043a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0435\u0442\u0441\u044f `sampling_kwargs` \u0438 \u0443\u0431\u0435\u0434\u0438\u043c\u0441\u044f, \u0447\u0442\u043e `'max_batch_size'` \u0442\u0430\u043c \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u0435\u0442. \u0412\u043e\u0442 \u043f\u0440\u0438\u043c\u0435\u0440 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f:\n\n1. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435, \u043a\u0430\u043a \u0444\u043e\u0440\u043c\u0438\u0440\u0443\u0435\u0442\u0441\u044f `sampling_kwargs` \u0432 \u0432\u0430\u0448\u0435\u043c \u043a\u043e\u0434\u0435:\n\n```python\nsampling_kwargs = dict(\n    temp=sampling_temperature if sampling_level == 2 else upsampling_temperature,\n    fp16=True,\n    max_batch_size=[16, 16, lv2_batch][sampling_level],  # \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435, \u0447\u0442\u043e \u044d\u0442\u043e\u0442 \u043a\u043b\u044e\u0447 \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f\n    chunk_size=chunk_size\n)\n```\n\n\u0423\u0434\u043e\u0441\u0442\u043e\u0432\u0435\u0440\u044c\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u0441\u0442\u0440\u043e\u043a\u0430, \u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0435\u043d\u043d\u0430\u044f \u0437\u0430 `max_batch_size`, \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u0442 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u043b\u043e\u0432\u0430\u0440\u044c. \u0415\u0441\u043b\u0438 \u043e\u0448\u0438\u0431\u043a\u0430 \u043f\u043e\u044f\u0432\u0438\u043b\u0430\u0441\u044c \u043f\u043e\u0441\u043b\u0435 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0439 \u0432\u044b\u0448\u0435, \u0432\u044b\u044f\u0441\u043d\u0438\u0442\u0435, \u0438\u0437\u043c\u0435\u043d\u044f\u043b\u0441\u044f \u043b\u0438 \u044d\u0442\u043e\u0442 \u0441\u043b\u043e\u0432\u0430\u0440\u044c \u0432 \u043a\u043e\u0434\u0435 \u043f\u0435\u0440\u0435\u0434 \u0432\u044b\u0437\u043e\u0432\u043e\u043c `sample_partial_window`.\n\n2. \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0442\u0435 `sampling_kwargs` \u043f\u0435\u0440\u0435\u0434 \u0432\u044b\u0437\u043e\u0432\u043e\u043c \u0444\u0443\u043d\u043a\u0446\u0438\u0438 `sample_partial_window` \u0438 \u0443\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e `max_batch_size` \u0431\u044b\u043b \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d:\n\n```python\nsampling_kwargs = {\n    'temp': sampling_temperature if sampling_level == 2 else upsampling_temperature,\n    'fp16': True,\n    'max_batch_size': [16, 16, lv2_batch][sampling_level],  # \u0421\u043b\u0435\u0434\u0438\u0442\u0435 \u0437\u0430 \u0438\u043d\u0434\u0435\u043a\u0441\u0430\u043c\u0438 \u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c lv2_batch\n    'chunk_size': chunk_size\n}\n\n# \u0412\u044b\u0437\u043e\u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0438\nxs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n```\n\n3. \u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u043f\u043e\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c: \u0423\u0447\u0438\u0442\u044b\u0432\u0430\u0439\u0442\u0435, \u0433\u0434\u0435 \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442\u044c \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 `sampling_kwargs`, \u0438 \u0443\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u043f\u0435\u0440\u0435\u0434 \u0432\u044b\u0437\u043e\u0432\u043e\u043c `sample_partial_window` \u043a\u043b\u044e\u0447 `'max_batch_size'` \u0432\u0441\u0435 \u0435\u0449\u0435 \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0432 \u0441\u043b\u043e\u0432\u0430\u0440\u0435.\n\n\u042d\u0442\u0438 \u0448\u0430\u0433\u0438 \u0434\u043e\u043b\u0436\u043d\u044b \u043f\u043e\u043c\u043e\u0447\u044c \u0443\u0441\u0442\u0440\u0430\u043d\u0438\u0442\u044c `KeyError`, \u0433\u0430\u0440\u0430\u043d\u0442\u0438\u0440\u0443\u044f, \u0447\u0442\u043e \u043a\u043b\u044e\u0447 `'max_batch_size'` \u0432\u0441\u0435\u0433\u0434\u0430 \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0432\u043e\u0432\u0430\u0442\u044c \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u0438.","behavior":"degenerate_repetition","conversation_hash":"022c008e63349296be6db776733b0311","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":3687,"n_turns_in_conversation":31,"n_words":392,"source_row_offset":28472,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:30:57 GMT","turn_id":"24891a8421a45cad16c71795","turn_index":28,"user_text":"Sampling 1181 tokens for [0,1181]. Conditioning on 590 tokens\n/usr/local/lib/python3.10/dist-packages/jukebox/sample.py:46: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n  zs = t.load(f\"{logdir}/data.pth.tar\")['zs']\n---------------------------------------------------------------------------\nKeyError                                  Traceback (most recent call last)\n<ipython-input-25-87f4b4dcef54> in <cell line: 39>()\n    205         top_prior = top_prior.to(device)\n    206 \n--> 207     xs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n    208 \n    209 \n\n1 frames\n/usr/local/lib/python3.10/dist-packages/jukebox/sample.py in sample_single_window(zs, labels, sampling_kwargs, level, prior, start, hps, combined_progress, autosave, prob_func)\n     81     empty_cache()\n     82 \n---> 83     max_batch_size = sampling_kwargs['max_batch_size']\n     84     del sampling_kwargs['max_batch_size']\n     85 \n\nKeyError: 'max_batch_size'"},{"assistant_initiates":false,"asst_text":"\u0414\u0430, \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0432\u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043a\u043e\u0434 \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 `sampling_kwargs` \u043d\u0435\u043f\u043e\u0441\u0440\u0435\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u043f\u0435\u0440\u0435\u0434 \u0432\u044b\u0437\u043e\u0432\u043e\u043c `sample_partial_window` \u0438\u043b\u0438 \u0432 \u0442\u043e\u043c \u043c\u0435\u0441\u0442\u0435, \u0433\u0434\u0435 \u0432\u044b \u0432\u043f\u0435\u0440\u0432\u044b\u0435 \u0435\u0433\u043e \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u0442\u0435. \u042d\u0442\u043e \u0433\u0430\u0440\u0430\u043d\u0442\u0438\u0440\u0443\u0435\u0442, \u0447\u0442\u043e \u043f\u0440\u0438 \u0432\u044b\u0437\u043e\u0432\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u043a\u043b\u044e\u0447 `'max_batch_size'` \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c \u0432 \u0441\u043b\u043e\u0432\u0430\u0440\u0435.\n\n\u0412\u043e\u0442 \u043a\u0430\u043a \u044d\u0442\u043e \u043c\u043e\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c:\n\n```python\n# \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f sampling_kwargs \u043f\u0435\u0440\u0435\u0434 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c\nsampling_kwargs = {\n    'temp': sampling_temperature if sampling_level == 2 else upsampling_temperature,\n    'fp16': True,\n    'max_batch_size': [16, 16, lv2_batch][sampling_level],\n    'chunk_size': chunk_size\n}\n\n# \u0422\u0435\u043f\u0435\u0440\u044c \u0432\u044b\u0437\u043e\u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 sample_partial_window \u0441 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u043c sampling_kwargs\nxs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n```\n\n\u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u044d\u0442\u043e\u0442 \u0431\u043b\u043e\u043a \u043a\u043e\u0434\u0430 \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0434\u043e \u0432\u044b\u0437\u043e\u0432\u0430 `sample_partial_window`, \u0447\u0442\u043e\u0431\u044b `sampling_kwargs` \u0431\u044b\u043b \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d \u0441 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u043c \u043a\u043b\u044e\u0447\u043e\u043c `'max_batch_size'`. \u042d\u0442\u043e \u0438\u0441\u043a\u043b\u044e\u0447\u0438\u0442 \u043e\u0448\u0438\u0431\u043a\u0443 `KeyError`, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u0443\u044e \u0441 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c \u043a\u043b\u044e\u0447\u043e\u043c.","behavior":"degenerate_repetition","conversation_hash":"022c008e63349296be6db776733b0311","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":1058,"n_turns_in_conversation":31,"n_words":115,"source_row_offset":28473,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:30:57 GMT","turn_id":"389b71ed94798f3832ddf07a","turn_index":29,"user_text":"\u043c\u043d\u0435 \u043d\u0443\u0436\u043d\u043e \u043f\u0435\u0440\u0435\u0434 \u043e\u0448\u0438\u0431\u043a\u043e\u0439 \u044d\u0442\u043e \u0432\u0441\u0442\u0430\u0432\u0438\u0442\u044c?"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `AssertionError: Range is [1049580.0,1166886000000), got tensor([[1048576.]], device='cuda:0')` \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `pos_start`, \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e, \u0432\u044b\u0445\u043e\u0434\u0438\u0442 \u0437\u0430 \u043f\u0440\u0435\u0434\u0435\u043b\u044b \u043e\u0436\u0438\u0434\u0430\u0435\u043c\u043e\u0433\u043e \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430 `[self.pos_min, self.pos_max)`. \u042d\u0442\u043e \u0437\u043d\u0430\u0447\u0438\u0442, \u0447\u0442\u043e \u0432\u0430\u0448\u0435 \u0442\u0435\u043a\u0443\u0449\u0435\u0435 \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 `pos_start` \u043c\u0435\u043d\u044c\u0448\u0435 `self.pos_min` \u0438\u043b\u0438 \u0431\u043e\u043b\u044c\u0448\u0435 \u0438\u043b\u0438 \u0440\u0430\u0432\u043d\u043e `self.pos_max`.\n\n\u0412\u043e\u0442 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0448\u0430\u0433\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0433\u0443\u0442 \u043f\u043e\u043c\u043e\u0447\u044c \u0432 \u0434\u0438\u0430\u0433\u043d\u043e\u0441\u0442\u0438\u043a\u0435 \u0438 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u044d\u0442\u043e\u0439 \u043e\u0448\u0438\u0431\u043a\u0438:\n\n1. **\u041f\u0440\u043e\u0432\u0435\u0440\u044c\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f `self.pos_min` \u0438 `self.pos_max`**: \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u043e\u043d\u0438 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u044b \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e. \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u0442\u0435, \u0433\u0434\u0435 \u0438\u043c\u0435\u043d\u043d\u043e \u043e\u043d\u0438 \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u044e\u0442\u0441\u044f \u0438 \u043a\u0430\u043a \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u044e\u0442\u0441\u044f \u0432 \u0432\u0430\u0448\u0435\u043c \u043a\u043e\u0434\u0435.\n\n2. **\u0414\u0438\u0430\u0433\u043d\u043e\u0441\u0442\u0438\u043a\u0430 `pos_start`**: \u041f\u0435\u0440\u0435\u0434 \u0432\u044b\u0437\u043e\u0432\u043e\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b\u0437\u044b\u0432\u0430\u0435\u0442 \u043e\u0448\u0438\u0431\u043a\u0443, \u0434\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0434\u0438\u0430\u0433\u043d\u043e\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434, \u0447\u0442\u043e\u0431\u044b \u0432\u044b\u044f\u0441\u043d\u0438\u0442\u044c, \u043f\u043e\u0447\u0435\u043c\u0443 `pos_start` \u0432\u044b\u0445\u043e\u0434\u0438\u0442 \u0437\u0430 \u043f\u0440\u0435\u0434\u0435\u043b\u044b:\n\n```python\nprint(f\"pos_start: {pos_start}\")\nprint(f\"self.pos_min: {self.pos_min}, self.pos_max: {self.pos_max}\")\n```\n\n3. **\u041b\u043e\u0433\u0438\u043a\u0430 \u0432\u044b\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u043d\u0438\u044f \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430**: \u0423\u0431\u0435\u0434\u0438\u0442\u0435\u0441\u044c, \u0447\u0442\u043e \u043b\u043e\u0433\u0438\u043a\u0430, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 `pos_start`, \u043d\u0430\u0445\u043e\u0434\u0438\u0442\u0441\u044f \u0432 \u043f\u0440\u0435\u0434\u0435\u043b\u0430\u0445 \u044d\u0442\u0438\u0445 \u0433\u0440\u0430\u043d\u0438\u0446. \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u043d\u0443\u0436\u043d\u043e \u0438\u0437\u043c\u0435\u043d\u0438\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0438\u043b\u0438 \u043b\u043e\u0433\u0438\u043a\u0443, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u0442 \u0438\u043b\u0438 \u043c\u043e\u0434\u0438\u0444\u0438\u0446\u0438\u0440\u0443\u0435\u0442 `pos_start`.\n\n4. **\u041a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u043a\u0430 `pos_start`**: \u0415\u0441\u043b\u0438 \u0432\u044b \u0443\u0432\u0435\u0440\u0435\u043d\u044b, \u0447\u0442\u043e \u0442\u0435\u043a\u0443\u0449\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f `self.pos_min` \u0438 `self.pos_max` \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u044b, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u0432\u0430\u043c \u043f\u043e\u0442\u0440\u0435\u0431\u0443\u0435\u0442\u0441\u044f \u043e\u0442\u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `pos_start`, \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u043e \u043d\u0430\u0445\u043e\u0434\u0438\u043b\u043e\u0441\u044c \u0432 \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u043e\u043c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0435. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440:\n\n```python\npos_start = torch.clamp(pos_start, min=self.pos_min, max=self.pos_max-1)\n```\n\n5. **\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435\u0441\u044c \u043a \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0438**: \u0415\u0441\u043b\u0438 \u044d\u0442\u043e \u0447\u0430\u0441\u0442\u044c \u0431\u043e\u043b\u0435\u0435 \u043a\u0440\u0443\u043f\u043d\u043e\u0439 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438, \u0442\u0430\u043a\u043e\u0439 \u043a\u0430\u043a Jukebox, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u0430\u0445\u043e\u0442\u0435\u0442\u044c \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u0443\u044e \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e \u0438\u043b\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u044b, \u0447\u0442\u043e\u0431\u044b \u0443\u0431\u0435\u0434\u0438\u0442\u044c\u0441\u044f \u0432 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u043a\u043e\u043d\u0444\u0438\u0433\u0443\u0440\u0430\u0446\u0438\u0438.\n\n\u0415\u0441\u043b\u0438 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u043e\u0441\u0442\u0430\u043b\u0430\u0441\u044c, \u043f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u043f\u0440\u043e\u0441\u043b\u0435\u0434\u0438\u0442\u044c, \u043e\u0442\u043a\u0443\u0434\u0430 \u0431\u0435\u0440\u0443\u0442\u0441\u044f \u044d\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0438 \u0432 \u043a\u0430\u043a\u043e\u043c \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0435 \u043e\u043d\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442\u0441\u044f \u0432 \u0440\u0430\u043c\u043a\u0430\u0445 \u0432\u0441\u0435\u0439 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u0438\u043b\u0438 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438. \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u044d\u0442\u043e \u043f\u043e\u043c\u043e\u0436\u0435\u0442 \u0432\u044b\u044f\u0432\u0438\u0442\u044c \u043d\u0435\u0434\u043e\u043f\u043e\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u0438\u043b\u0438 \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430.","behavior":"degenerate_repetition","conversation_hash":"022c008e63349296be6db776733b0311","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":2726,"n_turns_in_conversation":31,"n_words":293,"source_row_offset":28474,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:30:57 GMT","turn_id":"eae3298148c284323810f2d4","turn_index":30,"user_text":"<ipython-input-26-180158330386> in <cell line: 39>()\n    212 }\n    213 \n--> 214     xs = sample_partial_window(xs, labels, sampling_kwargs, sampling_level, top_prior, min(tokens_left, max_tokens), hps)\n    215 \n    216 \n\n9 frames\n/usr/local/lib/python3.10/dist-packages/jukebox/prior/conditioners.py in forward(self, pos_start, pos_end)\n     89         # Check if [pos_start,pos_end] in [pos_min, pos_max)\n     90         assert len(pos_start.shape) == 2, f\"Expected shape with 2 dims, got {pos_start.shape}\"\n---> 91         assert (self.pos_min <= pos_start).all() and (pos_start < self.pos_max).all(), f\"Range is [{self.pos_min},{self.pos_max}), got {pos_start}\"\n     92         pos_start = pos_start.float()\n     93         if pos_end is not None:\n\nAssertionError: Range is [1049580.0,1166886000000), got tensor([[1048576.]], device='cuda:0')"},{"assistant_initiates":false,"asst_text":"It seems you're encountering issues with loading assets using PixiJS in your Vue application. The warnings and errors you're seeing indicate that PixiJS is unable to locate or properly load your image and JSON files. Let's break down the problem and work towards a solution.\n\n### Error Analysis\n\n1. **Asset Not Found in Cache Warning:**\n\n   ```\n   PreView.vue:88 PixiJS Warning: [Assets] Asset id /src/assets/liuxin/liuxin.png was not found in the Cache\n   ```\n\n   This warning suggests that PixiJS cannot find the image asset in its cache when trying to create a texture from it.\n\n2. **TypeError on Reading 'source':**\n\n   ```\n   PreView.vue:140 Failed to fetch model list: TypeError: Cannot read properties of undefined (reading 'source')\n       at new _Spritesheet2 (chunk-NQFDCHBS.js?v=53b38b7a:1280:34)\n       at PreView.vue:89:25\n   ```\n\n   This error occurs when attempting to create a new `Spritesheet`. PixiJS tries to access the `source` property of an undefined object, indicating that the texture or data required for the spritesheet isn't properly loaded.\n\n### Possible Causes\n\n- **Incorrect Asset Paths:**\n\n  The paths to your image and JSON files might not be resolving correctly, leading to failed imports.\n\n- **TypeScript Module Declarations:**\n\n  TypeScript may not know how to handle imports of `.png` and `.json` files without proper module declarations.\n\n- **Asynchronous Asset Loading:**\n\n  PixiJS might be trying to use assets before they are fully loaded, especially if the assets are large or if there's a loading delay.\n\n### Solutions\n\n#### 1. Verify Asset Paths and Imports\n\nEnsure that the paths to your assets are correct. In your code, you're importing assets like this:\n\n```typescript\nimport liuxinPath from '@/assets/liuxin/liuxin.png';\nimport liuxinPathJS from '@/assets/liuxin/liuxin.json';\n```\n\n- Confirm that the files exist in the specified directory.\n- Check if the paths are correct relative to the `@` alias, which usually points to the `src` directory in Vue projects.\n\n#### 2. Add TypeScript Declarations for Non-Code Assets\n\nTypeScript needs to understand how to handle imports of image and JSON files. You can add custom type declarations to help TypeScript and your bundler (like Webpack or Vite) process these files.\n\n- **Create a `types` directory in your `src` folder**, and add a file named `assets.d.ts`:\n\n  ```typescript\n  // src/types/assets.d.ts\n\n  declare module '*.png' {\n    const value: string;\n    export default value;\n  }\n\n  declare module '*.json' {\n    const value: any;\n    export default value;\n  }\n  ```\n\n  This tells TypeScript how to handle imports of `.png` and `.json` files.\n\n- **Update the `tsconfig.json` to include the `types` directory**:\n\n  ```json\n  {\n    \"compilerOptions\": {\n      // ... existing options ...\n      \"typeRoots\": [\"./node_modules/@types\", \"./src/types\"]\n    }\n  }\n  ```\n\n#### 3. Ensure Assets Are Properly Loaded Before Use\n\nWhen working with PixiJS, it's crucial that all assets are fully loaded before you attempt to use them.\n\n- **Use PixiJS's Asset Loader**:\n\n  Instead of direct imports, use PixiJS's Asset Loader to load your assets. This method ensures that assets are loaded asynchronously and are available in the PixiJS cache when needed.\n\n  ```typescript\n  import { Assets } from 'pixi.js';\n\n  onMounted(async () => {\n    // Load assets\n    const [liuxinTexture, liuxinData] = await Promise.all([\n      Assets.load('@/assets/liuxin/liuxin.png'),\n      fetch('@/assets/liuxin/liuxin.json').then((response) => response.json()),\n    ]);\n\n    // Create spritesheet\n    const liuxinSheet = new Spritesheet(liuxinTexture, liuxinData);\n    await liuxinSheet.parse();\n\n    // Continue with your code to create sprites, etc.\n  });\n  ```\n\n  **Note**: Adjust the paths in `Assets.load` and `fetch` to match your project's structure.\n\n#### 4. Verify the Content of Imported JSON\n\nMake sure that the JSON file (`liuxin.json`) contains the correct data for your spritesheet.\n\n- **Log the JSON Data**:\n\n  ```typescript\n  console.log('liuxinPathJS:', liuxinPathJS);\n  ```\n\n  This will help you verify that the data is correctly imported and contains the expected structure.\n\n#### 5. Adjust the PixiJS Application Initialization\n\nTypically, when initializing a PixiJS `Application`, you pass the options directly without an `init` method.\n\n- **Update Application Creation**:\n\n  ```typescript\n  const app = new Application({\n    background: '#1099bb',\n  });\n  ```\n\n  Remove the `await app.init(...)` line, unless you're using a custom application class that requires initialization.\n\n#### 6. Check for Asynchronous Data Fetching Issues\n\nIn your `onMounted` function, you're fetching a model list and then processing it:\n\n```typescript\nconst httpdata = await Pixihttp.get_model_list();\n// ... process data ...\n```\n\nIf `httpdata` is `undefined` or doesn't have the expected structure, accessing its properties will result in errors.\n\n- **Ensure `Pixihttp.get_model_list()` Returns Data**:\n\n  Verify that the `get_model_list` method is functioning correctly and returns the expected data structure.\n\n- **Add Error Handling**:\n\n  ```typescript\n  try {\n    const httpdata = await Pixihttp.get_model_list();\n    if (httpdata && httpdata.data) {\n      // Proceed with processing\n    } else {\n      console.error('Invalid data received:', httpdata);\n    }\n  } catch (error) {\n    console.error('Error fetching model list:', error);\n  }\n  ```\n\n### Summary of Steps\n\n1. **Confirm asset paths and ensure that the files exist in the specified locations.**\n2. **Add TypeScript declarations for `.png` and `.json` files.**\n3. **Use PixiJS's Asset Loader to load assets asynchronously and ensure they're ready before use.**\n4. **Verify that your JSON data is correctly imported and has the expected structure.**\n5. **Simplify and correct the PixiJS application initialization if necessary.**\n6. **Add proper error handling and verify data when fetching the model list.**\n\n### Example Code Adjustment\n\nHere's how you might adjust your `onMounted` function incorporating the above suggestions:\n\n```typescript\nimport { onMounted, ref, reactive, computed, watch } from 'vue';\nimport { Application, Sprite, Spritesheet, AnimatedSprite, Assets, Container } from 'pixi.js';\n\nonMounted(async () => {\n  try {\n    const canvas = canvasdomref.value;\n\n    const app = new Application({\n      backgroundColor: 0x1099bb,\n      width: window.innerWidth,\n      height: window.innerHeight,\n    });\n\n    canvas?.appendChild(app.view);\n\n    // Load assets\n    const [liuxinTexture, liuxinData] = await Promise.all([\n      Assets.load(liuxinPath),\n      fetch(liuxinPathJS).then((response) => response.json()),\n    ]);\n\n    // Create spritesheet\n    const liuxinSheet = new Spritesheet(liuxinTexture, liuxinData);\n    await liuxinSheet.parse();\n\n    // Create animated sprite\n    const liuxinSp = new AnimatedSprite(liuxinSheet.animations['9c0d85519fc29bd805102e958f2e83e5_wps\u56fe\u7247']);\n    liuxinSp.animationSpeed = 0.03;\n    liuxinSp.width = 300;\n    liuxinSp.height = 300;\n    liuxinSp.loop = true;\n    liuxinSp.anchor.set(0.5);\n    liuxinSp.play();\n\n    const container = new Container();\n    container.x = app.screen.width / 2;\n    container.y = app.screen.height / 3;\n    container.addChild(liuxinSp);\n    app.stage.addChild(container);\n\n    // Fetch model list\n    const httpdata = await Pixihttp.get_model_list();\n    if (httpdata && httpdata.data) {\n      for (let entry of httpdata.data) {\n        if (entry.model_category === 'stable_diffusion_xl') {\n          stable_diffusion_xls.push(entry);\n        } else if (entry.model_category === 'flux') {\n          fluxs.push(entry);\n        }\n      }\n\n      // Sort models\n      stable_diffusion_xls.sort((a, b) => b.api_calls - a.api_calls);\n      fluxs.sort((a, b) => b.api_calls - a.api_calls);\n\n      console.log('Stable Diffusion XL Models:', stable_diffusion_xls);\n      console.log('Flux Models:', fluxs);\n    } else {\n      console.error('Invalid data received:', httpdata);\n    }\n\n    // Initialize gallery images\n    for (let i = 1; i <= 100; i++) {\n      images.push('https://example.com/path/to/image.jpg');\n    }\n  } catch (error) {\n    console.error('An error occurred:', error);\n  }\n});\n```\n\n### Additional Tips\n\n- **Use Console Logging for Debugging**:\n\n  Insert `console.log` statements to verify the values of variables at different stages.\n\n- **Check Network Tab in Developer Tools**:\n\n  Ensure that your assets are actually being requested and received by checking the Network tab in your browser's developer tools.\n\n- **Consider Using Absolute Paths**:\n\n  Sometimes, using absolute paths can help avoid issues with asset loading.\n\n- **Check Webpack or Vite Configuration**:\n\n  Ensure that your project's bundler is configured to handle image and JSON imports correctly.\n\n### References\n\n- [PixiJS Asset Loader](https://pixijs.download/release/docs/PIXI.Loader.html)\n- [TypeScript Module Declarations](https://www.typescriptlang.org/docs/handbook/modules.html#ambient-modules)\n- [Vue.js Asset Handling](https://cli.vuejs.org/guide/html-and-static-assets.html)\n- [Importing JSON in TypeScript](https://www.typescriptlang.org/docs/handbook/modules.html#json-modules)\n\n---\n\nBy following these steps, you should be able to resolve the asset loading issues and get your PixiJS application running smoothly within your Vue project.","behavior":"degenerate_repetition","conversation_hash":"916edef9b6951699547c38266dc34e7c","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-preview-2024-09-12","n_chars":20537,"n_turns_in_conversation":1,"n_words":2138,"source_row_offset":28475,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:31:12 GMT","turn_id":"b9419e2d663fb050f5e90ea5","turn_index":0,"user_text":"<script setup lang=\"ts\">\nimport { onMounted, ref, reactive, computed, watch, Ref } from 'vue'\nimport { Pixihttp } from '../systems/http.ts'\nimport {\n  Spritesheet,\n  AnimatedSprite,\n  Texture,\n  Application,\n  Sprite,\n} from 'pixi.js'\n// import * as PIXI from 'pixi.js'\nimport liuxinPath from '@/assets/liuxin/liuxin.png'\nimport liuxinPathJS from '@/assets/liuxin/liuxin.json'\n\n// \u5df2\u6709\u7684\u72b6\u6001\nconst checked_type = ref('1')\nconst model_id = ref('')\nconst canvasdomref = ref(null)\n// \u4f7f\u7528 reactive \u521b\u5efa\u54cd\u5e94\u5f0f\u6570\u7ec4\nconst stable_diffusion_xls = reactive<Array<any>>([])\nconst fluxs = reactive<Array<any>>([])\n\n// \u5206\u9875\u72b6\u6001\nconst currentPage = ref(1)\nconst itemsPerPage = 50\n\n// \u76d1\u542c checked_type \u53d8\u5316\uff0c\u91cd\u7f6e currentPage\nwatch(checked_type, () => {\n  currentPage.value = 1\n})\n\n// \u8ba1\u7b97\u603b\u9875\u6570\nconst totalPages = computed(() => {\n  const totalItems =\n    checked_type.value === '1' ? stable_diffusion_xls.length : fluxs.length\n  return Math.ceil(totalItems / itemsPerPage)\n})\n\n// \u8ba1\u7b97\u5f53\u524d\u9875\u663e\u793a\u7684\u6a21\u578b\nconst paginatedModels = computed(() => {\n  const models = checked_type.value === '1' ? stable_diffusion_xls : fluxs\n  const start = (currentPage.value - 1) * itemsPerPage\n  const end = start + itemsPerPage\n  return models.slice(start, end)\n})\n\n// \u65b0\u589e\uff1a\u753b\u5eca\u76f8\u5173\u72b6\u6001\nconst images = reactive<Array<string>>([])\nconst galleryCurrentPage = ref(1)\nconst galleryItemsPerPage = 12\nconst code_input_ = ref('')\nconst ondraw = () => {\n  console.log(code_input_.value)\n}\n// \u8ba1\u7b97\u753b\u5eca\u603b\u9875\u6570\nconst galleryTotalPages = computed(() =>\n  Math.ceil(images.length / galleryItemsPerPage)\n)\n\n// \u8ba1\u7b97\u5f53\u524d\u9875\u663e\u793a\u7684\u56fe\u7247\nconst paginatedGalleryImages = computed(() => {\n  const start = (galleryCurrentPage.value - 1) * galleryItemsPerPage\n  const end = start + galleryItemsPerPage\n  return images.slice(start, end)\n})\n\n// \u76d1\u542c galleryCurrentPage \u53d8\u5316\uff0c\u786e\u4fdd\u5176\u4e0d\u8d85\u8fc7\u603b\u9875\u6570\nwatch(galleryTotalPages, (newTotal) => {\n  if (galleryCurrentPage.value > newTotal) {\n    galleryCurrentPage.value = newTotal\n  }\n})\n\nonMounted(async () => {\n  try {\n    const canvas = canvasdomref.value\n    let width = window.screen.width\n    let height = window.screen.height\n    if (width > height) {\n      console.log('\u6a2a\u5c4f')\n    } else {\n      console.log('\u7ad6\u5c4f')\n    }\n    console.log('\u5c4f\u5e55\u957f\u5bbd:', width, height)\n    let app = new Application()\n    await app.init({ background: '#1099bb' })\n    let canvasInfo = app.canvas\n    const sprite = new Sprite(Texture.from(liuxinPath))\n    const liuxinsheet = new Spritesheet(Texture.from(liuxinPath), liuxinPathJS)\n    await liuxinsheet.parse()\n    const liuxinsp = new AnimatedSprite(\n      liuxinsheet.animations['9c0d85519fc29bd805102e958f2e83e5_wps\u56fe\u7247']\n    )\n    liuxinsp.animationSpeed = 0.03\n    liuxinsp.width = 300\n    liuxinsp.height = 300\n    liuxinsp.loop = true\n    liuxinsp.gotoAndPlay(0)\n    const container = new PIXI.Container()\n    container.x = app.screen.width / 2\n    container.y = app.screen.height / 3\n    liuxinsp!.anchor.x = 0.5\n    liuxinsp!.anchor.y = 0.5\n    console.log(\n      '\u7cbe\u7075\u5f53\u524d\u4f4d\u7f6e-x:',\n      liuxinsp.position.x,\n      'y:',\n      liuxinsp.position.y\n    )\n    container.addChild(liuxinsp)\n    app.stage.addChild(container)\n    // @ts-ignore\n    canvas?.appendChild(canvasInfo)\n\n    const httpdata = await Pixihttp.get_model_list()\n    for (let entry of httpdata.data) {\n      if (entry.model_category === 'stable_diffusion_xl') {\n        stable_diffusion_xls.push(entry)\n      } else if (entry.model_category === 'flux') {\n        fluxs.push(entry)\n      }\n    }\n\n    // \u6309 api_calls \u964d\u5e8f\u6392\u5e8f stable_diffusion_xls\n    stable_diffusion_xls.sort((a, b) => b.api_calls - a.api_calls)\n\n    // \u6309 api_calls \u964d\u5e8f\u6392\u5e8f fluxs\n    fluxs.sort((a, b) => b.api_calls - a.api_calls)\n\n    console.log('Stable Diffusion XL Models:', stable_diffusion_xls)\n    console.log('Flux Models:', fluxs)\n\n    // \u521d\u59cb\u5316\u753b\u5eca\u56fe\u7247\uff08\u5047\u8bbe\u6709100\u5f20\u56fe\u7247\uff09\n    for (let i = 1; i <= 100; i++) {\n      images.push(\n        'https://cdn.pixabay.com/photo/2023/08/11/08/29/highland-cattle-8183107_1280.jpg'\n      )\n    }\n  } catch (error) {\n    console.error('Failed to fetch model list:', error)\n  }\n})\n</script>\n\n<template>\n  <div class=\"root\">\n    <div ref=\"canvasdomref\">\n      <van-row justify=\"center\">\n        <van-col :span=\"20\">\n          <div class=\"main-container\">\n            <!-- \u6807\u9898\u90e8\u5206 -->\n            <van-row class=\"header\" justify=\"center\">\n              <van-col>\n                <h1 class=\"title\">\u6a21\u578b\u6d4b\u8bd5</h1>\n              </van-col>\n            </van-row>\n\n            <!-- \u6a21\u578b\u7c7b\u578b\u9009\u62e9 -->\n            <van-row class=\"section\" justify=\"center\">\n              <van-col :span=\"12\">\n                <div class=\"model-type\">\n                  <van-radio-group\n                    v-model=\"checked_type\"\n                    direction=\"horizontal\"\n                  >\n                    <van-radio name=\"1\">SD-XL</van-radio>\n                    <van-radio name=\"2\">FLUX</van-radio>\n                  </van-radio-group>\n                </div>\n              </van-col>\n            </van-row>\n\n            <!-- \u6a21\u578bID\u8f93\u5165 -->\n            <van-row class=\"section\" justify=\"center\">\n              <van-col :span=\"12\">\n                <div class=\"model-id\">\n                  <van-cell-group inset>\n                    <van-field\n                      v-model=\"model_id\"\n                      label=\"\u6a21\u578bID\uff1a\"\n                      left-icon=\"smile-o\"\n                      right-icon=\"warning-o\"\n                      placeholder=\"\u8f93\u5165\u6a21\u578bID\"\n                      clearable\n                    />\n                  </van-cell-group>\n                </div>\n              </van-col>\n            </van-row>\n\n            <!-- \u6a21\u578b\u5217\u8868\u548c\u4ee3\u7801\u8f93\u5165\u533a\u57df -->\n            <van-row class=\"section align-stretch\" justify=\"center\">\n              <!-- \u6a21\u578b\u5217\u8868 -->\n              <van-col :span=\"12\" class=\"flex-item\">\n                <div class=\"model-list\">\n                  <v-list lines=\"one\" density=\"compact\" max-height=\"300\">\n                    <v-list-item\n                      v-for=\"item in paginatedModels\"\n                      :key=\"item.model_id\"\n                      :title=\"item.model_name + ' \u8c03\u7528\u6b21\u6570\uff1a ' + item.api_calls\"\n                      :subtitle=\"'\u6a21\u578bID: ' + item.model_id\"\n                    ></v-list-item>\n                  </v-list>\n                  <v-pagination\n                    v-model=\"currentPage\"\n                    :length=\"totalPages\"\n                    class=\"pagination\"\n                  ></v-pagination>\n                </div>\n              </van-col>\n\n              <!-- \u4ee3\u7801\u8f93\u5165 -->\n              <van-col :span=\"12\" class=\"flex-item\">\n                <div class=\"code_input\">\n                  <v-container>\n                    <v-textarea\n                      row-height=\"15\"\n                      bg-color=\"amber-lighten-4\"\n                      color=\"orange orange-darken-4\"\n                      label=\"\u4ee3\u7801\u8f93\u5165\"\n                      rows=\"10\"\n                      v-model=\"code_input_\"\n                      placeholder='{\n  \"key\": \"\",\n  \"model_id\": \"your_model_id\",\n  \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\",\n  \"negative_prompt\": \"\",\n  \"width\": \"512\",\n  \"height\": \"512\",\n  \"samples\": \"1\",\n  \"num_inference_steps\": \"31\",\n  \"safety_checker\": \"no\",\n  \"enhance_prompt\": \"yes\",\n  \"seed\": null,\n  \"guidance_scale\": 7.5,\n  \"panorama\": \"no\",\n  \"self_attention\": \"no\",\n  \"upscale\": \"no\",\n  \"embeddings_model\": null,\n  \"lora_model\": null,\n  \"tomesd\": \"yes\",\n  \"clip_skip\": \"2\",\n  \"use_karras_sigmas\": \"yes\",\n  \"vae\": null,\n  \"lora_strength\": null,\n  \"scheduler\": \"UniPCMultistepScheduler\",\n  \"webhook\": null,\n  \"track_id\": null\n}'\n                      auto-grow\n                    ></v-textarea>\n                  </v-container>\n                  <v-btn block @click=\"ondraw\">\u5f00\u59cb\u7ed8\u56fe</v-btn>\n                </div>\n              </van-col>\n            </van-row>\n\n            <!-- \u65b0\u589e\uff1a\u753b\u5eca\u90e8\u5206 -->\n            <van-row class=\"draw\" justify=\"center\">\n              <van-col :span=\"24\">\n                <div class=\"gallery\">\n                  <h2 class=\"gallery-title\">\u753b\u5eca</h2>\n                  <van-row justify=\"start\" wrap>\n                    <van-col\n                      v-for=\"(image, index) in paginatedGalleryImages\"\n                      :key=\"index\"\n                      :span=\"4\"\n                      class=\"gallery-item\"\n                    >\n                      <img\n                        :src=\"image\"\n                        alt=\"Gallery Image\"\n                        class=\"gallery-image\"\n                      />\n                    </van-col>\n                  </van-row>\n                  <v-pagination\n                    v-model=\"galleryCurrentPage\"\n                    :length=\"galleryTotalPages\"\n                    class=\"pagination\"\n                  ></v-pagination>\n                </div>\n              </van-col>\n            </van-row>\n          </div>\n        </van-col>\n      </van-row>\n    </div>\n  </div>\n</template>\n\n<style scoped>\n.root {\n  background-color: #f5f5f5;\n  padding: 20px 0;\n  min-height: 100vh;\n}\n\n.main-container {\n  background-color: #ffffff;\n  padding: 30px;\n  border-radius: 8px;\n  box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);\n}\n\n.header {\n  margin-bottom: 30px;\n}\n\n.title {\n  font-size: 24px;\n  font-weight: 600;\n  text-align: center;\n  color: #333333;\n}\n\n.section {\n  margin-bottom: 25px;\n}\n\n.align-stretch {\n  display: flex;\n  align-items: stretch;\n}\n\n.flex-item {\n  display: flex;\n}\n\n.model-type {\n  display: flex;\n  justify-content: center;\n}\n\n.model-id .van-field__control {\n  padding-left: 10px;\n}\n\n.model-list,\n.code_input {\n  background-color: #fafafa;\n  padding: 15px;\n  border-radius: 6px;\n  border: 1px solid #eaeaea;\n  flex: 1;\n  display: flex;\n  flex-direction: column;\n}\n.gallery {\n  padding: 15px;\n  border-radius: 6px;\n\n  flex: 1;\n  display: flex;\n  flex-direction: column;\n}\n.draw {\n  background-color: #fafafa;\n  padding: 15px;\n  border-radius: 6px;\n  border: 1px solid #eaeaea;\n  flex: 1;\n  display: flex;\n  flex-direction: column;\n}\n.v-list-item {\n  padding: 10px 15px;\n  border-bottom: 1px solid #eaeaea;\n  flex: none;\n}\n\n.v-list-item:last-child {\n  border-bottom: none;\n}\n\n.v-list-item__title {\n  font-weight: 500;\n  color: #555555;\n}\n\n.v-list-item__subtitle {\n  color: #888888;\n  font-size: 14px;\n}\n\n.pagination {\n  display: flex;\n  justify-content: center;\n  margin-top: 20px;\n  flex-shrink: 0;\n}\n\n/* \u753b\u5eca\u6837\u5f0f */\n.gallery {\n  padding: 20px 0;\n}\n\n.gallery-title {\n  text-align: center;\n  margin-bottom: 20px;\n  font-size: 20px;\n  color: #333333;\n}\n\n.gallery-item {\n  padding: 10px;\n}\n\n.gallery-image {\n  width: 100%;\n  height: auto;\n  border-radius: 4px;\n  object-fit: cover;\n}\n</style>\n\u6211\u5f97\u5230\u4e86PreView.vue:88 PixiJS Warning:  [Assets] Asset id /src/assets/liuxin/liuxin.png was not found in the Cache\n(anonymous)\t@\tPreView.vue:88\nPromise.then\t\t\n(anonymous)\t@\tmain.ts:26\nShow 25 more frames\nPreView.vue:89 PixiJS Warning:  [Assets] Asset id /src/assets/liuxin/liuxin.png was not found in the Cache\nPreView.vue:140 Failed to fetch model list: TypeError: Cannot read properties of undefined (reading 'source')\n    at new _Spritesheet2 (chunk-NQFDCHBS.js?v=53b38b7a:1280:34)\n    at PreView.vue:89:25\n\ufeff\n"},{"assistant_initiates":false,"asst_text":"Certainly! Here are some relevant search tags based on your query:\n\n- Examination\n- Assessment\n- Trial\n- Evaluation\n- Testing\n- Quiz\n- Study\n- Certification\n- Debugging\n- Software Testing\n- Unit Test\n- Performance Test\n- Load Test\n- Test Cases\n- Test Plan\n- Test Automation\n- Test Design\n- Test Strategy\n- Test Management\n- Educational Testing\n\nThese tags cover a range of topics related to \"test,\" including academic exams, software testing, and evaluation methods.","behavior":"degenerate_repetition","conversation_hash":"8aa9ca690d5c272df0e037b92e871067","conversation_redacted":false,"conversation_toxic":false,"country":"France","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":599,"n_turns_in_conversation":1,"n_words":101,"source_row_offset":28476,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:31:18 GMT","turn_id":"555591c9edf297f23bdd5d35","turn_index":0,"user_text":"System: Generate a list of relevant search tags based on the following query and context.\nUser: Query: test\nContext: None\nAssistant:"},{"assistant_initiates":false,"asst_text":"Hello! How can I assist you today?","behavior":"degenerate_repetition","conversation_hash":"d6a9b5ec7baa0024275770df10b2d3b3","conversation_redacted":false,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Nolang","model":"o1-preview-2024-09-12","n_chars":34,"n_turns_in_conversation":1,"n_words":7,"source_row_offset":28477,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:31:40 GMT","turn_id":"5f50cb38f32ec211f62109db","turn_index":0,"user_text":""},{"assistant_initiates":false,"asst_text":"**Step 1: Understand the User's Query**\n\nThe user has provided the query: **\"test\"**.\n\nThis term is broad and can pertain to various contexts, including but not limited to:\n\n- **Educational Assessments**: Exams, quizzes, ability tests.\n- **Software Testing**: Unit tests, integration tests, system testing.\n- **SEO Optimization**: A/B testing, meta tag testing.\n- **Monitoring and Evaluation**: Project assessments, performance evaluations.\n- **General Usage**: Any process of evaluating or measuring something.\n\n**Step 2: Analyze the Provided Relevant Information**\n\nThe \"Relevant Info\" includes snippets related to:\n\n1. **SEO Meta Tags and HTML Elements**:\n   - Importance of H2s, image alt tags.\n   - Directives like `noindex` and `nofollow`.\n   - Schema markup for SEO optimization.\n   \n2. **Educational Assessments**:\n   - Ability/Achievement tests evaluating understanding and capability.\n   - Study aids like flashcards and practice tests.\n   - Different exam types: short answer, essay, case study.\n\n3. **Software Testing**:\n   - Importance of testing in software development.\n   - Types and best practices of software testing.\n\n4. **Monitoring and Evaluation (M&E) Methods**:\n   - Tools and processes for assessing project performance.\n   - Pros and cons of different evaluation types.\n\n**Step 3: Determine the Most Relevant Context**\n\nGiven the breadth of the term \"test\" and the provided information, it's essential to identify the specific area of interest. The relevant contexts based on the snippets are:\n\n- **SEO Optimization**\n- **Educational Assessments**\n- **Software Testing**\n- **Monitoring and Evaluation**\n\n**Step 4: Provide Detailed Information on Each Context**\n\n1. **SEO Optimization**:\n   - **Meta Tags**: Elements like `<title>`, `<meta description>`, and `<meta keywords>` help search engines understand page content.\n   - **HTML Elements**: Proper use of header tags (H1-H6) and image `alt` attributes improves accessibility and SEO.\n   - **Directives**:\n     - `noindex`: Instructs search engines not to index a page.\n     - `nofollow`: Tells search engines not to follow the links on a page.\n   - **Schema Markup**: Structured data that enhances search engine understanding, leading to better rankings and rich snippets.\n\n2. **Educational Assessments**:\n   - **Ability/Achievement Tests**: Measure a student's knowledge and skills in specific areas.\n   - **Study Aids**: Tools like flashcards and practice tests enhance learning and retention.\n   - **Exam Types**:\n     - **Short Answer Exams**: Require concise responses.\n     - **Essay Exams**: Assess the ability to formulate and express complex ideas.\n     - **Case Study Exams**: Evaluate problem-solving and analytical skills in real-world scenarios.\n\n3. **Software Testing**:\n   - **Definition**: The process of evaluating software to ensure it meets the required standards and functions as intended.\n   - **Importance**: Identifies bugs, ensures reliability, and enhances user satisfaction.\n   - **Types**:\n     - **Unit Testing**: Tests individual components.\n     - **Integration Testing**: Ensures combined parts work together.\n     - **System Testing**: Evaluates the complete and integrated software.\n     - **Acceptance Testing**: Determines if the software meets business requirements.\n\n4. **Monitoring and Evaluation (M&E) Methods**:\n   - **Purpose**: Assess the effectiveness, efficiency, and impact of projects or programs.\n   - **Methods**: Can include surveys, interviews, data analysis, and performance metrics.\n   - **Pros and Cons**: Different evaluation types offer various advantages and limitations based on the context and objectives.\n\n**Final Answer**\n\nThe term \"**test**\" encompasses multiple domains, each with its specific methodologies and purposes. Based on the provided information, here's a summary of the primary contexts:\n\n1. **SEO Optimization**: Involves testing various elements like meta tags, HTML structures, and schema markup to enhance website visibility and ranking on search engines.\n\n2. **Educational Assessments**: Entails designing and utilizing different types of tests and study aids to evaluate and improve student or worker performance and understanding.\n\n3. **Software Testing**: Focuses on systematically evaluating software applications to identify and rectify defects, ensuring functionality, reliability, and performance.\n\n4. **Monitoring and Evaluation (M&E)**: Centers on assessing the effectiveness and impact of projects or programs through various evaluation methods.\n\nIf you could specify which **\"test\"** context you're interested in, I can provide more detailed information or guidance tailored to that area.","behavior":"degenerate_repetition","conversation_hash":"b52c01bb6cf3c3c368482e2c69e7a405","conversation_redacted":false,"conversation_toxic":false,"country":"France","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":7573,"n_turns_in_conversation":1,"n_words":1084,"source_row_offset":28478,"source_shard":"turns-00029.parquet","timestamp":"Sat, 21 Sep 2024 00:31:43 GMT","turn_id":"e0841738e318ca6926151c62","turn_index":0,"user_text":"System: You are an **advanced AI assistant** capable of detailed, multi-step problem solving. Break down tasks into clear, logical steps. Explain each step thoroughly and provide a final answer. If you need additional information, use the following format for search queries IMPORTANT:\n\n```search\n{\n  \"queries\": [\n    \"first search query\",\n    \"second search query\",\n    \"third search query\",\n    \"etc\"\n  ]\n}```\n\nUser: Query: test\n\nIteration: 1\n\nPrevious Attempts:\n\n\nRelevant Info:\nThe same is true for HTML elements and attributes on the page, like H2s and image alt tags. So, let's dive in. Here are the top 14 meta tags and HTML elements for you to use on your site. Read ......\nThe noindex directive tells search engines not to include the page in search results. The nofollow directive tells crawlers not to follow the links on the page. 7. Schema Markup. Schema markup is a ty...\nOptimizing your SEO meta tags is crucial for improving your site's visibility and ranking in search engines. Here are some general tips to keep in mind when optimizing your SEO meta tags: Stay relevan...\nAbility/ Achievement tests are used to evaluate a student's or worker's understanding, comprehension, knowledge and/or capability in a particular area. They are used in academics, professions and many...\n3. Make your own study aids. When it comes to learning, practice tests work BETTER than simply highlighting or re-reading your notes. So, turn your notes into flashcards or use a flashcard app for mem...\nShort answer exams- double check that you have answered all parts of the question. Essay exams - practise writing essay responses under timed exam conditions. Case study exams - ensure that you refer ...\nLearn what software testing is, why it is important, and how to perform different types of tests. Explore the history, benefits and best practices of software testing, and how IBM can help you with to...\nLearn what software testing is, why it is important, and how it is done. Explore different types, techniques, levels, and benefits of software testing with examples and references....\nLearn what software testing is, why it is important, and how it is done. Explore the different categories, levels, and approaches of software testing, such as automated, static, dynamic, and explorato...\nMonitoring and Evaluation (M&E) methods encompass the tools, techniques, and processes used to assess the performance of projects, programs, or policies. These methods are essential in determining whe...\nRelated: What is the difference between monitoring and evaluation 10. Pros and cons of different types of evaluations. When considering the pros and cons of different types of evaluations, it is impor...\nEvaluation Methods and Approaches. For any task in evaluation, the choice of method, process or approach depends on what is appropriate for your particular situation. You can browse a range of evaluat...\nAssistant:"}],"shard":{"index":29,"name":"turns-00029.parquet","rows":54303}}