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LTA_openwebtext_dualt/docs/lta_samples/metrics_20260517/ctx1024_sampleds_sweep_bs512_ode128_ctx1024_sampledpath_20260517_223933/hit_ratio_curve.csv

@@ -0,0 +1 @@
+config,run_name,ckpt_step,train_views_seen,train_tokens_seen,token_acc_mean,exact_count,exact_ref_count,exact_ref_hits

LTA_openwebtext_dualt/logs/selfcond_4gpu/lta_owt_gpt2cached_len1024_1gpu_rollout_returnbase_step1_gradnorm_diag_20260514_004725.log

@@ -0,0 +1,102 @@
+{
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+  "rank": 0,
+  "world_size": 1,
+  "samples": "owt_cached_chunks:8734897",
+  "vocab_size": 50257,
+  "tokenizer_vocab_size": 50257,
+  "save_dir": "runs/lta_owt_gpt2cached_len1024_1gpu_rollout_returnbase_step1_gradnorm_diag_20260514_004725",
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+  "grad_accum": 1,
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+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
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+  "dirichlet_semantic_t_curve": "linear",
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+  "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
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+  "bridge_noise_init": "logistic_normal",
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+  "wrap": true,
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+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
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+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
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+  "resume_path": ""
+}
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LTA_openwebtext_dualt/logs/selfcond_4gpu/lta_owt_gpt2cached_len1024_gradmode_rollout_return_base_diag_20260514_003039.log

@@ -0,0 +1,102 @@
+{
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+  "world_size": 1,
+  "samples": "owt_cached_chunks:8734897",
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+  "tokenizer_vocab_size": 50257,
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+  "optimizer": "muon",
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+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
+  "dirichlet_semantic_t_value": 0.0,
+  "dirichlet_semantic_t_curve": "linear",
+  "dirichlet_semantic_t_power": 1.0,
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+  "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
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+  "logistic_normal_sigma_max": 2.2,
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+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
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+  "rollout_train_corrupt_only": true,
+  "rollout_train_samplewise": true,
+  "rollout_train_compute_always": false,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "allow_tf32": true,
+  "activation_checkpointing": false,
+  "activation_checkpoint_interval": 1,
+  "activation_checkpoint_scope": "block",
+  "ddp_static_graph": false,
+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
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+  "full_train_stats": false,
+  "record_pad_truncate": false,
+  "record_add_eos": false,
+  "record_add_special_tokens": false,
+  "record_pad_token": "pad",
+  "record_shuffle_buffer": 10000,
+  "wrap": true,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 0,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
+  "detokenizer": "auto",
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+  "num_workers": 4,
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+}
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LTA_openwebtext_dualt/logs/selfcond_4gpu/lta_owt_gpt2cached_len1024_rollout_ddp_forward_return_base_diag_4gpu_20260514_002607.log

@@ -0,0 +1,104 @@
+NCCL version 2.25.1+cuda12.8
+{
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+  "rank": 0,
+  "world_size": 4,
+  "samples": "owt_cached_chunks:8734897",
+  "vocab_size": 50257,
+  "tokenizer_vocab_size": 50257,
+  "save_dir": "runs/lta_owt_gpt2cached_len1024_rollout_ddp_forward_return_base_diag_4gpu_20260514_002607",
+  "batch_size": 32,
+  "grad_accum": 4,
+  "effective_batch_size": 512,
+  "global_batch_size": 512,
+  "lr_schedule": "constant_warmup",
+  "optimizer": "muon",
+  "warmup_steps": 2000,
+  "min_lr": 0.0,
+  "weight_decay": 0.0,
+  "adamw_param_groups": "nanogpt",
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+  "muon_ns_steps": 5,
+  "muon_update_scale": 1.0,
+  "ema_decay": 0.0,
+  "ema_start_step": 0,
+  "model_type": "ddit",
+  "dual_t": true,
+  "corrupt_t_mode": "same",
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+  "corrupt_max_t": 1.0,
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+  "mask_ratio_floor_schedule": "none",
+  "dirichlet_endpoint_mode": "categorical_dual_t",
+  "dirichlet_semantic_t_mode": "same",
+  "dirichlet_semantic_t_value": 0.0,
+  "dirichlet_semantic_t_curve": "linear",
+  "dirichlet_semantic_t_power": 1.0,
+  "endpoint_sequence_random_prob_alpha": 0.0,
+  "categorical_wrong_from_full_vocab": true,
+  "categorical_wrong_from_batch_valid_tokens": false,
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+  "mask_mixture_lowk_prob": 0.0,
+  "mask_mixture_lowcorrupt_prob": 0.0,
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+  "mask_mixture_all_prob": 0.0,
+  "mask_mixture_lowk_clean_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_lowcorrupt_tokens": "1,2,4,8,16,32,64",
+  "mask_mixture_block_tokens": "64,128",
+  "simplex_bridge_sampler": "dirichlet",
+  "logistic_normal_sigma_min": 0.18,
+  "logistic_normal_sigma_max": 2.2,
+  "logistic_normal_tau_min": 0.65,
+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
+  "target_loss": "hard_ce",
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+  "rollout_train_samplewise": true,
+  "rollout_train_compute_always": false,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "allow_tf32": true,
+  "activation_checkpointing": false,
+  "activation_checkpoint_interval": 1,
+  "activation_checkpoint_scope": "block",
+  "ddp_static_graph": false,
+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
+  "dataloader_prefetch_factor": 4,
+  "full_train_stats": false,
+  "record_pad_truncate": false,
+  "record_add_eos": false,
+  "record_add_special_tokens": false,
+  "record_pad_token": "pad",
+  "record_shuffle_buffer": 10000,
+  "wrap": true,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 0,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
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+}
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+step=40 micro_steps=160 elapsed=83.6s lr=4.100000e-05 loss=10.8125 loss_recon=10.8125 loss_meanflow=0.0000 mean_model_t=0.4828 mean_corrupt_t=0.4828 mean_loss_t_weight=1.0000 prior_center_loss_beta=0.0000 rollout_train_applied=0.5297 acc_all=0.0008 acc_corrupt=0.0010 corrupt_frac=0.5686 loss_all=10.8125 loss_corrupt=10.8125 acc_corrupt_t_0p0_0p2=0.0012 corrupt_frac_t_0p0_0p2=0.2223 acc_corrupt_t_0p2_0p4=0.0000 corrupt_frac_t_0p2_0p4=0.2560 acc_corrupt_t_0p4_0p6=0.0028 corrupt_frac_t_0p4_0p6=0.1561 acc_corrupt_t_0p6_0p8=0.0006 corrupt_frac_t_0p6_0p8=0.1894 acc_corrupt_t_0p8_1p0=0.0009 corrupt_frac_t_0p8_1p0=0.1763 wrong_frac=0.5301 init_acc_corrupt=0.4423 init_gold_top10=0.4647 init_gold_top100=0.5085 rollout_applied_pos_frac=0.6069 init_acc_rollout_applied=0.4848 init_acc_rollout_kept=0.3766 logit_acc_rollout_applied=0.0008 logit_acc_rollout_kept=0.0012

LTA_openwebtext_dualt/logs/selfcond_4gpu/lta_owt_gpt2cached_len1024_selfcond_p05_rollout1_samplewise_c1024_ddit768x12_muon_ema_gbs512_4gpu_50k_20260513_233654.log

@@ -0,0 +1,520 @@
+NCCL version 2.25.1+cuda12.8
+{
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+  "rank": 0,
+  "world_size": 4,
+  "samples": "owt_cached_chunks:8734897",
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+  "tokenizer_vocab_size": 50257,
+  "save_dir": "runs/lta_owt_gpt2cached_len1024_selfcond_p05_rollout1_samplewise_c1024_ddit768x12_muon_ema_gbs512_4gpu_50k_20260513_233654",
+  "batch_size": 32,
+  "grad_accum": 4,
+  "effective_batch_size": 512,
+  "global_batch_size": 512,
+  "lr_schedule": "constant_warmup",
+  "optimizer": "muon",
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+  "muon_update_scale": 1.0,
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+  "model_type": "ddit",
+  "dual_t": true,
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+  "prefix_block_prob": 0.0,
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+  "dirichlet_endpoint_mode": "categorical_dual_t",
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+  "mask_mixture_block_tokens": "64,128",
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+  "logistic_normal_tau_min": 0.65,
+  "logistic_normal_tau_max": 1.15,
+  "torch_compile": false,
+  "compile_mode": "max-autotune",
+  "state_format": "prob",
+  "target_loss": "hard_ce",
+  "meanflow_weight": 0.0,
+  "rollout_train_prob": 0.5,
+  "rollout_train_steps": 1,
+  "rollout_train_infer_steps": 64,
+  "rollout_train_temp": 1.45,
+  "rollout_train_max_gamma": 1.0,
+  "rollout_train_corrupt_only": true,
+  "rollout_train_samplewise": true,
+  "rollout_train_compute_always": false,
+  "bridge_noise_init": "logistic_normal",
+  "noise_sigma": -1.0,
+  "allow_tf32": true,
+  "activation_checkpointing": true,
+  "activation_checkpoint_interval": 2,
+  "activation_checkpoint_scope": "block",
+  "ddp_static_graph": false,
+  "ddp_gradient_as_bucket_view": true,
+  "blocking_data_transfer": false,
+  "dataloader_prefetch_factor": 4,
+  "full_train_stats": false,
+  "record_pad_truncate": false,
+  "record_add_eos": false,
+  "record_add_special_tokens": false,
+  "record_pad_token": "pad",
+  "record_shuffle_buffer": 10000,
+  "wrap": true,
+  "wrap_mode": "stream",
+  "wrap_record_buffer_size": 200,
+  "owt_cached_chunks": true,
+  "owt_chunk_cache_dir": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext_lta_cached_chunks/gpt2_len1024_train_minus_100k",
+  "owt_chunk_cache_rebuild": false,
+  "owt_chunk_cache_write_batch": 4096,
+  "owt_exact_repeat_per_chunk": 0,
+  "online_chunk_shuffle": false,
+  "online_chunk_shuffle_buffer": 10000,
+  "openwebtext_split": "train_minus_100k",
+  "detokenizer": "auto",
+  "resolved_detokenizer": null,
+  "num_workers": 4,
+  "latest_every": 500,
+  "resume_path": ""
+}
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1591, in <module>
+[rank0]:     main()
+[rank0]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1510, in main
+[rank0]:     loss.backward()
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/_tensor.py", line 648, in backward
+[rank0]:     torch.autograd.backward(
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/__init__.py", line 347, in backward
+[rank0]:     _engine_run_backward(
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py", line 823, in _engine_run_backward
+[rank0]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1128, in unpack_hook
+[rank0]:     frame.check_recomputed_tensors_match(gid)
+[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 902, in check_recomputed_tensors_match
+[rank0]:     raise CheckpointError(
+[rank0]: torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: Recomputed values for the following tensors have different metadata than during the forward pass.
+[rank0]: tensor at position 0:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 128]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 1:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 3:
+[rank0]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 4:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 5:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 6:
+[rank0]: saved metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 7:
+[rank0]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 8:
+[rank0]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 9:
+[rank0]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 11:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 12:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 13:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 15:
+[rank0]: saved metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 16:
+[rank0]: saved metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 17:
+[rank0]: saved metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 18:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 19:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 20:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 21:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 22:
+[rank0]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 23:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 24:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 25:
+[rank0]: saved metadata: {'shape': torch.Size([768, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 26:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1024, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 27:
+[rank0]: saved metadata: {'shape': torch.Size([3072, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=0)}
+[rank0]: tensor at position 28:
+[rank0]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+[rank0]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=0)}
+
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1591, in <module>
+[rank3]:     main()
+[rank3]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1510, in main
+[rank3]:     loss.backward()
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/_tensor.py", line 648, in backward
+[rank3]:     torch.autograd.backward(
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/__init__.py", line 347, in backward
+[rank3]:     _engine_run_backward(
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py", line 823, in _engine_run_backward
+[rank3]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+[rank3]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1128, in unpack_hook
+[rank3]:     frame.check_recomputed_tensors_match(gid)
+[rank3]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 902, in check_recomputed_tensors_match
+[rank3]:     raise CheckpointError(
+[rank3]: torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: Recomputed values for the following tensors have different metadata than during the forward pass.
+[rank3]: tensor at position 0:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 128]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 1:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 3:
+[rank3]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 4:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 5:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 6:
+[rank3]: saved metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 7:
+[rank3]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 8:
+[rank3]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 9:
+[rank3]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 11:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 12:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 13:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 15:
+[rank3]: saved metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 16:
+[rank3]: saved metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 17:
+[rank3]: saved metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 18:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 19:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 20:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 21:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 22:
+[rank3]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 23:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 24:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 25:
+[rank3]: saved metadata: {'shape': torch.Size([768, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 26:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1024, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 27:
+[rank3]: saved metadata: {'shape': torch.Size([3072, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=3)}
+[rank3]: tensor at position 28:
+[rank3]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+[rank3]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=3)}
+
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1591, in <module>
+[rank1]:     main()
+[rank1]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1510, in main
+[rank1]:     loss.backward()
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/_tensor.py", line 648, in backward
+[rank1]:     torch.autograd.backward(
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/__init__.py", line 347, in backward
+[rank1]:     _engine_run_backward(
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py", line 823, in _engine_run_backward
+[rank1]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+[rank1]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1128, in unpack_hook
+[rank1]:     frame.check_recomputed_tensors_match(gid)
+[rank1]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 902, in check_recomputed_tensors_match
+[rank1]:     raise CheckpointError(
+[rank1]: torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: Recomputed values for the following tensors have different metadata than during the forward pass.
+[rank1]: tensor at position 0:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 128]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 1:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 3:
+[rank1]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 4:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 5:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 6:
+[rank1]: saved metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 7:
+[rank1]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 8:
+[rank1]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 9:
+[rank1]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 11:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 12:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 13:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 15:
+[rank1]: saved metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 16:
+[rank1]: saved metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 17:
+[rank1]: saved metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 18:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 19:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 20:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 21:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 22:
+[rank1]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 23:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 24:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 25:
+[rank1]: saved metadata: {'shape': torch.Size([768, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 26:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1024, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 27:
+[rank1]: saved metadata: {'shape': torch.Size([3072, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=1)}
+[rank1]: tensor at position 28:
+[rank1]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+[rank1]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=1)}
+
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1591, in <module>
+[rank2]:     main()
+[rank2]:   File "/e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/train.py", line 1510, in main
+[rank2]:     loss.backward()
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/_tensor.py", line 648, in backward
+[rank2]:     torch.autograd.backward(
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/__init__.py", line 347, in backward
+[rank2]:     _engine_run_backward(
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py", line 823, in _engine_run_backward
+[rank2]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+[rank2]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1128, in unpack_hook
+[rank2]:     frame.check_recomputed_tensors_match(gid)
+[rank2]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 902, in check_recomputed_tensors_match
+[rank2]:     raise CheckpointError(
+[rank2]: torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: Recomputed values for the following tensors have different metadata than during the forward pass.
+[rank2]: tensor at position 0:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 128]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 1:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 3:
+[rank2]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 4:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 5:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 6:
+[rank2]: saved metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 7:
+[rank2]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 8:
+[rank2]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([768, 2304]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 9:
+[rank2]: saved metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 11:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 12:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 13:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([1, 1, 1024, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 15:
+[rank2]: saved metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 16:
+[rank2]: saved metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 17:
+[rank2]: saved metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 12, 64]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 18:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 12, 1024]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 19:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([2]), 'dtype': torch.int64, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 20:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 21:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32768, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 22:
+[rank2]: saved metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 23:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 24:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 25:
+[rank2]: saved metadata: {'shape': torch.Size([768, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 26:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1024, 3072]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 1]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 27:
+[rank2]: saved metadata: {'shape': torch.Size([3072, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([1, 1, 768]), 'dtype': torch.float32, 'device': device(type='cuda', index=2)}
+[rank2]: tensor at position 28:
+[rank2]: saved metadata: {'shape': torch.Size([32, 1, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+[rank2]: recomputed metadata: {'shape': torch.Size([32, 1024, 768]), 'dtype': torch.bfloat16, 'device': device(type='cuda', index=2)}
+
+W0513 23:37:21.171000 944815 torch/distributed/elastic/agent/server/api.py:719] Received 15 death signal, shutting down workers
+W0513 23:37:21.173000 944815 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 944821 closing signal SIGTERM
+W0513 23:37:21.173000 944815 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 944822 closing signal SIGTERM
+W0513 23:37:21.173000 944815 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 944823 closing signal SIGTERM
+W0513 23:37:21.174000 944815 torch/distributed/elastic/multiprocessing/api.py:898] Sending process 944824 closing signal SIGTERM
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 922, in <module>
+    main()
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper
+    return f(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 918, in main
+    run(args)
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/run.py", line 909, in run
+    elastic_launch(
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/launcher/api.py", line 261, in launch_agent
+    result = agent.run()
+             ^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/metrics/api.py", line 137, in wrapper
+    result = f(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/agent/server/api.py", line 711, in run
+    result = self._invoke_run(role)
+             ^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/agent/server/api.py", line 870, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/usr/local/lib/python3.12/dist-packages/torch/distributed/elastic/multiprocessing/api.py", line 84, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 944815 got signal: 15

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/huggingface_hub-1.16.4.dist-info/INSTALLER

@@ -0,0 +1 @@
+uv

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/huggingface_hub-1.16.4.dist-info/METADATA

@@ -0,0 +1,329 @@
+Metadata-Version: 2.4
+Name: huggingface_hub
+Version: 1.16.4
+Summary: Client library to download and publish models, datasets and other repos on the huggingface.co hub
+Home-page: https://github.com/huggingface/huggingface_hub
+Author: Hugging Face, Inc.
+Author-email: julien@huggingface.co
+License: Apache-2.0
+Keywords: model-hub machine-learning models natural-language-processing deep-learning pytorch pretrained-models
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.10.0
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: click>=8.4.0
+Requires-Dist: filelock>=3.10.0
+Requires-Dist: fsspec>=2023.5.0
+Requires-Dist: hf-xet<2.0.0,>=1.4.3; platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "arm64" or platform_machine == "aarch64"
+Requires-Dist: httpx<1,>=0.23.0
+Requires-Dist: packaging>=20.9
+Requires-Dist: pyyaml>=5.1
+Requires-Dist: tqdm>=4.42.1
+Requires-Dist: typer<0.26.0,>=0.20.0
+Requires-Dist: typing-extensions>=4.1.0
+Provides-Extra: oauth
+Requires-Dist: authlib>=1.3.2; extra == "oauth"
+Requires-Dist: fastapi; extra == "oauth"
+Requires-Dist: httpx; extra == "oauth"
+Requires-Dist: itsdangerous; extra == "oauth"
+Provides-Extra: torch
+Requires-Dist: torch; extra == "torch"
+Requires-Dist: safetensors[torch]; extra == "torch"
+Provides-Extra: fastai
+Requires-Dist: toml; extra == "fastai"
+Requires-Dist: fastai>=2.4; extra == "fastai"
+Requires-Dist: fastcore>=1.3.27; extra == "fastai"
+Provides-Extra: hf-xet
+Requires-Dist: hf-xet<2.0.0,>=1.4.3; extra == "hf-xet"
+Provides-Extra: mcp
+Requires-Dist: mcp>=1.8.0; extra == "mcp"
+Provides-Extra: testing
+Requires-Dist: authlib>=1.3.2; extra == "testing"
+Requires-Dist: fastapi; extra == "testing"
+Requires-Dist: httpx; extra == "testing"
+Requires-Dist: itsdangerous; extra == "testing"
+Requires-Dist: jedi; extra == "testing"
+Requires-Dist: Jinja2; extra == "testing"
+Requires-Dist: pytest>=8.4.2; extra == "testing"
+Requires-Dist: pytest-cov; extra == "testing"
+Requires-Dist: pytest-env; extra == "testing"
+Requires-Dist: pytest-xdist; extra == "testing"
+Requires-Dist: pytest-vcr; extra == "testing"
+Requires-Dist: pytest-asyncio; extra == "testing"
+Requires-Dist: pytest-rerunfailures<16.0; extra == "testing"
+Requires-Dist: pytest-mock; extra == "testing"
+Requires-Dist: urllib3<2.0; extra == "testing"
+Requires-Dist: soundfile; extra == "testing"
+Requires-Dist: Pillow; extra == "testing"
+Requires-Dist: numpy; extra == "testing"
+Requires-Dist: duckdb; extra == "testing"
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+Requires-Dist: types-urllib3; extra == "dev"
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub-dark.svg">
+    <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg">
+    <img alt="huggingface_hub library logo" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg" width="352" height="59" style="max-width: 100%">
+  </picture>
+  <br/>
+  <br/>
+</p> 
+
+<p align="center">
+    <i>The official Python client for the Huggingface Hub.</i>
+</p>
+
+<p align="center">
+    <a href="https://huggingface.co/docs/huggingface_hub/en/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/huggingface_hub/index.svg?down_color=red&down_message=offline&up_message=online&label=doc"></a>
+    <a href="https://github.com/huggingface/huggingface_hub/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/huggingface_hub.svg"></a>
+    <a href="https://github.com/huggingface/huggingface_hub"><img alt="PyPi version" src="https://img.shields.io/pypi/pyversions/huggingface_hub.svg"></a>
+    <a href="https://pypi.org/project/huggingface-hub"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/huggingface_hub"></a>
+    <a href="https://codecov.io/gh/huggingface/huggingface_hub"><img alt="Code coverage" src="https://codecov.io/gh/huggingface/huggingface_hub/branch/main/graph/badge.svg?token=RXP95LE2XL"></a>
+</p>
+
+<h4 align="center">
+    <p>
+        <b>English</b> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_de.md">Deutsch</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_fr.md">Français</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_hi.md">हिंदी</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_ko.md">한국어</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_cn.md">中文 (简体)</a>
+    <p>
+</h4>
+
+---
+
+**Documentation**: <a href="https://hf.co/docs/huggingface_hub" target="_blank">https://hf.co/docs/huggingface_hub</a>
+
+**Source Code**: <a href="https://github.com/huggingface/huggingface_hub" target="_blank">https://github.com/huggingface/huggingface_hub</a>
+
+---
+
+## Welcome to the huggingface_hub library
+
+The `huggingface_hub` library allows you to interact with the [Hugging Face Hub](https://huggingface.co/), a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The `huggingface_hub` library provides a simple way to do all these things with Python.
+
+## Key features
+
+- [Download files](https://huggingface.co/docs/huggingface_hub/en/guides/download) from the Hub.
+- [Upload files](https://huggingface.co/docs/huggingface_hub/en/guides/upload) to the Hub.
+- [Manage your repositories](https://huggingface.co/docs/huggingface_hub/en/guides/repository).
+- [Run Inference](https://huggingface.co/docs/huggingface_hub/en/guides/inference) on deployed models.
+- [Search](https://huggingface.co/docs/huggingface_hub/en/guides/search) for models, datasets and Spaces.
+- [Share Model Cards](https://huggingface.co/docs/huggingface_hub/en/guides/model-cards) to document your models.
+- [Engage with the community](https://huggingface.co/docs/huggingface_hub/en/guides/community) through PRs and comments.
+
+## Installation
+
+Install the `huggingface_hub` package with [pip](https://pypi.org/project/huggingface-hub/):
+
+```bash
+pip install huggingface_hub
+```
+
+We recommend using [`uv`](https://docs.astral.sh/uv/) for a fast and reliable install:
+
+```bash
+uv pip install huggingface_hub
+```
+
+In order to keep the package minimal by default, `huggingface_hub` comes with optional dependencies useful for some use cases. For example, if you want to use the MCP module, run:
+
+```bash
+pip install "huggingface_hub[mcp]"
+```
+
+To learn more about installation and optional dependencies, check out the [installation guide](https://huggingface.co/docs/huggingface_hub/en/installation).
+
+## Quick start
+
+### Download files
+
+Download a single file
+
+```py
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="tiiuae/falcon-7b-instruct", filename="config.json")
+```
+
+Or an entire repository
+
+```py
+from huggingface_hub import snapshot_download
+
+snapshot_download("stabilityai/stable-diffusion-2-1")
+```
+
+Files will be downloaded in a local cache folder. More details in [this guide](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache).
+
+### Login
+
+The Hugging Face Hub uses tokens to authenticate applications (see [docs](https://huggingface.co/docs/hub/security-tokens)). To log in your machine, run the following CLI:
+
+```bash
+hf auth login
+# or using an environment variable
+hf auth login --token $HUGGINGFACE_TOKEN
+```
+
+### Create a repository
+
+```py
+from huggingface_hub import create_repo
+
+create_repo(repo_id="super-cool-model")
+```
+
+### Upload files
+
+Upload a single file
+
+```py
+from huggingface_hub import upload_file
+
+upload_file(
+    path_or_fileobj="/home/lysandre/dummy-test/README.md",
+    path_in_repo="README.md",
+    repo_id="lysandre/test-model",
+)
+```
+
+Or an entire folder
+
+```py
+from huggingface_hub import upload_folder
+
+upload_folder(
+    folder_path="/path/to/local/space",
+    repo_id="username/my-cool-space",
+    repo_type="space",
+)
+```
+
+For details in the [upload guide](https://huggingface.co/docs/huggingface_hub/en/guides/upload).
+
+## Integrating to the Hub.
+
+We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations [here](https://huggingface.co/docs/hub/libraries).
+
+The advantages are:
+
+- Free model or dataset hosting for libraries and their users.
+- Built-in file versioning, even with very large files, thanks to a git-based approach.
+- In-browser widgets to play with the uploaded models.
+- Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
+- Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
+- Usage stats and more features to come.
+
+If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a [step-by-step guide](https://huggingface.co/docs/hub/adding-a-library) with ❤️ showing how to do this integration.
+
+## Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community.
+Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.
+We wrote a [contribution guide](https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md) to summarize
+how to get started to contribute to this repository.

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LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/huggingface_hub-1.16.4.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (82.0.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/huggingface_hub-1.16.4.dist-info/entry_points.txt

@@ -0,0 +1,7 @@
+[console_scripts]
+hf = huggingface_hub.cli.hf:main
+huggingface-cli = huggingface_hub.cli.deprecated_cli:main
+tiny-agents = huggingface_hub.inference._mcp.cli:app
+
+[fsspec.specs]
+hf = huggingface_hub.HfFileSystem

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/huggingface_hub-1.16.4.dist-info/top_level.txt

@@ -0,0 +1 @@
+huggingface_hub

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/idna/compat.py

@@ -0,0 +1,41 @@
+from typing import Any, Union
+
+from .core import decode, encode
+
+
+def ToASCII(label: str) -> bytes:
+    """Compatibility shim for :rfc:`3490` ``ToASCII``.
+
+    Delegates to :func:`idna.encode` (IDNA 2008). Provided to ease porting
+    of code written against the legacy :mod:`encodings.idna` API; new code
+    should call :func:`idna.encode` directly.
+
+    :param label: The label or domain to encode.
+    :returns: The encoded form as ASCII :class:`bytes`.
+    """
+    return encode(label)
+
+
+def ToUnicode(label: Union[bytes, bytearray]) -> str:
+    """Compatibility shim for :rfc:`3490` ``ToUnicode``.
+
+    Delegates to :func:`idna.decode` (IDNA 2008). Provided to ease porting
+    of code written against the legacy :mod:`encodings.idna` API; new code
+    should call :func:`idna.decode` directly.
+
+    :param label: The label or domain to decode.
+    :returns: The decoded Unicode form.
+    """
+    return decode(label)
+
+
+def nameprep(s: Any) -> None:
+    """Stub for :rfc:`3491` Nameprep, which is not used by IDNA 2008.
+
+    IDNA 2008 (:rfc:`5891`) replaces Nameprep with the per-codepoint
+    validity classes from :rfc:`5892`; this function exists only to
+    return a clear error if legacy code attempts to call it.
+
+    :raises NotImplementedError: Always.
+    """
+    raise NotImplementedError("IDNA 2008 does not utilise nameprep protocol")

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/idna/idnadata.py

@@ -0,0 +1,4366 @@
+# This file is automatically generated by tools/idna-data
+
+from functools import cache
+
+__version__ = "17.0.0"
+
+scripts = {
+    "Greek": (
+        0x37000000374,
+        0x37500000378,
+        0x37A0000037E,
+        0x37F00000380,
+        0x38400000385,
+        0x38600000387,
+        0x3880000038B,
+        0x38C0000038D,
+        0x38E000003A2,
+        0x3A3000003E2,
+        0x3F000000400,
+        0x1D2600001D2B,
+        0x1D5D00001D62,
+        0x1D6600001D6B,
+        0x1DBF00001DC0,
+        0x1F0000001F16,
+        0x1F1800001F1E,
+        0x1F2000001F46,
+        0x1F4800001F4E,
+        0x1F5000001F58,
+        0x1F5900001F5A,
+        0x1F5B00001F5C,
+        0x1F5D00001F5E,
+        0x1F5F00001F7E,
+        0x1F8000001FB5,
+        0x1FB600001FC5,
+        0x1FC600001FD4,
+        0x1FD600001FDC,
+        0x1FDD00001FF0,
+        0x1FF200001FF5,
+        0x1FF600001FFF,
+        0x212600002127,
+        0xAB650000AB66,
+        0x101400001018F,
+        0x101A0000101A1,
+        0x1D2000001D246,
+    ),
+    "Han": (
+        0x2E8000002E9A,
+        0x2E9B00002EF4,
+        0x2F0000002FD6,
+        0x300500003006,
+        0x300700003008,
+        0x30210000302A,
+        0x30380000303C,
+        0x340000004DC0,
+        0x4E000000A000,
+        0xF9000000FA6E,
+        0xFA700000FADA,
+        0x16FE200016FE4,
+        0x16FF000016FF7,
+        0x200000002A6E0,
+        0x2A7000002B81E,
+        0x2B8200002CEAE,
+        0x2CEB00002EBE1,
+        0x2EBF00002EE5E,
+        0x2F8000002FA1E,
+        0x300000003134B,
+        0x313500003347A,
+    ),
+    "Hebrew": (
+        0x591000005C8,
+        0x5D0000005EB,
+        0x5EF000005F5,
+        0xFB1D0000FB37,
+        0xFB380000FB3D,
+        0xFB3E0000FB3F,
+        0xFB400000FB42,
+        0xFB430000FB45,
+        0xFB460000FB50,
+    ),
+    "Hiragana": (
+        0x304100003097,
+        0x309D000030A0,
+        0x1B0010001B120,
+        0x1B1320001B133,
+        0x1B1500001B153,
+        0x1F2000001F201,
+    ),
+    "Katakana": (
+        0x30A1000030FB,
+        0x30FD00003100,
+        0x31F000003200,
+        0x32D0000032FF,
+        0x330000003358,
+        0xFF660000FF70,
+        0xFF710000FF9E,
+        0x1AFF00001AFF4,
+        0x1AFF50001AFFC,
+        0x1AFFD0001AFFF,
+        0x1B0000001B001,
+        0x1B1200001B123,
+        0x1B1550001B156,
+        0x1B1640001B168,
+    ),
+}
+
+
+@cache
+def joining_types() -> dict[int, int]:
+    return {
+        0xAD: 84,
+        0x300: 84,
+        0x301: 84,
+        0x302: 84,
+        0x303: 84,
+        0x304: 84,
+        0x305: 84,
+        0x306: 84,
+        0x307: 84,
+        0x308: 84,
+        0x309: 84,
+        0x30A: 84,
+        0x30B: 84,
+        0x30C: 84,
+        0x30D: 84,
+        0x30E: 84,
+        0x30F: 84,
+        0x310: 84,
+        0x311: 84,
+        0x312: 84,
+        0x313: 84,
+        0x314: 84,
+        0x315: 84,
+        0x316: 84,
+        0x317: 84,
+        0x318: 84,
+        0x319: 84,
+        0x31A: 84,
+        0x31B: 84,
+        0x31C: 84,
+        0x31D: 84,
+        0x31E: 84,
+        0x31F: 84,
+        0x320: 84,
+        0x321: 84,
+        0x322: 84,
+        0x323: 84,
+        0x324: 84,
+        0x325: 84,
+        0x326: 84,
+        0x327: 84,
+        0x328: 84,
+        0x329: 84,
+        0x32A: 84,
+        0x32B: 84,
+        0x32C: 84,
+        0x32D: 84,
+        0x32E: 84,
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+        0x330: 84,
+        0x331: 84,
+        0x332: 84,
+        0x333: 84,
+        0x334: 84,
+        0x335: 84,
+        0x336: 84,
+        0x337: 84,
+        0x338: 84,
+        0x339: 84,
+        0x33A: 84,
+        0x33B: 84,
+        0x33C: 84,
+        0x33D: 84,
+        0x33E: 84,
+        0x33F: 84,
+        0x340: 84,
+        0x341: 84,
+        0x342: 84,
+        0x343: 84,
+        0x344: 84,
+        0x345: 84,
+        0x346: 84,
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+        0x348: 84,
+        0x349: 84,
+        0x34A: 84,
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+        0x34C: 84,
+        0x34D: 84,
+        0x34E: 84,
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+        0x350: 84,
+        0x351: 84,
+        0x352: 84,
+        0x353: 84,
+        0x354: 84,
+        0x355: 84,
+        0x356: 84,
+        0x357: 84,
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+        0x485: 84,
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+        0x595: 84,
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+        0x5A3: 84,
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+        0x620: 68,
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+        0xE0071: 84,
+        0xE0072: 84,
+        0xE0073: 84,
+        0xE0074: 84,
+        0xE0075: 84,
+        0xE0076: 84,
+        0xE0077: 84,
+        0xE0078: 84,
+        0xE0079: 84,
+        0xE007A: 84,
+        0xE007B: 84,
+        0xE007C: 84,
+        0xE007D: 84,
+        0xE007E: 84,
+        0xE007F: 84,
+        0xE0100: 84,
+        0xE0101: 84,
+        0xE0102: 84,
+        0xE0103: 84,
+        0xE0104: 84,
+        0xE0105: 84,
+        0xE0106: 84,
+        0xE0107: 84,
+        0xE0108: 84,
+        0xE0109: 84,
+        0xE010A: 84,
+        0xE010B: 84,
+        0xE010C: 84,
+        0xE010D: 84,
+        0xE010E: 84,
+        0xE010F: 84,
+        0xE0110: 84,
+        0xE0111: 84,
+        0xE0112: 84,
+        0xE0113: 84,
+        0xE0114: 84,
+        0xE0115: 84,
+        0xE0116: 84,
+        0xE0117: 84,
+        0xE0118: 84,
+        0xE0119: 84,
+        0xE011A: 84,
+        0xE011B: 84,
+        0xE011C: 84,
+        0xE011D: 84,
+        0xE011E: 84,
+        0xE011F: 84,
+        0xE0120: 84,
+        0xE0121: 84,
+        0xE0122: 84,
+        0xE0123: 84,
+        0xE0124: 84,
+        0xE0125: 84,
+        0xE0126: 84,
+        0xE0127: 84,
+        0xE0128: 84,
+        0xE0129: 84,
+        0xE012A: 84,
+        0xE012B: 84,
+        0xE012C: 84,
+        0xE012D: 84,
+        0xE012E: 84,
+        0xE012F: 84,
+        0xE0130: 84,
+        0xE0131: 84,
+        0xE0132: 84,
+        0xE0133: 84,
+        0xE0134: 84,
+        0xE0135: 84,
+        0xE0136: 84,
+        0xE0137: 84,
+        0xE0138: 84,
+        0xE0139: 84,
+        0xE013A: 84,
+        0xE013B: 84,
+        0xE013C: 84,
+        0xE013D: 84,
+        0xE013E: 84,
+        0xE013F: 84,
+        0xE0140: 84,
+        0xE0141: 84,
+        0xE0142: 84,
+        0xE0143: 84,
+        0xE0144: 84,
+        0xE0145: 84,
+        0xE0146: 84,
+        0xE0147: 84,
+        0xE0148: 84,
+        0xE0149: 84,
+        0xE014A: 84,
+        0xE014B: 84,
+        0xE014C: 84,
+        0xE014D: 84,
+        0xE014E: 84,
+        0xE014F: 84,
+        0xE0150: 84,
+        0xE0151: 84,
+        0xE0152: 84,
+        0xE0153: 84,
+        0xE0154: 84,
+        0xE0155: 84,
+        0xE0156: 84,
+        0xE0157: 84,
+        0xE0158: 84,
+        0xE0159: 84,
+        0xE015A: 84,
+        0xE015B: 84,
+        0xE015C: 84,
+        0xE015D: 84,
+        0xE015E: 84,
+        0xE015F: 84,
+        0xE0160: 84,
+        0xE0161: 84,
+        0xE0162: 84,
+        0xE0163: 84,
+        0xE0164: 84,
+        0xE0165: 84,
+        0xE0166: 84,
+        0xE0167: 84,
+        0xE0168: 84,
+        0xE0169: 84,
+        0xE016A: 84,
+        0xE016B: 84,
+        0xE016C: 84,
+        0xE016D: 84,
+        0xE016E: 84,
+        0xE016F: 84,
+        0xE0170: 84,
+        0xE0171: 84,
+        0xE0172: 84,
+        0xE0173: 84,
+        0xE0174: 84,
+        0xE0175: 84,
+        0xE0176: 84,
+        0xE0177: 84,
+        0xE0178: 84,
+        0xE0179: 84,
+        0xE017A: 84,
+        0xE017B: 84,
+        0xE017C: 84,
+        0xE017D: 84,
+        0xE017E: 84,
+        0xE017F: 84,
+        0xE0180: 84,
+        0xE0181: 84,
+        0xE0182: 84,
+        0xE0183: 84,
+        0xE0184: 84,
+        0xE0185: 84,
+        0xE0186: 84,
+        0xE0187: 84,
+        0xE0188: 84,
+        0xE0189: 84,
+        0xE018A: 84,
+        0xE018B: 84,
+        0xE018C: 84,
+        0xE018D: 84,
+        0xE018E: 84,
+        0xE018F: 84,
+        0xE0190: 84,
+        0xE0191: 84,
+        0xE0192: 84,
+        0xE0193: 84,
+        0xE0194: 84,
+        0xE0195: 84,
+        0xE0196: 84,
+        0xE0197: 84,
+        0xE0198: 84,
+        0xE0199: 84,
+        0xE019A: 84,
+        0xE019B: 84,
+        0xE019C: 84,
+        0xE019D: 84,
+        0xE019E: 84,
+        0xE019F: 84,
+        0xE01A0: 84,
+        0xE01A1: 84,
+        0xE01A2: 84,
+        0xE01A3: 84,
+        0xE01A4: 84,
+        0xE01A5: 84,
+        0xE01A6: 84,
+        0xE01A7: 84,
+        0xE01A8: 84,
+        0xE01A9: 84,
+        0xE01AA: 84,
+        0xE01AB: 84,
+        0xE01AC: 84,
+        0xE01AD: 84,
+        0xE01AE: 84,
+        0xE01AF: 84,
+        0xE01B0: 84,
+        0xE01B1: 84,
+        0xE01B2: 84,
+        0xE01B3: 84,
+        0xE01B4: 84,
+        0xE01B5: 84,
+        0xE01B6: 84,
+        0xE01B7: 84,
+        0xE01B8: 84,
+        0xE01B9: 84,
+        0xE01BA: 84,
+        0xE01BB: 84,
+        0xE01BC: 84,
+        0xE01BD: 84,
+        0xE01BE: 84,
+        0xE01BF: 84,
+        0xE01C0: 84,
+        0xE01C1: 84,
+        0xE01C2: 84,
+        0xE01C3: 84,
+        0xE01C4: 84,
+        0xE01C5: 84,
+        0xE01C6: 84,
+        0xE01C7: 84,
+        0xE01C8: 84,
+        0xE01C9: 84,
+        0xE01CA: 84,
+        0xE01CB: 84,
+        0xE01CC: 84,
+        0xE01CD: 84,
+        0xE01CE: 84,
+        0xE01CF: 84,
+        0xE01D0: 84,
+        0xE01D1: 84,
+        0xE01D2: 84,
+        0xE01D3: 84,
+        0xE01D4: 84,
+        0xE01D5: 84,
+        0xE01D6: 84,
+        0xE01D7: 84,
+        0xE01D8: 84,
+        0xE01D9: 84,
+        0xE01DA: 84,
+        0xE01DB: 84,
+        0xE01DC: 84,
+        0xE01DD: 84,
+        0xE01DE: 84,
+        0xE01DF: 84,
+        0xE01E0: 84,
+        0xE01E1: 84,
+        0xE01E2: 84,
+        0xE01E3: 84,
+        0xE01E4: 84,
+        0xE01E5: 84,
+        0xE01E6: 84,
+        0xE01E7: 84,
+        0xE01E8: 84,
+        0xE01E9: 84,
+        0xE01EA: 84,
+        0xE01EB: 84,
+        0xE01EC: 84,
+        0xE01ED: 84,
+        0xE01EE: 84,
+        0xE01EF: 84,
+    }
+
+
+codepoint_classes = {
+    "PVALID": (
+        0x2D0000002E,
+        0x300000003A,
+        0x610000007B,
+        0xDF000000F7,
+        0xF800000100,
+        0x10100000102,
+        0x10300000104,
+        0x10500000106,
+        0x10700000108,
+        0x1090000010A,
+        0x10B0000010C,
+        0x10D0000010E,
+        0x10F00000110,
+        0x11100000112,
+        0x11300000114,
+        0x11500000116,
+        0x11700000118,
+        0x1190000011A,
+        0x11B0000011C,
+        0x11D0000011E,
+        0x11F00000120,
+        0x12100000122,
+        0x12300000124,
+        0x12500000126,
+        0x12700000128,
+        0x1290000012A,
+        0x12B0000012C,
+        0x12D0000012E,
+        0x12F00000130,
+        0x13100000132,
+        0x13500000136,
+        0x13700000139,
+        0x13A0000013B,
+        0x13C0000013D,
+        0x13E0000013F,
+        0x14200000143,
+        0x14400000145,
+        0x14600000147,
+        0x14800000149,
+        0x14B0000014C,
+        0x14D0000014E,
+        0x14F00000150,
+        0x15100000152,
+        0x15300000154,
+        0x15500000156,
+        0x15700000158,
+        0x1590000015A,
+        0x15B0000015C,
+        0x15D0000015E,
+        0x15F00000160,
+        0x16100000162,
+        0x16300000164,
+        0x16500000166,
+        0x16700000168,
+        0x1690000016A,
+        0x16B0000016C,
+        0x16D0000016E,
+        0x16F00000170,
+        0x17100000172,
+        0x17300000174,
+        0x17500000176,
+        0x17700000178,
+        0x17A0000017B,
+        0x17C0000017D,
+        0x17E0000017F,
+        0x18000000181,
+        0x18300000184,
+        0x18500000186,
+        0x18800000189,
+        0x18C0000018E,
+        0x19200000193,
+        0x19500000196,
+        0x1990000019C,
+        0x19E0000019F,
+        0x1A1000001A2,
+        0x1A3000001A4,
+        0x1A5000001A6,
+        0x1A8000001A9,
+        0x1AA000001AC,
+        0x1AD000001AE,
+        0x1B0000001B1,
+        0x1B4000001B5,
+        0x1B6000001B7,
+        0x1B9000001BC,
+        0x1BD000001C4,
+        0x1CE000001CF,
+        0x1D0000001D1,
+        0x1D2000001D3,
+        0x1D4000001D5,
+        0x1D6000001D7,
+        0x1D8000001D9,
+        0x1DA000001DB,
+        0x1DC000001DE,
+        0x1DF000001E0,
+        0x1E1000001E2,
+        0x1E3000001E4,
+        0x1E5000001E6,
+        0x1E7000001E8,
+        0x1E9000001EA,
+        0x1EB000001EC,
+        0x1ED000001EE,
+        0x1EF000001F1,
+        0x1F5000001F6,
+        0x1F9000001FA,
+        0x1FB000001FC,
+        0x1FD000001FE,
+        0x1FF00000200,
+        0x20100000202,
+        0x20300000204,
+        0x20500000206,
+        0x20700000208,
+        0x2090000020A,
+        0x20B0000020C,
+        0x20D0000020E,
+        0x20F00000210,
+        0x21100000212,
+        0x21300000214,
+        0x21500000216,
+        0x21700000218,
+        0x2190000021A,
+        0x21B0000021C,
+        0x21D0000021E,
+        0x21F00000220,
+        0x22100000222,
+        0x22300000224,
+        0x22500000226,
+        0x22700000228,
+        0x2290000022A,
+        0x22B0000022C,
+        0x22D0000022E,
+        0x22F00000230,
+        0x23100000232,
+        0x2330000023A,
+        0x23C0000023D,
+        0x23F00000241,
+        0x24200000243,
+        0x24700000248,
+        0x2490000024A,
+        0x24B0000024C,
+        0x24D0000024E,
+        0x24F000002B0,
+        0x2B9000002C2,
+        0x2C6000002D2,
+        0x2EC000002ED,
+        0x2EE000002EF,
+        0x30000000340,
+        0x34200000343,
+        0x3460000034F,
+        0x35000000370,
+        0x37100000372,
+        0x37300000374,
+        0x37700000378,
+        0x37B0000037E,
+        0x39000000391,
+        0x3AC000003CF,
+        0x3D7000003D8,
+        0x3D9000003DA,
+        0x3DB000003DC,
+        0x3DD000003DE,
+        0x3DF000003E0,
+        0x3E1000003E2,
+        0x3E3000003E4,
+        0x3E5000003E6,
+        0x3E7000003E8,
+        0x3E9000003EA,
+        0x3EB000003EC,
+        0x3ED000003EE,
+        0x3EF000003F0,
+        0x3F3000003F4,
+        0x3F8000003F9,
+        0x3FB000003FD,
+        0x43000000460,
+        0x46100000462,
+        0x46300000464,
+        0x46500000466,
+        0x46700000468,
+        0x4690000046A,
+        0x46B0000046C,
+        0x46D0000046E,
+        0x46F00000470,
+        0x47100000472,
+        0x47300000474,
+        0x47500000476,
+        0x47700000478,
+        0x4790000047A,
+        0x47B0000047C,
+        0x47D0000047E,
+        0x47F00000480,
+        0x48100000482,
+        0x48300000488,
+        0x48B0000048C,
+        0x48D0000048E,
+        0x48F00000490,
+        0x49100000492,
+        0x49300000494,
+        0x49500000496,
+        0x49700000498,
+        0x4990000049A,
+        0x49B0000049C,
+        0x49D0000049E,
+        0x49F000004A0,
+        0x4A1000004A2,
+        0x4A3000004A4,
+        0x4A5000004A6,
+        0x4A7000004A8,
+        0x4A9000004AA,
+        0x4AB000004AC,
+        0x4AD000004AE,
+        0x4AF000004B0,
+        0x4B1000004B2,
+        0x4B3000004B4,
+        0x4B5000004B6,
+        0x4B7000004B8,
+        0x4B9000004BA,
+        0x4BB000004BC,
+        0x4BD000004BE,
+        0x4BF000004C0,
+        0x4C2000004C3,
+        0x4C4000004C5,
+        0x4C6000004C7,
+        0x4C8000004C9,
+        0x4CA000004CB,
+        0x4CC000004CD,
+        0x4CE000004D0,
+        0x4D1000004D2,
+        0x4D3000004D4,
+        0x4D5000004D6,
+        0x4D7000004D8,
+        0x4D9000004DA,
+        0x4DB000004DC,
+        0x4DD000004DE,
+        0x4DF000004E0,
+        0x4E1000004E2,
+        0x4E3000004E4,
+        0x4E5000004E6,
+        0x4E7000004E8,
+        0x4E9000004EA,
+        0x4EB000004EC,
+        0x4ED000004EE,
+        0x4EF000004F0,
+        0x4F1000004F2,
+        0x4F3000004F4,
+        0x4F5000004F6,
+        0x4F7000004F8,
+        0x4F9000004FA,
+        0x4FB000004FC,
+        0x4FD000004FE,
+        0x4FF00000500,
+        0x50100000502,
+        0x50300000504,
+        0x50500000506,
+        0x50700000508,
+        0x5090000050A,
+        0x50B0000050C,
+        0x50D0000050E,
+        0x50F00000510,
+        0x51100000512,
+        0x51300000514,
+        0x51500000516,
+        0x51700000518,
+        0x5190000051A,
+        0x51B0000051C,
+        0x51D0000051E,
+        0x51F00000520,
+        0x52100000522,
+        0x52300000524,
+        0x52500000526,
+        0x52700000528,
+        0x5290000052A,
+        0x52B0000052C,
+        0x52D0000052E,
+        0x52F00000530,
+        0x5590000055A,
+        0x56000000587,
+        0x58800000589,
+        0x591000005BE,
+        0x5BF000005C0,
+        0x5C1000005C3,
+        0x5C4000005C6,
+        0x5C7000005C8,
+        0x5D0000005EB,
+        0x5EF000005F3,
+        0x6100000061B,
+        0x62000000640,
+        0x64100000660,
+        0x66E00000675,
+        0x679000006D4,
+        0x6D5000006DD,
+        0x6DF000006E9,
+        0x6EA000006F0,
+        0x6FA00000700,
+        0x7100000074B,
+        0x74D000007B2,
+        0x7C0000007F6,
+        0x7FD000007FE,
+        0x8000000082E,
+        0x8400000085C,
+        0x8600000086B,
+        0x87000000888,
+        0x88900000890,
+        0x897000008E2,
+        0x8E300000958,
+        0x96000000964,
+        0x96600000970,
+        0x97100000984,
+        0x9850000098D,
+        0x98F00000991,
+        0x993000009A9,
+        0x9AA000009B1,
+        0x9B2000009B3,
+        0x9B6000009BA,
+        0x9BC000009C5,
+        0x9C7000009C9,
+        0x9CB000009CF,
+        0x9D7000009D8,
+        0x9E0000009E4,
+        0x9E6000009F2,
+        0x9FC000009FD,
+        0x9FE000009FF,
+        0xA0100000A04,
+        0xA0500000A0B,
+        0xA0F00000A11,
+        0xA1300000A29,
+        0xA2A00000A31,
+        0xA3200000A33,
+        0xA3500000A36,
+        0xA3800000A3A,
+        0xA3C00000A3D,
+        0xA3E00000A43,
+        0xA4700000A49,
+        0xA4B00000A4E,
+        0xA5100000A52,
+        0xA5C00000A5D,
+        0xA6600000A76,
+        0xA8100000A84,
+        0xA8500000A8E,
+        0xA8F00000A92,
+        0xA9300000AA9,
+        0xAAA00000AB1,
+        0xAB200000AB4,
+        0xAB500000ABA,
+        0xABC00000AC6,
+        0xAC700000ACA,
+        0xACB00000ACE,
+        0xAD000000AD1,
+        0xAE000000AE4,
+        0xAE600000AF0,
+        0xAF900000B00,
+        0xB0100000B04,
+        0xB0500000B0D,
+        0xB0F00000B11,
+        0xB1300000B29,
+        0xB2A00000B31,
+        0xB3200000B34,
+        0xB3500000B3A,
+        0xB3C00000B45,
+        0xB4700000B49,
+        0xB4B00000B4E,
+        0xB5500000B58,
+        0xB5F00000B64,
+        0xB6600000B70,
+        0xB7100000B72,
+        0xB8200000B84,
+        0xB8500000B8B,
+        0xB8E00000B91,
+        0xB9200000B96,
+        0xB9900000B9B,
+        0xB9C00000B9D,
+        0xB9E00000BA0,
+        0xBA300000BA5,
+        0xBA800000BAB,
+        0xBAE00000BBA,
+        0xBBE00000BC3,
+        0xBC600000BC9,
+        0xBCA00000BCE,
+        0xBD000000BD1,
+        0xBD700000BD8,
+        0xBE600000BF0,
+        0xC0000000C0D,
+        0xC0E00000C11,
+        0xC1200000C29,
+        0xC2A00000C3A,
+        0xC3C00000C45,
+        0xC4600000C49,
+        0xC4A00000C4E,
+        0xC5500000C57,
+        0xC5800000C5B,
+        0xC5C00000C5E,
+        0xC6000000C64,
+        0xC6600000C70,
+        0xC8000000C84,
+        0xC8500000C8D,
+        0xC8E00000C91,
+        0xC9200000CA9,
+        0xCAA00000CB4,
+        0xCB500000CBA,
+        0xCBC00000CC5,
+        0xCC600000CC9,
+        0xCCA00000CCE,
+        0xCD500000CD7,
+        0xCDC00000CDF,
+        0xCE000000CE4,
+        0xCE600000CF0,
+        0xCF100000CF4,
+        0xD0000000D0D,
+        0xD0E00000D11,
+        0xD1200000D45,
+        0xD4600000D49,
+        0xD4A00000D4F,
+        0xD5400000D58,
+        0xD5F00000D64,
+        0xD6600000D70,
+        0xD7A00000D80,
+        0xD8100000D84,
+        0xD8500000D97,
+        0xD9A00000DB2,
+        0xDB300000DBC,
+        0xDBD00000DBE,
+        0xDC000000DC7,
+        0xDCA00000DCB,
+        0xDCF00000DD5,
+        0xDD600000DD7,
+        0xDD800000DE0,
+        0xDE600000DF0,
+        0xDF200000DF4,
+        0xE0100000E33,
+        0xE3400000E3B,
+        0xE4000000E4F,
+        0xE5000000E5A,
+        0xE8100000E83,
+        0xE8400000E85,
+        0xE8600000E8B,
+        0xE8C00000EA4,
+        0xEA500000EA6,
+        0xEA700000EB3,
+        0xEB400000EBE,
+        0xEC000000EC5,
+        0xEC600000EC7,
+        0xEC800000ECF,
+        0xED000000EDA,
+        0xEDE00000EE0,
+        0xF0000000F01,
+        0xF0B00000F0C,
+        0xF1800000F1A,
+        0xF2000000F2A,
+        0xF3500000F36,
+        0xF3700000F38,
+        0xF3900000F3A,
+        0xF3E00000F43,
+        0xF4400000F48,
+        0xF4900000F4D,
+        0xF4E00000F52,
+        0xF5300000F57,
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+        0x1E6E00001E6F6,
+        0x1E6FE0001E700,
+        0x1E7E00001E7E7,
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+        0x1E7F00001E7FF,
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+        0x1E9220001E94C,
+        0x1E9500001E95A,
+        0x200000002A6E0,
+        0x2A7000002B81E,
+        0x2B8200002CEAE,
+        0x2CEB00002EBE1,
+        0x2EBF00002EE5E,
+        0x300000003134B,
+        0x313500003347A,
+    ),
+    "CONTEXTJ": (0x200C0000200E,),
+    "CONTEXTO": (
+        0xB7000000B8,
+        0x37500000376,
+        0x5F3000005F5,
+        0x6600000066A,
+        0x6F0000006FA,
+        0x30FB000030FC,
+    ),
+}

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/idna/package_data.py

@@ -0,0 +1 @@
+__version__ = "3.16"

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/siglip/image_processing_pil_siglip.py

@@ -0,0 +1,38 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for SigLIP."""
+
+from ...image_processing_backends import PilBackend
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    PILImageResampling,
+)
+from ...utils import auto_docstring
+
+
+@auto_docstring(custom_intro="Constructs a SigLIP image processor.")
+class SiglipImageProcessorPil(PilBackend):
+    resample = PILImageResampling.BICUBIC
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 224, "width": 224}
+    default_to_square = False
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+
+
+__all__ = ["SiglipImageProcessorPil"]

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/siglip/image_processing_siglip.py

@@ -0,0 +1,38 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for SigLIP."""
+
+from ...image_processing_backends import TorchvisionBackend
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    PILImageResampling,
+)
+from ...utils import auto_docstring
+
+
+@auto_docstring(custom_intro="Constructs a SigLIP image processor.")
+class SiglipImageProcessor(TorchvisionBackend):
+    resample = PILImageResampling.BICUBIC
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 224, "width": 224}
+    default_to_square = False
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+
+
+__all__ = ["SiglipImageProcessor"]

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/siglip/modeling_siglip.py

@@ -0,0 +1,936 @@
+# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Siglip model."""
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import torch
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...masking_utils import create_bidirectional_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+    ModelOutput,
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    torch_int,
+)
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
+
+
+@auto_docstring(
+    custom_intro="""
+    Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
+    """
+)
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip
+class SiglipVisionModelOutput(ModelOutput):
+    r"""
+    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+        The image embeddings obtained by applying the projection layer to the pooler_output.
+    """
+
+    image_embeds: torch.FloatTensor | None = None
+    last_hidden_state: torch.FloatTensor | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+
+
+@auto_docstring(
+    custom_intro="""
+    Base class for text model's outputs that also contains a pooling of the last hidden states.
+    """
+)
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip
+class SiglipTextModelOutput(ModelOutput):
+    r"""
+    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+        The text embeddings obtained by applying the projection layer to the pooler_output.
+    """
+
+    text_embeds: torch.FloatTensor | None = None
+    last_hidden_state: torch.FloatTensor | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+
+
+@auto_docstring
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip
+class SiglipOutput(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+        Contrastive loss for image-text similarity.
+    logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
+        The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
+        similarity scores.
+    logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
+        The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
+        similarity scores.
+    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
+        The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`].
+    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
+        The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`].
+    text_model_output (`BaseModelOutputWithPooling`):
+        The output of the [`SiglipTextModel`].
+    vision_model_output (`BaseModelOutputWithPooling`):
+        The output of the [`SiglipVisionModel`].
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits_per_image: torch.FloatTensor | None = None
+    logits_per_text: torch.FloatTensor | None = None
+    text_embeds: torch.FloatTensor | None = None
+    image_embeds: torch.FloatTensor | None = None
+    text_model_output: BaseModelOutputWithPooling = None
+    vision_model_output: BaseModelOutputWithPooling = None
+
+    def to_tuple(self) -> tuple[Any]:
+        return tuple(v.to_tuple() if isinstance(v, ModelOutput) else v for v in self.values())
+
+
+class SiglipVisionEmbeddings(nn.Module):
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+        images. This method is also adapted to support torch.jit tracing and no class embeddings.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+        """
+
+        num_patches = embeddings.shape[1]
+        num_positions = self.position_embedding.weight.shape[0]
+
+        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+            return self.position_embedding(self.position_ids)
+
+        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)
+
+        dim = embeddings.shape[-1]
+
+        new_height = height // self.patch_size
+        new_width = width // self.patch_size
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(new_height, new_width),
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
+        _, _, height, width = pixel_values.shape
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))  # shape = [*, width, grid, grid]
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embedding(self.position_ids)
+        return embeddings
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip
+class SiglipTextEmbeddings(nn.Module):
+    def __init__(self, config: SiglipTextConfig):
+        super().__init__()
+        embed_dim = config.hidden_size
+
+        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
+        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+    ) -> torch.Tensor:
+        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+        max_position_embedding = self.position_embedding.weight.shape[0]
+
+        if seq_length > max_position_embedding:
+            raise ValueError(
+                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
+                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
+            )
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.token_embedding(input_ids)
+
+        position_embeddings = self.position_embedding(position_ids)
+        embeddings = inputs_embeds + position_embeddings
+
+        return embeddings
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+    attn_output = torch.matmul(attn_weights, value)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class SiglipAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+        self.is_causal = False
+
+        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Input shape: Batch x Time x Channel"""
+
+        input_shape = hidden_states.shape[:-1]
+
+        hidden_shape = (*input_shape, -1, self.head_dim)
+        queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            queries,
+            keys,
+            values,
+            attention_mask,
+            is_causal=self.is_causal,
+            scaling=self.scale,
+            dropout=0.0 if not self.training else self.dropout,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
+class SiglipMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class SiglipEncoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: SiglipVisionConfig | SiglipTextConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.self_attn = SiglipAttention(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+
+    @auto_docstring
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+@auto_docstring
+class SiglipPreTrainedModel(PreTrainedModel):
+    config: SiglipConfig
+    base_model_prefix = "siglip"
+    input_modalities = ("image", "text")
+    supports_gradient_checkpointing = True
+
+    _no_split_modules = [
+        "SiglipTextEmbeddings",
+        "SiglipVisionEmbeddings",
+        "SiglipEncoderLayer",
+        "SiglipMultiheadAttentionPoolingHead",
+    ]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_attention_backend = True
+
+    _can_record_outputs = {
+        "hidden_states": SiglipEncoderLayer,
+        "attentions": SiglipAttention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, SiglipVisionEmbeddings):
+            width = (
+                self.config.vision_config.hidden_size
+                if isinstance(self.config, SiglipConfig)
+                else self.config.hidden_size
+            )
+            init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
+            if hasattr(module, "position_ids"):
+                init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
+        elif isinstance(module, nn.Embedding):
+            init.default_flax_embed_init_(module.weight)
+        elif isinstance(module, SiglipAttention):
+            init.xavier_uniform_(module.q_proj.weight)
+            init.xavier_uniform_(module.k_proj.weight)
+            init.xavier_uniform_(module.v_proj.weight)
+            init.xavier_uniform_(module.out_proj.weight)
+            init.zeros_(module.q_proj.bias)
+            init.zeros_(module.k_proj.bias)
+            init.zeros_(module.v_proj.bias)
+            init.zeros_(module.out_proj.bias)
+        elif isinstance(module, SiglipMLP):
+            init.xavier_uniform_(module.fc1.weight)
+            init.xavier_uniform_(module.fc2.weight)
+            init.normal_(module.fc1.bias, std=1e-6)
+            init.normal_(module.fc2.bias, std=1e-6)
+        elif isinstance(module, SiglipMultiheadAttentionPoolingHead):
+            init.xavier_uniform_(module.probe)
+            init.xavier_uniform_(module.attention.in_proj_weight)
+            init.zeros_(module.attention.in_proj_bias)
+        elif isinstance(module, SiglipModel):
+            init.zeros_(module.logit_scale)
+            init.zeros_(module.logit_bias)
+        elif isinstance(module, SiglipForImageClassification):
+            init.normal_(
+                module.classifier.weight,
+                std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor,
+            )
+        elif isinstance(module, (nn.Linear, nn.Conv2d)):
+            init.lecun_normal_(module.weight)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            init.zeros_(module.bias)
+            init.ones_(module.weight)
+        elif isinstance(module, SiglipTextEmbeddings):
+            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
+
+
+# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->Siglip
+class SiglipEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`SiglipEncoderLayer`].
+
+    Args:
+        config: SiglipConfig
+    """
+
+    def __init__(self, config: SiglipConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    # Ignore copy
+    @auto_docstring
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states,
+                attention_mask,
+                **kwargs,
+            )
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+@auto_docstring(
+    custom_intro="""
+    The text model from SigLIP without any head or projection on top.
+    """
+)
+class SiglipTextModel(SiglipPreTrainedModel):
+    config: SiglipTextConfig
+    input_modalities = ("text",)
+    base_model_prefix = "text_model"
+    _input_embed_layer = "token_embedding"
+
+    def __init__(self, config: SiglipTextConfig):
+        super().__init__(config)
+        self.config = config
+        embed_dim = config.hidden_size
+        self.embeddings = SiglipTextEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+        self.head = nn.Linear(embed_dim, config.projection_size)
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer, SiglipTextModel
+
+        >>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> # important: make sure to set padding="max_length" as that's how the model was trained
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
+        ```"""
+        if input_ids is None:
+            raise ValueError("You have to specify input_ids")
+
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
+
+        # note: SigLIP's text model does not use a causal mask, unlike the original CLIP model.
+        attention_mask = create_bidirectional_mask(
+            config=self.config,
+            inputs_embeds=hidden_states,
+            attention_mask=attention_mask,
+        )
+
+        encoder_outputs: BaseModelOutput = self.encoder(
+            inputs_embeds=hidden_states,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+
+        last_hidden_state = encoder_outputs.last_hidden_state
+        last_hidden_state = self.final_layer_norm(last_hidden_state)
+
+        # The model uses the last token's hidden state, which may be padding.
+        pooled_output = last_hidden_state[:, -1, :]
+        pooled_output = self.head(pooled_output)
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The vision model from SigLIP without any head or projection on top.
+    """
+)
+class SiglipVisionModel(SiglipPreTrainedModel):
+    config: SiglipVisionConfig
+    main_input_name = "pixel_values"
+    input_modalities = ("image",)
+    base_model_prefix = "vision_model"
+    _input_embed_layer = "patch_embedding"
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__(config)
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head
+        if self.use_head:
+            self.head = SiglipMultiheadAttentionPoolingHead(config)
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        Examples:
+
+        ```python
+        >>> import httpx
+        >>> from io import BytesIO
+        >>> from PIL import Image
+        >>> from transformers import AutoProcessor, SiglipVisionModel
+
+        >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> with httpx.stream("GET", url) as response:
+        ...     image = Image.open(BytesIO(response.read()))
+
+        >>> inputs = processor(images=image, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled features
+        ```"""
+        hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
+
+        encoder_outputs: BaseModelOutput = self.encoder(
+            inputs_embeds=hidden_states,
+            **kwargs,
+        )
+
+        last_hidden_state = encoder_outputs.last_hidden_state
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        pooler_output = self.head(last_hidden_state) if self.use_head else None
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooler_output,
+        )
+
+
+class SiglipMultiheadAttentionPoolingHead(nn.Module):
+    """Multihead Attention Pooling."""
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+
+        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
+        self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+
+    def forward(self, hidden_state):
+        batch_size = hidden_state.shape[0]
+        probe = self.probe.repeat(batch_size, 1, 1)
+
+        hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
+
+        residual = hidden_state
+        hidden_state = self.layernorm(hidden_state)
+        hidden_state = residual + self.mlp(hidden_state)
+
+        return hidden_state[:, 0]
+
+
+@auto_docstring
+class SiglipModel(SiglipPreTrainedModel):
+    config: SiglipConfig
+
+    def __init__(self, config: SiglipConfig):
+        super().__init__(config)
+
+        text_config = config.text_config
+        vision_config = config.vision_config
+
+        # First, initialize the text and vision models with proper attention implementation
+        self.text_model = SiglipTextModel._from_config(text_config)
+        self.vision_model = SiglipVisionModel._from_config(vision_config)
+
+        self.logit_scale = nn.Parameter(torch.randn(1))
+        self.logit_bias = nn.Parameter(torch.randn(1))
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.text_model.embeddings.token_embedding
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.text_model.embeddings.token_embedding = value
+
+    @can_return_tuple
+    @auto_docstring
+    def get_text_features(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModel
+        >>> import torch
+
+        >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> # important: make sure to set padding="max_length" as that's how the model was trained
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
+        >>> with torch.no_grad():
+        ...     text_features = model.get_text_features(**inputs)
+        ```"""
+        return self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        interpolate_pos_encoding: bool = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        Examples:
+
+        ```python
+        >>> import torch
+        >>> from transformers import AutoProcessor, AutoModel
+        >>> from transformers.image_utils import load_image
+
+        >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = load_image(url)
+
+        >>> inputs = processor(images=image, return_tensors="pt")
+
+        >>> with torch.no_grad():
+        ...     image_features = model.get_image_features(**inputs)
+        ```"""
+        return self.vision_model(
+            pixel_values=pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            **kwargs,
+        )
+
+    # NOTE: SiglipModel uses Pretrained backbones, so we don't need to add `capture_outputs` here
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        return_loss: bool | None = None,
+        interpolate_pos_encoding: bool = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> SiglipOutput:
+        r"""
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import httpx
+        >>> from io import BytesIO
+        >>> from transformers import AutoProcessor, AutoModel
+        >>> import torch
+
+        >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> with httpx.stream("GET", url) as response:
+        ...     image = Image.open(BytesIO(response.read()))
+
+        >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
+        >>> # important: we pass `padding=max_length` since the model was trained with this
+        >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> logits_per_image = outputs.logits_per_image
+        >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
+        >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+        31.9% that image 0 is 'a photo of 2 cats'
+        ```"""
+        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
+            pixel_values=pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            **kwargs,
+        )
+
+        text_outputs: BaseModelOutputWithPooling = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            **kwargs,
+        )
+
+        image_embeds = vision_outputs.pooler_output
+        text_embeds = text_outputs.pooler_output
+
+        # normalized features
+        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
+        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
+
+        # cosine similarity as logits
+        logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device))
+
+        logit_scale, logit_bias = self.logit_scale.to(text_embeds.device), self.logit_bias.to(text_embeds.device)
+        logits_per_text = logits_per_text * logit_scale.exp() + logit_bias
+
+        logits_per_image = logits_per_text.t()
+
+        loss = None
+        if return_loss:
+            # Adapted from https://github.com/google-research/big_vision/blob/01edb81a4716f93a48be43b3a4af14e29cdb3a7f/big_vision/trainers/proj/image_text/siglip.py#L287
+            eye = torch.eye(logits_per_text.size(0), device=logits_per_text.device)
+            m1_diag1 = -torch.ones_like(logits_per_text) + 2 * eye
+            loglik = torch.nn.functional.logsigmoid(m1_diag1 * logits_per_text)
+            nll = -torch.sum(loglik, dim=-1)
+            loss = nll.mean()
+
+        return SiglipOutput(
+            loss=loss,
+            logits_per_image=logits_per_image,
+            logits_per_text=logits_per_text,
+            text_embeds=text_embeds,
+            image_embeds=image_embeds,
+            text_model_output=text_outputs,
+            vision_model_output=vision_outputs,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    SigLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
+    the patch tokens) e.g. for ImageNet.
+    """
+)
+class SiglipForImageClassification(SiglipPreTrainedModel):
+    main_input_name = "pixel_values"
+    input_modalities = ("image",)
+
+    def __init__(self, config: SiglipConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.vision_model = SiglipVisionModel._from_config(config.vision_config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.vision_model.embeddings.patch_embedding
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.vision_model.embeddings.patch_embedding = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        interpolate_pos_encoding: bool = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ImageClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoImageProcessor, SiglipForImageClassification
+        >>> import torch
+        >>> from PIL import Image
+        >>> import httpx
+        >>> from io import BytesIO
+
+        >>> torch.manual_seed(3)  # doctest: +IGNORE_RESULT
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> with httpx.stream("GET", url) as response:
+        ...     image = Image.open(BytesIO(response.read()))
+
+        >>> # note: we are loading a `SiglipModel` from the hub here,
+        >>> # so the head will be randomly initialized, hence the predictions will be random if seed is not set above.
+        >>> image_processor = AutoImageProcessor.from_pretrained("google/siglip-base-patch16-224")
+        >>> model = SiglipForImageClassification.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> inputs = image_processor(images=image, return_tensors="pt")
+        >>> outputs = model(**inputs)
+        >>> logits = outputs.logits
+        >>> # model predicts one of the two classes
+        >>> predicted_class_idx = logits.argmax(-1).item()
+        >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+        Predicted class: LABEL_1
+        ```"""
+        outputs: BaseModelOutputWithPooling = self.vision_model(
+            pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            **kwargs,
+        )
+
+        sequence_output = outputs.last_hidden_state
+
+        # average pool the patch tokens
+        sequence_output = torch.mean(sequence_output, dim=1)
+        # apply classifier
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(labels, logits, self.config)
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "SiglipModel",
+    "SiglipPreTrainedModel",
+    "SiglipTextModel",
+    "SiglipVisionModel",
+    "SiglipForImageClassification",
+]

LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/siglip/tokenization_siglip.py

@@ -0,0 +1,352 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for SigLIP model."""
+
+import os
+import re
+import string
+import warnings
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any
+
+import sentencepiece as spm
+
+from ...tokenization_utils_base import AddedToken
+from ...tokenization_utils_sentencepiece import SentencePieceBackend
+
+
+if TYPE_CHECKING:
+    from ...tokenization_utils_base import TextInput
+from ...utils import logging, requires_backends
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
+
+
+SPIECE_UNDERLINE = "▁"
+
+
+@requires(backends=("sentencepiece",))
+class SiglipTokenizer(SentencePieceBackend):
+    """
+    Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"</s>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        additional_special_tokens (`list[str]`, *optional*):
+            Additional special tokens used by the tokenizer.
+        sp_model_kwargs (`dict`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+        model_max_length (`int`, *optional*, defaults to 64):
+            The maximum length (in number of tokens) for model inputs.
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        eos_token="</s>",
+        unk_token="<unk>",
+        pad_token="</s>",
+        additional_special_tokens=None,
+        sp_model_kwargs: dict[str, Any] | None = None,
+        model_max_length=64,
+        do_lower_case=True,
+        **kwargs,
+    ) -> None:
+        requires_backends(self, "protobuf")
+
+        pad_token = (
+            AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True)
+            if isinstance(pad_token, str)
+            else pad_token
+        )
+        unk_token = (
+            AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True)
+            if isinstance(unk_token, str)
+            else unk_token
+        )
+        eos_token = (
+            AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True)
+            if isinstance(eos_token, str)
+            else eos_token
+        )
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.do_lower_case = do_lower_case
+
+        super().__init__(
+            vocab_file=vocab_file,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            additional_special_tokens=additional_special_tokens,
+            sp_model_kwargs=self.sp_model_kwargs,
+            model_max_length=model_max_length,
+            do_lower_case=do_lower_case,
+            **kwargs,
+        )
+
+    @property
+    def vocab_size(self):
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def get_special_tokens_mask(
+        self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False
+    ) -> list[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`list[int]`):
+                List of IDs.
+            token_ids_1 (`list[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        # normal case: some special tokens
+        if token_ids_1 is None:
+            return ([0] * len(token_ids_0)) + [1]
+        return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+
+    def _add_eos_if_not_present(self, token_ids: list[int]) -> list[int]:
+        """Do not add eos again if user already added it."""
+        if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
+            warnings.warn(
+                f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
+                " eos tokens being added."
+            )
+            return token_ids
+        else:
+            return token_ids + [self.eos_token_id]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: list[int], token_ids_1: list[int] | None = None
+    ) -> list[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`list[int]`):
+                List of IDs.
+            token_ids_1 (`list[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `list[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: list[int], token_ids_1: list[int] | None = None
+    ) -> list[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A sequence has the following format:
+
+        - single sequence: `X </s>`
+        - pair of sequences: `A </s> B </s>`
+
+        Args:
+            token_ids_0 (`list[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`list[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        token_ids_0 = self._add_eos_if_not_present(token_ids_0)
+        if token_ids_1 is None:
+            return token_ids_0
+        else:
+            token_ids_1 = self._add_eos_if_not_present(token_ids_1)
+            return token_ids_0 + token_ids_1
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+
+    def remove_punctuation(self, text: str) -> str:
+        return text.translate(str.maketrans("", "", string.punctuation))
+
+    # source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
+    def canonicalize_text(self, text, *, keep_punctuation_exact_string=None):
+        """Returns canonicalized `text` (puncuation removed).
+
+        Args:
+            text (`str`):
+                String to be canonicalized.
+            keep_punctuation_exact_string (`str`, *optional*):
+                If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}'
+                (but will still remove '{' and '}' that appear separately).
+        """
+        if self.do_lower_case:
+            text = text.lower()
+
+        if keep_punctuation_exact_string:
+            text = keep_punctuation_exact_string.join(
+                self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string)
+            )
+        else:
+            text = self.remove_punctuation(text)
+        text = re.sub(r"\s+", " ", text)
+        text = text.strip()
+
+        return text
+
+    def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> list[str]:
+        """
+        Converts a string to a list of tokens.
+        """
+        tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
+
+        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
+            tokens = tokens[1:]
+        return tokens
+
+    @property
+    def unk_token_length(self):
+        return len(self.sp_model.encode(str(self.unk_token)))
+
+    def _tokenize(self, text, **kwargs):
+        """
+        Returns a tokenized string.
+
+        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
+        SPIECE_UNDERLINE.
+
+        For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`.
+
+        Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
+        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
+        """
+        text = self.canonicalize_text(text, keep_punctuation_exact_string=None)
+        tokens = self.sp_model.encode(text, out_type=str)
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+
+__all__ = ["SiglipTokenizer"]