Add files using upload-large-folder tool

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0024000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_00:48:01 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0024000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0024000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0024000.pt
+[ckpt] step=24000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0024000.pt",
+  "step": 24000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 37.619945964953295,
+    "nll_per_token": 3.6275343875335566,
+    "tokens": 31104,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 43.23458093990694,
+    "nll_per_token": 3.7666406597531137,
+    "tokens": 26976,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.199855812913517,
+    "unique_tokens": 1283,
+    "token_count": 32768,
+    "distinct_1": 0.039154052734375,
+    "distinct_2": 0.2093380905511811,
+    "top_token_mass": 0.17962646484375
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0024000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_00:49:28 done step_0024000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0026000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_00:59:11 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0026000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0026000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0026000.pt
+[ckpt] step=26000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0026000.pt",
+  "step": 26000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 35.89577727548234,
+    "nll_per_token": 3.580619663937538,
+    "tokens": 29884,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 49.59969132385835,
+    "nll_per_token": 3.9039846104022096,
+    "tokens": 24642,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.084428652987828,
+    "unique_tokens": 1684,
+    "token_count": 32768,
+    "distinct_1": 0.0513916015625,
+    "distinct_2": 0.26141117125984253,
+    "top_token_mass": 0.28240966796875
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0026000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_01:00:38 done step_0026000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0036000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_01:55:06 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0036000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0036000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0036000.pt
+[ckpt] step=36000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0036000.pt",
+  "step": 36000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 32.03130455319775,
+    "nll_per_token": 3.466713691895554,
+    "tokens": 34847,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 43.99512686882181,
+    "nll_per_token": 3.7840788748033742,
+    "tokens": 29005,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.594531016483154,
+    "unique_tokens": 2055,
+    "token_count": 32768,
+    "distinct_1": 0.062713623046875,
+    "distinct_2": 0.30748646653543305,
+    "top_token_mass": 0.12908935546875
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0036000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_01:56:33 done step_0036000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0043000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_02:33:51 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0043000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0043000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0043000.pt
+[ckpt] step=43000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0043000.pt",
+  "step": 43000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 32.396833110183806,
+    "nll_per_token": 3.47806067450823,
+    "tokens": 36558,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 46.66273572534209,
+    "nll_per_token": 3.8429458959363183,
+    "tokens": 30142,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.7134501397670774,
+    "unique_tokens": 2314,
+    "token_count": 32768,
+    "distinct_1": 0.07061767578125,
+    "distinct_2": 0.343626968503937,
+    "top_token_mass": 0.089569091796875
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0043000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_02:35:19 done step_0043000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0057000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_03:52:12 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0057000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0057000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0057000.pt
+[ckpt] step=57000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0057000.pt",
+  "step": 57000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 34.365326774260126,
+    "nll_per_token": 3.537048113454887,
+    "tokens": 33970,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 47.077690502065494,
+    "nll_per_token": 3.851799226432384,
+    "tokens": 28220,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.549639476387168,
+    "unique_tokens": 1947,
+    "token_count": 32768,
+    "distinct_1": 0.059417724609375,
+    "distinct_2": 0.308224655511811,
+    "top_token_mass": 0.161376953125
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0057000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_03:53:39 done step_0057000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0061000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_04:14:35 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0061000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0061000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0061000.pt
+[ckpt] step=61000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0061000.pt",
+  "step": 61000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 36.6153939417802,
+    "nll_per_token": 3.6004687514823353,
+    "tokens": 32940,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 49.96815178111428,
+    "nll_per_token": 3.9113858381024373,
+    "tokens": 27368,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.471410769547124,
+    "unique_tokens": 1977,
+    "token_count": 32768,
+    "distinct_1": 0.060333251953125,
+    "distinct_2": 0.31508366141732286,
+    "top_token_mass": 0.18902587890625
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0061000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_04:16:04 done step_0061000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0062000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_04:19:49 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0062000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0062000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0062000.pt
+[ckpt] step=62000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0062000.pt",
+  "step": 62000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 35.85427846491238,
+    "nll_per_token": 3.5794629034369594,
+    "tokens": 33538,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 51.2420066753968,
+    "nll_per_token": 3.9365596385450212,
+    "tokens": 27637,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.4497637957927205,
+    "unique_tokens": 2328,
+    "token_count": 32768,
+    "distinct_1": 0.071044921875,
+    "distinct_2": 0.3408895177165354,
+    "top_token_mass": 0.182647705078125
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0062000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_04:21:17 done step_0062000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0075000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_05:32:13 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0075000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0075000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0075000.pt
+[ckpt] step=75000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0075000.pt",
+  "step": 75000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 32.95752017664256,
+    "nll_per_token": 3.4952194648516532,
+    "tokens": 31139,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 41.776719632486575,
+    "nll_per_token": 3.732339237787117,
+    "tokens": 26285,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.1386953288155235,
+    "unique_tokens": 1595,
+    "token_count": 32768,
+    "distinct_1": 0.048675537109375,
+    "distinct_2": 0.25406003937007876,
+    "top_token_mass": 0.238525390625
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0075000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_05:33:40 done step_0075000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0096000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_07:29:29 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0096000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0096000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0096000.pt
+[ckpt] step=96000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0096000.pt",
+  "step": 96000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 34.53827260221156,
+    "nll_per_token": 3.542068059951168,
+    "tokens": 34826,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 47.05061183472258,
+    "nll_per_token": 3.8512238698729293,
+    "tokens": 29034,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.6155513789703067,
+    "unique_tokens": 2021,
+    "token_count": 32768,
+    "distinct_1": 0.061676025390625,
+    "distinct_2": 0.3253567913385827,
+    "top_token_mass": 0.132080078125
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0096000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_07:30:58 done step_0096000

LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0121000_logistic_normal_t1p45.log

@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_09:49:18 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0121000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0121000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0121000.pt
+[ckpt] step=121000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0121000.pt",
+  "step": 121000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 33.29695020508285,
+    "nll_per_token": 3.505465807359249,
+    "tokens": 34680,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 44.710081598534735,
+    "nll_per_token": 3.800199015306508,
+    "tokens": 28945,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.563704315569347,
+    "unique_tokens": 2256,
+    "token_count": 32768,
+    "distinct_1": 0.06884765625,
+    "distinct_2": 0.36192790354330706,
+    "top_token_mass": 0.141448974609375
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0121000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_09:50:46 done step_0121000

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/yaml/emitter.py

@@ -0,0 +1,1137 @@
+
+# Emitter expects events obeying the following grammar:
+# stream ::= STREAM-START document* STREAM-END
+# document ::= DOCUMENT-START node DOCUMENT-END
+# node ::= SCALAR | sequence | mapping
+# sequence ::= SEQUENCE-START node* SEQUENCE-END
+# mapping ::= MAPPING-START (node node)* MAPPING-END
+
+__all__ = ['Emitter', 'EmitterError']
+
+from .error import YAMLError
+from .events import *
+
+class EmitterError(YAMLError):
+    pass
+
+class ScalarAnalysis:
+    def __init__(self, scalar, empty, multiline,
+            allow_flow_plain, allow_block_plain,
+            allow_single_quoted, allow_double_quoted,
+            allow_block):
+        self.scalar = scalar
+        self.empty = empty
+        self.multiline = multiline
+        self.allow_flow_plain = allow_flow_plain
+        self.allow_block_plain = allow_block_plain
+        self.allow_single_quoted = allow_single_quoted
+        self.allow_double_quoted = allow_double_quoted
+        self.allow_block = allow_block
+
+class Emitter:
+
+    DEFAULT_TAG_PREFIXES = {
+        '!' : '!',
+        'tag:yaml.org,2002:' : '!!',
+    }
+
+    def __init__(self, stream, canonical=None, indent=None, width=None,
+            allow_unicode=None, line_break=None):
+
+        # The stream should have the methods `write` and possibly `flush`.
+        self.stream = stream
+
+        # Encoding can be overridden by STREAM-START.
+        self.encoding = None
+
+        # Emitter is a state machine with a stack of states to handle nested
+        # structures.
+        self.states = []
+        self.state = self.expect_stream_start
+
+        # Current event and the event queue.
+        self.events = []
+        self.event = None
+
+        # The current indentation level and the stack of previous indents.
+        self.indents = []
+        self.indent = None
+
+        # Flow level.
+        self.flow_level = 0
+
+        # Contexts.
+        self.root_context = False
+        self.sequence_context = False
+        self.mapping_context = False
+        self.simple_key_context = False
+
+        # Characteristics of the last emitted character:
+        #  - current position.
+        #  - is it a whitespace?
+        #  - is it an indention character
+        #    (indentation space, '-', '?', or ':')?
+        self.line = 0
+        self.column = 0
+        self.whitespace = True
+        self.indention = True
+
+        # Whether the document requires an explicit document indicator
+        self.open_ended = False
+
+        # Formatting details.
+        self.canonical = canonical
+        self.allow_unicode = allow_unicode
+        self.best_indent = 2
+        if indent and 1 < indent < 10:
+            self.best_indent = indent
+        self.best_width = 80
+        if width and width > self.best_indent*2:
+            self.best_width = width
+        self.best_line_break = '\n'
+        if line_break in ['\r', '\n', '\r\n']:
+            self.best_line_break = line_break
+
+        # Tag prefixes.
+        self.tag_prefixes = None
+
+        # Prepared anchor and tag.
+        self.prepared_anchor = None
+        self.prepared_tag = None
+
+        # Scalar analysis and style.
+        self.analysis = None
+        self.style = None
+
+    def dispose(self):
+        # Reset the state attributes (to clear self-references)
+        self.states = []
+        self.state = None
+
+    def emit(self, event):
+        self.events.append(event)
+        while not self.need_more_events():
+            self.event = self.events.pop(0)
+            self.state()
+            self.event = None
+
+    # In some cases, we wait for a few next events before emitting.
+
+    def need_more_events(self):
+        if not self.events:
+            return True
+        event = self.events[0]
+        if isinstance(event, DocumentStartEvent):
+            return self.need_events(1)
+        elif isinstance(event, SequenceStartEvent):
+            return self.need_events(2)
+        elif isinstance(event, MappingStartEvent):
+            return self.need_events(3)
+        else:
+            return False
+
+    def need_events(self, count):
+        level = 0
+        for event in self.events[1:]:
+            if isinstance(event, (DocumentStartEvent, CollectionStartEvent)):
+                level += 1
+            elif isinstance(event, (DocumentEndEvent, CollectionEndEvent)):
+                level -= 1
+            elif isinstance(event, StreamEndEvent):
+                level = -1
+            if level < 0:
+                return False
+        return (len(self.events) < count+1)
+
+    def increase_indent(self, flow=False, indentless=False):
+        self.indents.append(self.indent)
+        if self.indent is None:
+            if flow:
+                self.indent = self.best_indent
+            else:
+                self.indent = 0
+        elif not indentless:
+            self.indent += self.best_indent
+
+    # States.
+
+    # Stream handlers.
+
+    def expect_stream_start(self):
+        if isinstance(self.event, StreamStartEvent):
+            if self.event.encoding and not hasattr(self.stream, 'encoding'):
+                self.encoding = self.event.encoding
+            self.write_stream_start()
+            self.state = self.expect_first_document_start
+        else:
+            raise EmitterError("expected StreamStartEvent, but got %s"
+                    % self.event)
+
+    def expect_nothing(self):
+        raise EmitterError("expected nothing, but got %s" % self.event)
+
+    # Document handlers.
+
+    def expect_first_document_start(self):
+        return self.expect_document_start(first=True)
+
+    def expect_document_start(self, first=False):
+        if isinstance(self.event, DocumentStartEvent):
+            if (self.event.version or self.event.tags) and self.open_ended:
+                self.write_indicator('...', True)
+                self.write_indent()
+            if self.event.version:
+                version_text = self.prepare_version(self.event.version)
+                self.write_version_directive(version_text)
+            self.tag_prefixes = self.DEFAULT_TAG_PREFIXES.copy()
+            if self.event.tags:
+                handles = sorted(self.event.tags.keys())
+                for handle in handles:
+                    prefix = self.event.tags[handle]
+                    self.tag_prefixes[prefix] = handle
+                    handle_text = self.prepare_tag_handle(handle)
+                    prefix_text = self.prepare_tag_prefix(prefix)
+                    self.write_tag_directive(handle_text, prefix_text)
+            implicit = (first and not self.event.explicit and not self.canonical
+                    and not self.event.version and not self.event.tags
+                    and not self.check_empty_document())
+            if not implicit:
+                self.write_indent()
+                self.write_indicator('---', True)
+                if self.canonical:
+                    self.write_indent()
+            self.state = self.expect_document_root
+        elif isinstance(self.event, StreamEndEvent):
+            if self.open_ended:
+                self.write_indicator('...', True)
+                self.write_indent()
+            self.write_stream_end()
+            self.state = self.expect_nothing
+        else:
+            raise EmitterError("expected DocumentStartEvent, but got %s"
+                    % self.event)
+
+    def expect_document_end(self):
+        if isinstance(self.event, DocumentEndEvent):
+            self.write_indent()
+            if self.event.explicit:
+                self.write_indicator('...', True)
+                self.write_indent()
+            self.flush_stream()
+            self.state = self.expect_document_start
+        else:
+            raise EmitterError("expected DocumentEndEvent, but got %s"
+                    % self.event)
+
+    def expect_document_root(self):
+        self.states.append(self.expect_document_end)
+        self.expect_node(root=True)
+
+    # Node handlers.
+
+    def expect_node(self, root=False, sequence=False, mapping=False,
+            simple_key=False):
+        self.root_context = root
+        self.sequence_context = sequence
+        self.mapping_context = mapping
+        self.simple_key_context = simple_key
+        if isinstance(self.event, AliasEvent):
+            self.expect_alias()
+        elif isinstance(self.event, (ScalarEvent, CollectionStartEvent)):
+            self.process_anchor('&')
+            self.process_tag()
+            if isinstance(self.event, ScalarEvent):
+                self.expect_scalar()
+            elif isinstance(self.event, SequenceStartEvent):
+                if self.flow_level or self.canonical or self.event.flow_style   \
+                        or self.check_empty_sequence():
+                    self.expect_flow_sequence()
+                else:
+                    self.expect_block_sequence()
+            elif isinstance(self.event, MappingStartEvent):
+                if self.flow_level or self.canonical or self.event.flow_style   \
+                        or self.check_empty_mapping():
+                    self.expect_flow_mapping()
+                else:
+                    self.expect_block_mapping()
+        else:
+            raise EmitterError("expected NodeEvent, but got %s" % self.event)
+
+    def expect_alias(self):
+        if self.event.anchor is None:
+            raise EmitterError("anchor is not specified for alias")
+        self.process_anchor('*')
+        self.state = self.states.pop()
+
+    def expect_scalar(self):
+        self.increase_indent(flow=True)
+        self.process_scalar()
+        self.indent = self.indents.pop()
+        self.state = self.states.pop()
+
+    # Flow sequence handlers.
+
+    def expect_flow_sequence(self):
+        self.write_indicator('[', True, whitespace=True)
+        self.flow_level += 1
+        self.increase_indent(flow=True)
+        self.state = self.expect_first_flow_sequence_item
+
+    def expect_first_flow_sequence_item(self):
+        if isinstance(self.event, SequenceEndEvent):
+            self.indent = self.indents.pop()
+            self.flow_level -= 1
+            self.write_indicator(']', False)
+            self.state = self.states.pop()
+        else:
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            self.states.append(self.expect_flow_sequence_item)
+            self.expect_node(sequence=True)
+
+    def expect_flow_sequence_item(self):
+        if isinstance(self.event, SequenceEndEvent):
+            self.indent = self.indents.pop()
+            self.flow_level -= 1
+            if self.canonical:
+                self.write_indicator(',', False)
+                self.write_indent()
+            self.write_indicator(']', False)
+            self.state = self.states.pop()
+        else:
+            self.write_indicator(',', False)
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            self.states.append(self.expect_flow_sequence_item)
+            self.expect_node(sequence=True)
+
+    # Flow mapping handlers.
+
+    def expect_flow_mapping(self):
+        self.write_indicator('{', True, whitespace=True)
+        self.flow_level += 1
+        self.increase_indent(flow=True)
+        self.state = self.expect_first_flow_mapping_key
+
+    def expect_first_flow_mapping_key(self):
+        if isinstance(self.event, MappingEndEvent):
+            self.indent = self.indents.pop()
+            self.flow_level -= 1
+            self.write_indicator('}', False)
+            self.state = self.states.pop()
+        else:
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            if not self.canonical and self.check_simple_key():
+                self.states.append(self.expect_flow_mapping_simple_value)
+                self.expect_node(mapping=True, simple_key=True)
+            else:
+                self.write_indicator('?', True)
+                self.states.append(self.expect_flow_mapping_value)
+                self.expect_node(mapping=True)
+
+    def expect_flow_mapping_key(self):
+        if isinstance(self.event, MappingEndEvent):
+            self.indent = self.indents.pop()
+            self.flow_level -= 1
+            if self.canonical:
+                self.write_indicator(',', False)
+                self.write_indent()
+            self.write_indicator('}', False)
+            self.state = self.states.pop()
+        else:
+            self.write_indicator(',', False)
+            if self.canonical or self.column > self.best_width:
+                self.write_indent()
+            if not self.canonical and self.check_simple_key():
+                self.states.append(self.expect_flow_mapping_simple_value)
+                self.expect_node(mapping=True, simple_key=True)
+            else:
+                self.write_indicator('?', True)
+                self.states.append(self.expect_flow_mapping_value)
+                self.expect_node(mapping=True)
+
+    def expect_flow_mapping_simple_value(self):
+        self.write_indicator(':', False)
+        self.states.append(self.expect_flow_mapping_key)
+        self.expect_node(mapping=True)
+
+    def expect_flow_mapping_value(self):
+        if self.canonical or self.column > self.best_width:
+            self.write_indent()
+        self.write_indicator(':', True)
+        self.states.append(self.expect_flow_mapping_key)
+        self.expect_node(mapping=True)
+
+    # Block sequence handlers.
+
+    def expect_block_sequence(self):
+        indentless = (self.mapping_context and not self.indention)
+        self.increase_indent(flow=False, indentless=indentless)
+        self.state = self.expect_first_block_sequence_item
+
+    def expect_first_block_sequence_item(self):
+        return self.expect_block_sequence_item(first=True)
+
+    def expect_block_sequence_item(self, first=False):
+        if not first and isinstance(self.event, SequenceEndEvent):
+            self.indent = self.indents.pop()
+            self.state = self.states.pop()
+        else:
+            self.write_indent()
+            self.write_indicator('-', True, indention=True)
+            self.states.append(self.expect_block_sequence_item)
+            self.expect_node(sequence=True)
+
+    # Block mapping handlers.
+
+    def expect_block_mapping(self):
+        self.increase_indent(flow=False)
+        self.state = self.expect_first_block_mapping_key
+
+    def expect_first_block_mapping_key(self):
+        return self.expect_block_mapping_key(first=True)
+
+    def expect_block_mapping_key(self, first=False):
+        if not first and isinstance(self.event, MappingEndEvent):
+            self.indent = self.indents.pop()
+            self.state = self.states.pop()
+        else:
+            self.write_indent()
+            if self.check_simple_key():
+                self.states.append(self.expect_block_mapping_simple_value)
+                self.expect_node(mapping=True, simple_key=True)
+            else:
+                self.write_indicator('?', True, indention=True)
+                self.states.append(self.expect_block_mapping_value)
+                self.expect_node(mapping=True)
+
+    def expect_block_mapping_simple_value(self):
+        self.write_indicator(':', False)
+        self.states.append(self.expect_block_mapping_key)
+        self.expect_node(mapping=True)
+
+    def expect_block_mapping_value(self):
+        self.write_indent()
+        self.write_indicator(':', True, indention=True)
+        self.states.append(self.expect_block_mapping_key)
+        self.expect_node(mapping=True)
+
+    # Checkers.
+
+    def check_empty_sequence(self):
+        return (isinstance(self.event, SequenceStartEvent) and self.events
+                and isinstance(self.events[0], SequenceEndEvent))
+
+    def check_empty_mapping(self):
+        return (isinstance(self.event, MappingStartEvent) and self.events
+                and isinstance(self.events[0], MappingEndEvent))
+
+    def check_empty_document(self):
+        if not isinstance(self.event, DocumentStartEvent) or not self.events:
+            return False
+        event = self.events[0]
+        return (isinstance(event, ScalarEvent) and event.anchor is None
+                and event.tag is None and event.implicit and event.value == '')
+
+    def check_simple_key(self):
+        length = 0
+        if isinstance(self.event, NodeEvent) and self.event.anchor is not None:
+            if self.prepared_anchor is None:
+                self.prepared_anchor = self.prepare_anchor(self.event.anchor)
+            length += len(self.prepared_anchor)
+        if isinstance(self.event, (ScalarEvent, CollectionStartEvent))  \
+                and self.event.tag is not None:
+            if self.prepared_tag is None:
+                self.prepared_tag = self.prepare_tag(self.event.tag)
+            length += len(self.prepared_tag)
+        if isinstance(self.event, ScalarEvent):
+            if self.analysis is None:
+                self.analysis = self.analyze_scalar(self.event.value)
+            length += len(self.analysis.scalar)
+        return (length < 128 and (isinstance(self.event, AliasEvent)
+            or (isinstance(self.event, ScalarEvent)
+                    and not self.analysis.empty and not self.analysis.multiline)
+            or self.check_empty_sequence() or self.check_empty_mapping()))
+
+    # Anchor, Tag, and Scalar processors.
+
+    def process_anchor(self, indicator):
+        if self.event.anchor is None:
+            self.prepared_anchor = None
+            return
+        if self.prepared_anchor is None:
+            self.prepared_anchor = self.prepare_anchor(self.event.anchor)
+        if self.prepared_anchor:
+            self.write_indicator(indicator+self.prepared_anchor, True)
+        self.prepared_anchor = None
+
+    def process_tag(self):
+        tag = self.event.tag
+        if isinstance(self.event, ScalarEvent):
+            if self.style is None:
+                self.style = self.choose_scalar_style()
+            if ((not self.canonical or tag is None) and
+                ((self.style == '' and self.event.implicit[0])
+                        or (self.style != '' and self.event.implicit[1]))):
+                self.prepared_tag = None
+                return
+            if self.event.implicit[0] and tag is None:
+                tag = '!'
+                self.prepared_tag = None
+        else:
+            if (not self.canonical or tag is None) and self.event.implicit:
+                self.prepared_tag = None
+                return
+        if tag is None:
+            raise EmitterError("tag is not specified")
+        if self.prepared_tag is None:
+            self.prepared_tag = self.prepare_tag(tag)
+        if self.prepared_tag:
+            self.write_indicator(self.prepared_tag, True)
+        self.prepared_tag = None
+
+    def choose_scalar_style(self):
+        if self.analysis is None:
+            self.analysis = self.analyze_scalar(self.event.value)
+        if self.event.style == '"' or self.canonical:
+            return '"'
+        if not self.event.style and self.event.implicit[0]:
+            if (not (self.simple_key_context and
+                    (self.analysis.empty or self.analysis.multiline))
+                and (self.flow_level and self.analysis.allow_flow_plain
+                    or (not self.flow_level and self.analysis.allow_block_plain))):
+                return ''
+        if self.event.style and self.event.style in '|>':
+            if (not self.flow_level and not self.simple_key_context
+                    and self.analysis.allow_block):
+                return self.event.style
+        if not self.event.style or self.event.style == '\'':
+            if (self.analysis.allow_single_quoted and
+                    not (self.simple_key_context and self.analysis.multiline)):
+                return '\''
+        return '"'
+
+    def process_scalar(self):
+        if self.analysis is None:
+            self.analysis = self.analyze_scalar(self.event.value)
+        if self.style is None:
+            self.style = self.choose_scalar_style()
+        split = (not self.simple_key_context)
+        #if self.analysis.multiline and split    \
+        #        and (not self.style or self.style in '\'\"'):
+        #    self.write_indent()
+        if self.style == '"':
+            self.write_double_quoted(self.analysis.scalar, split)
+        elif self.style == '\'':
+            self.write_single_quoted(self.analysis.scalar, split)
+        elif self.style == '>':
+            self.write_folded(self.analysis.scalar)
+        elif self.style == '|':
+            self.write_literal(self.analysis.scalar)
+        else:
+            self.write_plain(self.analysis.scalar, split)
+        self.analysis = None
+        self.style = None
+
+    # Analyzers.
+
+    def prepare_version(self, version):
+        major, minor = version
+        if major != 1:
+            raise EmitterError("unsupported YAML version: %d.%d" % (major, minor))
+        return '%d.%d' % (major, minor)
+
+    def prepare_tag_handle(self, handle):
+        if not handle:
+            raise EmitterError("tag handle must not be empty")
+        if handle[0] != '!' or handle[-1] != '!':
+            raise EmitterError("tag handle must start and end with '!': %r" % handle)
+        for ch in handle[1:-1]:
+            if not ('0' <= ch <= '9' or 'A' <= ch <= 'Z' or 'a' <= ch <= 'z'    \
+                    or ch in '-_'):
+                raise EmitterError("invalid character %r in the tag handle: %r"
+                        % (ch, handle))
+        return handle
+
+    def prepare_tag_prefix(self, prefix):
+        if not prefix:
+            raise EmitterError("tag prefix must not be empty")
+        chunks = []
+        start = end = 0
+        if prefix[0] == '!':
+            end = 1
+        while end < len(prefix):
+            ch = prefix[end]
+            if '0' <= ch <= '9' or 'A' <= ch <= 'Z' or 'a' <= ch <= 'z' \
+                    or ch in '-;/?!:@&=+$,_.~*\'()[]':
+                end += 1
+            else:
+                if start < end:
+                    chunks.append(prefix[start:end])
+                start = end = end+1
+                data = ch.encode('utf-8')
+                for ch in data:
+                    chunks.append('%%%02X' % ord(ch))
+        if start < end:
+            chunks.append(prefix[start:end])
+        return ''.join(chunks)
+
+    def prepare_tag(self, tag):
+        if not tag:
+            raise EmitterError("tag must not be empty")
+        if tag == '!':
+            return tag
+        handle = None
+        suffix = tag
+        prefixes = sorted(self.tag_prefixes.keys())
+        for prefix in prefixes:
+            if tag.startswith(prefix)   \
+                    and (prefix == '!' or len(prefix) < len(tag)):
+                handle = self.tag_prefixes[prefix]
+                suffix = tag[len(prefix):]
+        chunks = []
+        start = end = 0
+        while end < len(suffix):
+            ch = suffix[end]
+            if '0' <= ch <= '9' or 'A' <= ch <= 'Z' or 'a' <= ch <= 'z' \
+                    or ch in '-;/?:@&=+$,_.~*\'()[]'   \
+                    or (ch == '!' and handle != '!'):
+                end += 1
+            else:
+                if start < end:
+                    chunks.append(suffix[start:end])
+                start = end = end+1
+                data = ch.encode('utf-8')
+                for ch in data:
+                    chunks.append('%%%02X' % ch)
+        if start < end:
+            chunks.append(suffix[start:end])
+        suffix_text = ''.join(chunks)
+        if handle:
+            return '%s%s' % (handle, suffix_text)
+        else:
+            return '!<%s>' % suffix_text
+
+    def prepare_anchor(self, anchor):
+        if not anchor:
+            raise EmitterError("anchor must not be empty")
+        for ch in anchor:
+            if not ('0' <= ch <= '9' or 'A' <= ch <= 'Z' or 'a' <= ch <= 'z'    \
+                    or ch in '-_'):
+                raise EmitterError("invalid character %r in the anchor: %r"
+                        % (ch, anchor))
+        return anchor
+
+    def analyze_scalar(self, scalar):
+
+        # Empty scalar is a special case.
+        if not scalar:
+            return ScalarAnalysis(scalar=scalar, empty=True, multiline=False,
+                    allow_flow_plain=False, allow_block_plain=True,
+                    allow_single_quoted=True, allow_double_quoted=True,
+                    allow_block=False)
+
+        # Indicators and special characters.
+        block_indicators = False
+        flow_indicators = False
+        line_breaks = False
+        special_characters = False
+
+        # Important whitespace combinations.
+        leading_space = False
+        leading_break = False
+        trailing_space = False
+        trailing_break = False
+        break_space = False
+        space_break = False
+
+        # Check document indicators.
+        if scalar.startswith('---') or scalar.startswith('...'):
+            block_indicators = True
+            flow_indicators = True
+
+        # First character or preceded by a whitespace.
+        preceded_by_whitespace = True
+
+        # Last character or followed by a whitespace.
+        followed_by_whitespace = (len(scalar) == 1 or
+                scalar[1] in '\0 \t\r\n\x85\u2028\u2029')
+
+        # The previous character is a space.
+        previous_space = False
+
+        # The previous character is a break.
+        previous_break = False
+
+        index = 0
+        while index < len(scalar):
+            ch = scalar[index]
+
+            # Check for indicators.
+            if index == 0:
+                # Leading indicators are special characters.
+                if ch in '#,[]{}&*!|>\'\"%@`':
+                    flow_indicators = True
+                    block_indicators = True
+                if ch in '?:':
+                    flow_indicators = True
+                    if followed_by_whitespace:
+                        block_indicators = True
+                if ch == '-' and followed_by_whitespace:
+                    flow_indicators = True
+                    block_indicators = True
+            else:
+                # Some indicators cannot appear within a scalar as well.
+                if ch in ',?[]{}':
+                    flow_indicators = True
+                if ch == ':':
+                    flow_indicators = True
+                    if followed_by_whitespace:
+                        block_indicators = True
+                if ch == '#' and preceded_by_whitespace:
+                    flow_indicators = True
+                    block_indicators = True
+
+            # Check for line breaks, special, and unicode characters.
+            if ch in '\n\x85\u2028\u2029':
+                line_breaks = True
+            if not (ch == '\n' or '\x20' <= ch <= '\x7E'):
+                if (ch == '\x85' or '\xA0' <= ch <= '\uD7FF'
+                        or '\uE000' <= ch <= '\uFFFD'
+                        or '\U00010000' <= ch < '\U0010ffff') and ch != '\uFEFF':
+                    unicode_characters = True
+                    if not self.allow_unicode:
+                        special_characters = True
+                else:
+                    special_characters = True
+
+            # Detect important whitespace combinations.
+            if ch == ' ':
+                if index == 0:
+                    leading_space = True
+                if index == len(scalar)-1:
+                    trailing_space = True
+                if previous_break:
+                    break_space = True
+                previous_space = True
+                previous_break = False
+            elif ch in '\n\x85\u2028\u2029':
+                if index == 0:
+                    leading_break = True
+                if index == len(scalar)-1:
+                    trailing_break = True
+                if previous_space:
+                    space_break = True
+                previous_space = False
+                previous_break = True
+            else:
+                previous_space = False
+                previous_break = False
+
+            # Prepare for the next character.
+            index += 1
+            preceded_by_whitespace = (ch in '\0 \t\r\n\x85\u2028\u2029')
+            followed_by_whitespace = (index+1 >= len(scalar) or
+                    scalar[index+1] in '\0 \t\r\n\x85\u2028\u2029')
+
+        # Let's decide what styles are allowed.
+        allow_flow_plain = True
+        allow_block_plain = True
+        allow_single_quoted = True
+        allow_double_quoted = True
+        allow_block = True
+
+        # Leading and trailing whitespaces are bad for plain scalars.
+        if (leading_space or leading_break
+                or trailing_space or trailing_break):
+            allow_flow_plain = allow_block_plain = False
+
+        # We do not permit trailing spaces for block scalars.
+        if trailing_space:
+            allow_block = False
+
+        # Spaces at the beginning of a new line are only acceptable for block
+        # scalars.
+        if break_space:
+            allow_flow_plain = allow_block_plain = allow_single_quoted = False
+
+        # Spaces followed by breaks, as well as special character are only
+        # allowed for double quoted scalars.
+        if space_break or special_characters:
+            allow_flow_plain = allow_block_plain =  \
+            allow_single_quoted = allow_block = False
+
+        # Although the plain scalar writer supports breaks, we never emit
+        # multiline plain scalars.
+        if line_breaks:
+            allow_flow_plain = allow_block_plain = False
+
+        # Flow indicators are forbidden for flow plain scalars.
+        if flow_indicators:
+            allow_flow_plain = False
+
+        # Block indicators are forbidden for block plain scalars.
+        if block_indicators:
+            allow_block_plain = False
+
+        return ScalarAnalysis(scalar=scalar,
+                empty=False, multiline=line_breaks,
+                allow_flow_plain=allow_flow_plain,
+                allow_block_plain=allow_block_plain,
+                allow_single_quoted=allow_single_quoted,
+                allow_double_quoted=allow_double_quoted,
+                allow_block=allow_block)
+
+    # Writers.
+
+    def flush_stream(self):
+        if hasattr(self.stream, 'flush'):
+            self.stream.flush()
+
+    def write_stream_start(self):
+        # Write BOM if needed.
+        if self.encoding and self.encoding.startswith('utf-16'):
+            self.stream.write('\uFEFF'.encode(self.encoding))
+
+    def write_stream_end(self):
+        self.flush_stream()
+
+    def write_indicator(self, indicator, need_whitespace,
+            whitespace=False, indention=False):
+        if self.whitespace or not need_whitespace:
+            data = indicator
+        else:
+            data = ' '+indicator
+        self.whitespace = whitespace
+        self.indention = self.indention and indention
+        self.column += len(data)
+        self.open_ended = False
+        if self.encoding:
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+
+    def write_indent(self):
+        indent = self.indent or 0
+        if not self.indention or self.column > indent   \
+                or (self.column == indent and not self.whitespace):
+            self.write_line_break()
+        if self.column < indent:
+            self.whitespace = True
+            data = ' '*(indent-self.column)
+            self.column = indent
+            if self.encoding:
+                data = data.encode(self.encoding)
+            self.stream.write(data)
+
+    def write_line_break(self, data=None):
+        if data is None:
+            data = self.best_line_break
+        self.whitespace = True
+        self.indention = True
+        self.line += 1
+        self.column = 0
+        if self.encoding:
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+
+    def write_version_directive(self, version_text):
+        data = '%%YAML %s' % version_text
+        if self.encoding:
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+        self.write_line_break()
+
+    def write_tag_directive(self, handle_text, prefix_text):
+        data = '%%TAG %s %s' % (handle_text, prefix_text)
+        if self.encoding:
+            data = data.encode(self.encoding)
+        self.stream.write(data)
+        self.write_line_break()
+
+    # Scalar streams.
+
+    def write_single_quoted(self, text, split=True):
+        self.write_indicator('\'', True)
+        spaces = False
+        breaks = False
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if spaces:
+                if ch is None or ch != ' ':
+                    if start+1 == end and self.column > self.best_width and split   \
+                            and start != 0 and end != len(text):
+                        self.write_indent()
+                    else:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if self.encoding:
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                    start = end
+            elif breaks:
+                if ch is None or ch not in '\n\x85\u2028\u2029':
+                    if text[start] == '\n':
+                        self.write_line_break()
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    self.write_indent()
+                    start = end
+            else:
+                if ch is None or ch in ' \n\x85\u2028\u2029' or ch == '\'':
+                    if start < end:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if self.encoding:
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                        start = end
+            if ch == '\'':
+                data = '\'\''
+                self.column += 2
+                if self.encoding:
+                    data = data.encode(self.encoding)
+                self.stream.write(data)
+                start = end + 1
+            if ch is not None:
+                spaces = (ch == ' ')
+                breaks = (ch in '\n\x85\u2028\u2029')
+            end += 1
+        self.write_indicator('\'', False)
+
+    ESCAPE_REPLACEMENTS = {
+        '\0':       '0',
+        '\x07':     'a',
+        '\x08':     'b',
+        '\x09':     't',
+        '\x0A':     'n',
+        '\x0B':     'v',
+        '\x0C':     'f',
+        '\x0D':     'r',
+        '\x1B':     'e',
+        '\"':       '\"',
+        '\\':       '\\',
+        '\x85':     'N',
+        '\xA0':     '_',
+        '\u2028':   'L',
+        '\u2029':   'P',
+    }
+
+    def write_double_quoted(self, text, split=True):
+        self.write_indicator('"', True)
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if ch is None or ch in '"\\\x85\u2028\u2029\uFEFF' \
+                    or not ('\x20' <= ch <= '\x7E'
+                        or (self.allow_unicode
+                            and ('\xA0' <= ch <= '\uD7FF'
+                                or '\uE000' <= ch <= '\uFFFD'))):
+                if start < end:
+                    data = text[start:end]
+                    self.column += len(data)
+                    if self.encoding:
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    start = end
+                if ch is not None:
+                    if ch in self.ESCAPE_REPLACEMENTS:
+                        data = '\\'+self.ESCAPE_REPLACEMENTS[ch]
+                    elif ch <= '\xFF':
+                        data = '\\x%02X' % ord(ch)
+                    elif ch <= '\uFFFF':
+                        data = '\\u%04X' % ord(ch)
+                    else:
+                        data = '\\U%08X' % ord(ch)
+                    self.column += len(data)
+                    if self.encoding:
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    start = end+1
+            if 0 < end < len(text)-1 and (ch == ' ' or start >= end)    \
+                    and self.column+(end-start) > self.best_width and split:
+                data = text[start:end]+'\\'
+                if start < end:
+                    start = end
+                self.column += len(data)
+                if self.encoding:
+                    data = data.encode(self.encoding)
+                self.stream.write(data)
+                self.write_indent()
+                self.whitespace = False
+                self.indention = False
+                if text[start] == ' ':
+                    data = '\\'
+                    self.column += len(data)
+                    if self.encoding:
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+            end += 1
+        self.write_indicator('"', False)
+
+    def determine_block_hints(self, text):
+        hints = ''
+        if text:
+            if text[0] in ' \n\x85\u2028\u2029':
+                hints += str(self.best_indent)
+            if text[-1] not in '\n\x85\u2028\u2029':
+                hints += '-'
+            elif len(text) == 1 or text[-2] in '\n\x85\u2028\u2029':
+                hints += '+'
+        return hints
+
+    def write_folded(self, text):
+        hints = self.determine_block_hints(text)
+        self.write_indicator('>'+hints, True)
+        if hints[-1:] == '+':
+            self.open_ended = True
+        self.write_line_break()
+        leading_space = True
+        spaces = False
+        breaks = True
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if breaks:
+                if ch is None or ch not in '\n\x85\u2028\u2029':
+                    if not leading_space and ch is not None and ch != ' '   \
+                            and text[start] == '\n':
+                        self.write_line_break()
+                    leading_space = (ch == ' ')
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    if ch is not None:
+                        self.write_indent()
+                    start = end
+            elif spaces:
+                if ch != ' ':
+                    if start+1 == end and self.column > self.best_width:
+                        self.write_indent()
+                    else:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if self.encoding:
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                    start = end
+            else:
+                if ch is None or ch in ' \n\x85\u2028\u2029':
+                    data = text[start:end]
+                    self.column += len(data)
+                    if self.encoding:
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    if ch is None:
+                        self.write_line_break()
+                    start = end
+            if ch is not None:
+                breaks = (ch in '\n\x85\u2028\u2029')
+                spaces = (ch == ' ')
+            end += 1
+
+    def write_literal(self, text):
+        hints = self.determine_block_hints(text)
+        self.write_indicator('|'+hints, True)
+        if hints[-1:] == '+':
+            self.open_ended = True
+        self.write_line_break()
+        breaks = True
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if breaks:
+                if ch is None or ch not in '\n\x85\u2028\u2029':
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    if ch is not None:
+                        self.write_indent()
+                    start = end
+            else:
+                if ch is None or ch in '\n\x85\u2028\u2029':
+                    data = text[start:end]
+                    if self.encoding:
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    if ch is None:
+                        self.write_line_break()
+                    start = end
+            if ch is not None:
+                breaks = (ch in '\n\x85\u2028\u2029')
+            end += 1
+
+    def write_plain(self, text, split=True):
+        if self.root_context:
+            self.open_ended = True
+        if not text:
+            return
+        if not self.whitespace:
+            data = ' '
+            self.column += len(data)
+            if self.encoding:
+                data = data.encode(self.encoding)
+            self.stream.write(data)
+        self.whitespace = False
+        self.indention = False
+        spaces = False
+        breaks = False
+        start = end = 0
+        while end <= len(text):
+            ch = None
+            if end < len(text):
+                ch = text[end]
+            if spaces:
+                if ch != ' ':
+                    if start+1 == end and self.column > self.best_width and split:
+                        self.write_indent()
+                        self.whitespace = False
+                        self.indention = False
+                    else:
+                        data = text[start:end]
+                        self.column += len(data)
+                        if self.encoding:
+                            data = data.encode(self.encoding)
+                        self.stream.write(data)
+                    start = end
+            elif breaks:
+                if ch not in '\n\x85\u2028\u2029':
+                    if text[start] == '\n':
+                        self.write_line_break()
+                    for br in text[start:end]:
+                        if br == '\n':
+                            self.write_line_break()
+                        else:
+                            self.write_line_break(br)
+                    self.write_indent()
+                    self.whitespace = False
+                    self.indention = False
+                    start = end
+            else:
+                if ch is None or ch in ' \n\x85\u2028\u2029':
+                    data = text[start:end]
+                    self.column += len(data)
+                    if self.encoding:
+                        data = data.encode(self.encoding)
+                    self.stream.write(data)
+                    start = end
+            if ch is not None:
+                spaces = (ch == ' ')
+                breaks = (ch in '\n\x85\u2028\u2029')
+            end += 1

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