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lm-evaluation-harness/wandb/run-20240514_103002-s6mpkihm/files/output.log

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+
+2024-05-14:10:30:02,599 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:10:30:07,079 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:10:30:07,081 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:10:30:07,081 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step100'}
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/core/register.py:145: UserWarning: "hpu:X" notation is not supported by Gaudi PyTorch intergration bridge. Please change to "hpu" without index (Triggered internally at /npu-stack/pytorch-integration/pytorch_helpers/lazy_to_backend.cpp:53.)
+  return func(*args, **kwargs)
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/__init__.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
+  warnings.warn(
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+2024-05-14:10:30:13,626 WARNING  [task.py:763] [Task: indiccopa-hi] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-14:10:30:13,626 WARNING  [task.py:775] [Task: indiccopa-hi] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+[2024-05-14 10:30:13,248] [INFO] [real_accelerator.py:178:get_accelerator] Setting ds_accelerator to hpu (auto detect)
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for ai4bharat/IndicCOPA contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/ai4bharat/IndicCOPA
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-05-14:10:30:15,078 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:10:30:15,078 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:10:30:15,097 INFO     [task.py:395] Building contexts for indiccopa-hi on rank 2...
+100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56/56 [00:00<00:00, 110221.03it/s]
+Passed argument batch_size = auto:1. Detecting largest batch size
+2024-05-14:10:30:16,729 INFO     [evaluator.py:379] Running loglikelihood requests
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+Determined largest batch size: 64

lm-evaluation-harness/wandb/run-20240514_103002-s6mpkihm/files/requirements.txt

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lm-evaluation-harness/wandb/run-20240514_103002-s6mpkihm/files/wandb-summary.json

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+{"_wandb": {"runtime": 21}}

lm-evaluation-harness/wandb/run-20240514_103117-q880yywu/files/output.log

@@ -0,0 +1,42 @@
+
+2024-05-14:10:31:17,654 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:10:31:22,053 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:10:31:22,056 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:10:31:22,057 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step20'}
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 398, in cached_file
+    resolved_file = hf_hub_download(
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/data/cronscript/ckpts//hf_ckpt//global_step20'. Use `repo_type` argument if needed.
+The above exception was the direct cause of the following exception:
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 928, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 631, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 686, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 462, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/data/cronscript/ckpts//hf_ckpt//global_step20'. Please provide either the path to a local folder or the repo_id of a model on the Hub.

lm-evaluation-harness/wandb/run-20240514_103117-q880yywu/files/requirements.txt

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+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.3
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.2
+absl-py==2.1.0
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+aiosignal==1.3.1
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+av==9.2.0
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+certifi==2024.2.2
+cffi==1.15.1
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+chardet==5.2.0
+charset-normalizer==3.3.2
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+colorama==0.4.6
+datasets==2.19.1
+deepspeed==0.12.4+hpu.synapse.v1.15.1
+dill==0.3.8
+distlib==0.3.8
+distro==1.9.0
+docker-pycreds==0.4.0
+einops==0.8.0
+evaluate==0.4.2
+exceptiongroup==1.2.0
+expecttest==0.2.1
+filelock==3.13.4
+frozenlist==1.4.1
+fsspec==2024.3.1
+gitdb==4.0.11
+google-auth-oauthlib==0.4.6
+google-auth==2.29.0
+grpcio==1.62.1
+h11==0.14.0
+habana-media-loader==1.15.1.15
+habana-pyhlml==1.15.1.15
+habana-torch-dataloader==1.15.1.15
+habana-torch-plugin==1.15.1.15
+habana_gpu_migration==1.15.1.15
+habana_quantization_toolkit==1.15.1.15
+hjson==3.1.0
+httpcore==1.0.5
+httpx==0.27.0
+huggingface-hub==0.23.0
+identify==2.5.35
+idna==3.7
+importlib_resources==6.4.0
+iniconfig==2.0.0
+joblib==1.4.2
+jsonlines==4.0.0
+lightning-habana==1.4.0
+lightning-utilities==0.11.2
+lightning==2.2.0.post0
+lm_eval==0.3.0
+lm_eval==0.4.2
+lm_eval==0.4.2
+lm_eval==0.4.2
+mbstrdecoder==1.1.3
+more-itertools==10.2.0
+mpi4py==3.1.4
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+networkx==3.3
+ninja==1.11.1.1
+nltk==3.8.1
+nodeenv==1.8.0
+numexpr==2.10.0
+numpy==1.23.5
+oauthlib==3.2.2
+omegaconf==2.3.0
+openai==1.29.0
+packaging==24.0
+pandas==2.0.1
+pathspec==0.12.1
+pathvalidate==3.2.0
+peft==0.10.0
+perfetto==0.7.0
+pip==22.0.2
+pip==23.3.1
+platformdirs==4.2.0
+pluggy==1.4.0
+portalocker==2.8.2
+pre-commit==3.3.3
+protobuf==3.20.3
+psutil==5.9.8
+py-cpuinfo==9.0.0
+pyarrow-hotfix==0.6
+pyarrow==16.0.0
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pybind11==2.10.4
+pycountry==23.12.11
+pycparser==2.22
+pydantic==1.10.13
+pynvml==8.0.4
+pytablewriter==1.2.0
+pytest==8.1.1
+python-dateutil==2.9.0.post0
+pytorch-lightning==2.2.2
+pytz==2024.1
+regex==2023.5.5
+requests-oauthlib==2.0.0
+requests==2.31.0
+rouge_score==0.1.2
+rsa==4.9
+sacrebleu==1.5.0
+safetensors==0.4.3
+scikit-learn==1.4.2
+scipy==1.13.0
+sentencepiece==0.2.0
+sentry-sdk==2.1.1
+setproctitle==1.3.3
+setuptools==59.6.0
+setuptools==69.5.1
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+sqlitedict==2.1.0
+symengine==0.11.0
+sympy==1.12
+tabledata==1.3.3
+tcolorpy==0.1.6
+tdqm==0.0.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorboard==2.11.2
+threadpoolctl==3.5.0
+tokenizers==0.19.1
+tomli==2.0.1
+torch==2.2.0a0+git8964477
+torch_tb_profiler==0.4.0
+torchaudio==2.2.0+08901ad
+torchdata==0.7.1+5e6f7b7
+torchmetrics==1.3.2
+torchtext==0.17.0+400da5c
+torchvision==0.17.0+b2383d4
+tqdm-multiprocess==0.0.11
+tqdm==4.66.2
+transformers==4.40.2
+typepy==1.3.2
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==1.26.18
+virtualenv==20.25.1
+wandb==0.17.0
+wheel==0.37.1
+wheel==0.43.0
+word2number==1.1
+xxhash==3.4.1
+yamllint==1.35.1
+yarl==1.9.4
+zstandard==0.22.0

lm-evaluation-harness/wandb/run-20240522_164053-70sdzmdf/files/config.yaml

@@ -0,0 +1,43 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.41.0
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1716396053
+    t:
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+      3:
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+      4: 3.10.12
+      5: 0.17.0
+      6: 4.41.0
+      8:
+      - 5
+      13: linux-x86_64

lm-evaluation-harness/wandb/run-20240522_164053-70sdzmdf/files/output.log

@@ -0,0 +1,34 @@
+
+2024-05-22:16:40:54,677 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-22:16:41:03,813 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-22:16:41:03,814 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-22:16:41:03,814 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100'}
+2024-05-22:16:41:06,099 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 370, in cached_file
+    raise EnvironmentError(
+OSError: /mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100 does not appear to have a file named config.json. Checkout 'https://huggingface.co//mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100/tree/main' for available files.

lm-evaluation-harness/wandb/run-20240522_164053-70sdzmdf/files/requirements.txt

@@ -0,0 +1,155 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.3
+absl-py==2.1.0
+accelerate==0.30.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+async-timeout==4.0.3
+attrs==23.2.0
+av==9.2.0
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.15.1
+cfgv==3.4.0
+chardet==5.2.0
+charset-normalizer==3.3.2
+click==8.1.7
+cmake==3.29.2
+colorama==0.4.6
+datasets==2.19.1
+deepspeed==0.12.4+hpu.synapse.v1.15.1
+dill==0.3.8
+distlib==0.3.8
+docker-pycreds==0.4.0
+einops==0.8.0
+evaluate==0.4.2
+exceptiongroup==1.2.1
+expecttest==0.2.1
+filelock==3.14.0
+frozenlist==1.4.1
+fsspec==2024.3.1
+gitdb==4.0.11
+google-auth-oauthlib==0.4.6
+google-auth==2.29.0
+grpcio==1.63.0
+habana-media-loader==1.15.1.15
+habana-pyhlml==1.15.1.15
+habana-torch-dataloader==1.15.1.15
+habana-torch-plugin==1.15.1.15
+habana_gpu_migration==1.15.1.15
+habana_quantization_toolkit==1.15.1.15
+hjson==3.1.0
+huggingface-hub==0.23.1
+identify==2.5.36
+idna==3.7
+iniconfig==2.0.0
+joblib==1.4.2
+jsonlines==4.0.0
+lightning-habana==1.4.0
+lightning-utilities==0.11.2
+lightning==2.2.0.post0
+lm_eval==0.4.2
+lm_eval==0.4.2
+lm_eval==0.4.2
+lxml==5.2.2
+mbstrdecoder==1.1.3
+more-itertools==10.2.0
+mpi4py==3.1.4
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+networkx==3.3
+ninja==1.11.1.1
+nltk==3.8.1
+nodeenv==1.8.0
+numexpr==2.10.0
+numpy==1.23.5
+oauthlib==3.2.2
+packaging==24.0
+pandas==2.0.1
+pathspec==0.12.1
+pathvalidate==3.2.0
+peft==0.11.1
+perfetto==0.7.0
+pillow==10.3.0
+pip==22.0.2
+pip==23.3.1
+platformdirs==4.2.1
+pluggy==1.5.0
+portalocker==2.8.2
+pre-commit==3.3.3
+pretty-errors==1.2.25
+protobuf==3.20.3
+psutil==5.9.8
+py-cpuinfo==9.0.0
+pyarrow-hotfix==0.6
+pyarrow==16.1.0
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pybind11==2.10.4
+pycparser==2.22
+pydantic==1.10.13
+pynvml==8.0.4
+pytablewriter==1.2.0
+pytest==8.2.0
+python-dateutil==2.9.0.post0
+pytorch-lightning==2.2.4
+pytz==2024.1
+regex==2023.5.5
+requests-oauthlib==2.0.0
+requests==2.31.0
+rouge_score==0.1.2
+rsa==4.9
+sacrebleu==2.4.2
+safetensors==0.4.3
+scikit-learn==1.5.0
+scipy==1.13.0
+sentencepiece==0.2.0
+sentry-sdk==2.2.1
+setproctitle==1.3.3
+setuptools==59.6.0
+setuptools==69.5.1
+six==1.16.0
+smmap==5.0.1
+sqlitedict==2.1.0
+symengine==0.11.0
+sympy==1.12
+tabledata==1.3.3
+tabulate==0.9.0
+tcolorpy==0.1.6
+tdqm==0.0.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorboard==2.11.2
+threadpoolctl==3.5.0
+tokenizers==0.19.1
+tomli==2.0.1
+torch==2.2.0a0+git8964477
+torch_tb_profiler==0.4.0
+torchaudio==2.2.0+08901ad
+torchdata==0.7.1+5e6f7b7
+torchmetrics==1.4.0
+torchtext==0.17.0+400da5c
+torchvision==0.17.0+b2383d4
+tqdm-multiprocess==0.0.11
+tqdm==4.66.4
+transformers==4.41.0
+typepy==1.3.2
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==1.26.18
+virtualenv==20.26.1
+wandb==0.17.0
+wheel==0.37.1
+wheel==0.43.0
+word2number==1.1
+xxhash==3.4.1
+yamllint==1.35.1
+yarl==1.9.4
+zstandard==0.22.0

lm-evaluation-harness/wandb/run-20240522_164053-70sdzmdf/files/wandb-metadata.json

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+    "startedAt": "2024-05-22T16:40:53.881781",
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+        "--model_args",
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+        "--tasks",
+        "hellaswag,arc_easy,openbookqa,winogrande,sst2,mrpc",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=bharatgpt,group=trial_expt_2"
+    ],
+    "state": "running",
+    "program": "-m lm_eval.__main__",
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+    "git": {
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+    "root": "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness",
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+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      3:
+      - 2
+      - 13
+      - 23
+      - 62
+      4: 3.10.12
+      5: 0.17.0
+      6: 4.36.2
+      8:
+      - 5
+      13: linux-x86_64
+task_configs:
+  desc: null
+  value:
+    arc_easy:
+      task: arc_easy
+      group:
+      - ai2_arc
+      dataset_path: allenai/ai2_arc
+      dataset_name: ARC-Easy
+      training_split: train
+      validation_split: validation
+      test_split: test
+      doc_to_text: 'Question: {{question}}
+
+        Answer:'
+      doc_to_target: '{{choices.label.index(answerKey)}}'
+      doc_to_choice: '{{choices.text}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      - metric: acc_norm
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: 'Question: {{question}}
+
+        Answer:'
+      metadata:
+        version: 1.0
+    boolq:
+      task: boolq
+      group:
+      - super-glue-lm-eval-v1
+      dataset_path: super_glue
+      dataset_name: boolq
+      training_split: train
+      validation_split: validation
+      doc_to_text: '{{passage}}
+
+        Question: {{question}}?
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice:
+      - 'no'
+      - 'yes'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: passage
+      metadata:
+        version: 2.0
+    copa:
+      task: copa
+      group:
+      - super-glue-lm-eval-v1
+      dataset_path: super_glue
+      dataset_name: copa
+      training_split: train
+      validation_split: validation
+      doc_to_text: "def doc_to_text(doc):\n    # Drop the period\n    connector =\
+        \ {\n        \"cause\": \"because\",\n        \"effect\": \"therefore\",\n\
+        \    }[doc[\"question\"]]\n    return doc[\"premise\"].strip()[:-1] + f\"\
+        \ {connector}\"\n"
+      doc_to_target: "def doc_to_target(doc):\n    correct_choice = doc[\"choice1\"\
+        ] if doc[\"label\"] == 0 else doc[\"choice2\"]\n    # Connect the sentences\n\
+        \    return \" \" + convert_choice(correct_choice)\n"
+      doc_to_choice: "def doc_to_choice(doc):\n    return [\" \" + convert_choice(doc[\"\
+        choice1\"]), \" \" + convert_choice(doc[\"choice2\"])]\n"
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    mrpc:
+      task: mrpc
+      group: glue
+      dataset_path: glue
+      dataset_name: mrpc
+      training_split: train
+      validation_split: validation
+      doc_to_text: 'Sentence 1: {{sentence1}}
+
+        Sentence 2: {{sentence2}}
+
+        Question: Do both sentences mean the same thing?
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice:
+      - 'no'
+      - 'yes'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      - metric: f1
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    piqa:
+      task: piqa
+      dataset_path: piqa
+      training_split: train
+      validation_split: validation
+      doc_to_text: 'Question: {{goal}}
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice: '{{[sol1, sol2]}}'
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      - metric: acc_norm
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: goal
+      metadata:
+        version: 1.0
+    sst2:
+      task: sst2
+      group: glue
+      dataset_path: glue
+      dataset_name: sst2
+      training_split: train
+      validation_split: validation
+      doc_to_text: '{{sentence}}
+
+        Question: Is this sentence positive or negative?
+
+        Answer:'
+      doc_to_target: label
+      doc_to_choice:
+      - negative
+      - positive
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: false
+      metadata:
+        version: 1.0
+    winogrande:
+      task: winogrande
+      dataset_path: winogrande
+      dataset_name: winogrande_xl
+      training_split: train
+      validation_split: validation
+      doc_to_text: "def doc_to_text(doc):\n    answer_to_num = {\"1\": 0, \"2\": 1}\n\
+        \    return answer_to_num[doc[\"answer\"]]\n"
+      doc_to_target: "def doc_to_target(doc):\n    idx = doc[\"sentence\"].index(\"\
+        _\") + 1\n    return doc[\"sentence\"][idx:].strip()\n"
+      doc_to_choice: "def doc_to_choice(doc):\n    idx = doc[\"sentence\"].index(\"\
+        _\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"\
+        sentence\"][:idx] + opt for opt in options]\n"
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
+      num_fewshot: 0
+      metric_list:
+      - metric: acc
+        aggregation: mean
+        higher_is_better: true
+      output_type: multiple_choice
+      repeats: 1
+      should_decontaminate: true
+      doc_to_decontamination_query: sentence
+      metadata:
+        version: 1.0
+cli_configs:
+  desc: null
+  value:
+    model: hf
+    model_args: pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step10000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer
+    batch_size: auto
+    batch_sizes:
+    - 64
+    device: null
+    use_cache: null
+    limit: null
+    bootstrap_iters: 100000
+    gen_kwargs: null

lm-evaluation-harness/wandb/run-20240530_125148-ddhu1ki8/files/media/table/evaluation/eval_results_1_f6c5ffdda60541ca1d9f.table.json

@@ -0,0 +1 @@
+{"columns": ["Tasks", "Version", "Filter", "num_fewshot", "Metric", "Value", "Stderr"], "data": [["winogrande", 1.0, "none", 0, "acc", "0.5043409629044988", "0.0141"], ["sst2", 1.0, "none", 0, "acc", "0.49770642201834864", "0.0169"], ["piqa", 1.0, "none", 0, "acc", "0.528835690968444", "0.0116"], ["piqa", 1.0, "none", 0, "acc_norm", "0.5097932535364527", "0.0117"], ["mrpc", 1.0, "none", 0, "acc", "0.3161764705882353", "0.0230"], ["mrpc", 1.0, "none", 0, "f1", "0.0", "0.0000"], ["copa", 1.0, "none", 0, "acc", "0.54", "0.0501"], ["boolq", 2.0, "none", 0, "acc", "0.3782874617737003", "0.0085"], ["arc_easy", 1.0, "none", 0, "acc", "0.26136363636363635", "0.0090"], ["arc_easy", 1.0, "none", 0, "acc_norm", "0.2622053872053872", "0.0090"]]}

lm-evaluation-harness/wandb/run-20240530_125148-ddhu1ki8/files/output.log

@@ -0,0 +1,549 @@
+
+2024-05-30:12:51:49,570 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-30:12:51:58,826 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'boolq', 'copa', 'mrpc', 'piqa', 'sst2', 'winogrande']
+2024-05-30:12:51:58,827 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-30:12:51:58,828 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step10000', 'tokenizer': '/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer'}
+2024-05-30:12:52:01,173 INFO     [huggingface.py:164] Using device 'cuda'
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+Downloading readme: 100%|██████████| 9.00k/9.00k [00:00<00:00, 16.7MB/s]
+Downloading data: 100%|██████████| 331k/331k [00:00<00:00, 1.30MB/s]
+Downloading data: 100%|██████████| 346k/346k [00:00<00:00, 2.91MB/s]
+Downloading data: 100%|██████████| 86.1k/86.1k [00:00<00:00, 212kB/s]
+Generating train split: 100%|██████████| 2251/2251 [00:00<00:00, 62398.00 examples/s]
+Generating test split: 100%|██████████| 2376/2376 [00:00<00:00, 319607.01 examples/s]
+Generating validation split: 100%|██████████| 570/570 [00:00<00:00, 150950.45 examples/s]
+2024-05-30:12:52:32,725 WARNING  [task.py:763] [Task: boolq] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:52:32,726 WARNING  [task.py:775] [Task: boolq] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for super_glue contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/super_glue
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+Downloading builder script: 100%|██████████| 30.7k/30.7k [00:00<00:00, 39.7MB/s]
+Downloading readme: 100%|██████████| 18.2k/18.2k [00:00<00:00, 30.5MB/s]
+Downloading data: 100%|██████████| 4.12M/4.12M [00:00<00:00, 35.6MB/s]
+Generating train split: 100%|██████████| 9427/9427 [00:00<00:00, 22295.61 examples/s]
+Generating validation split: 100%|██████████| 3270/3270 [00:00<00:00, 22421.99 examples/s]
+Generating test split: 100%|██████████| 3245/3245 [00:00<00:00, 23363.77 examples/s]
+2024-05-30:12:52:36,700 WARNING  [task.py:763] [Task: copa] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:52:36,700 WARNING  [task.py:775] [Task: copa] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+Downloading data: 100%|██████████| 44.0k/44.0k [00:00<00:00, 49.8MB/s]
+Generating train split: 100%|██████████| 400/400 [00:00<00:00, 16380.48 examples/s]
+Generating validation split: 100%|██████████| 100/100 [00:00<00:00, 12630.40 examples/s]
+Generating test split: 100%|██████████| 500/500 [00:00<00:00, 17361.39 examples/s]
+2024-05-30:12:52:38,954 WARNING  [task.py:763] [Task: mrpc] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:52:38,955 WARNING  [task.py:775] [Task: mrpc] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+2024-05-30:12:52:38,955 WARNING  [task.py:763] [Task: mrpc] metric f1 is defined, but aggregation is not. using default aggregation=f1
+2024-05-30:12:52:38,958 WARNING  [task.py:775] [Task: mrpc] metric f1 is defined, but higher_is_better is not. using default higher_is_better=True
+Downloading readme: 100%|██████████| 35.3k/35.3k [00:00<00:00, 42.2MB/s]
+Downloading data: 100%|██████████| 649k/649k [00:00<00:00, 2.82MB/s]
+Downloading data: 100%|██████████| 75.7k/75.7k [00:00<00:00, 493kB/s]
+Downloading data: 100%|██████████| 308k/308k [00:00<00:00, 1.96MB/s]
+Generating train split: 100%|██████████| 3668/3668 [00:00<00:00, 410488.73 examples/s]
+Generating validation split: 100%|██████████| 408/408 [00:00<00:00, 175749.82 examples/s]
+Generating test split: 100%|██████████| 1725/1725 [00:00<00:00, 383910.35 examples/s]
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for piqa contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/piqa
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+Downloading builder script: 100%|██████████| 5.36k/5.36k [00:00<00:00, 11.7MB/s]
+Downloading readme: 100%|██████████| 8.41k/8.41k [00:00<00:00, 17.6MB/s]
+Downloading data: 100%|██████████| 1.82M/1.82M [00:00<00:00, 4.15MB/s]
+Downloading data: 100%|██████████| 815k/815k [00:00<00:00, 16.5MB/s]
+Generating train split: 100%|██████████| 16113/16113 [00:00<00:00, 23714.34 examples/s]
+Generating test split: 100%|██████████| 3084/3084 [00:00<00:00, 24629.11 examples/s]
+Generating validation split: 100%|██████████| 1838/1838 [00:00<00:00, 23946.83 examples/s]
+2024-05-30:12:52:50,849 WARNING  [task.py:763] [Task: sst2] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-30:12:52:50,850 WARNING  [task.py:775] [Task: sst2] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+Downloading data: 100%|██████████| 3.11M/3.11M [00:00<00:00, 15.6MB/s]
+Downloading data: 100%|██████████| 72.8k/72.8k [00:00<00:00, 155kB/s]
+Downloading data: 100%|██████████| 148k/148k [00:00<00:00, 798kB/s]
+Generating train split: 100%|██████████| 67349/67349 [00:00<00:00, 1393425.48 examples/s]
+Generating validation split: 100%|██████████| 872/872 [00:00<00:00, 395740.43 examples/s]
+Generating test split: 100%|██████████| 1821/1821 [00:00<00:00, 467918.13 examples/s]
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for winogrande contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/winogrande
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+Downloading builder script: 100%|██████████| 5.65k/5.65k [00:00<00:00, 12.2MB/s]
+Downloading readme: 100%|██████████| 9.97k/9.97k [00:00<00:00, 19.8MB/s]
+Downloading data: 100%|██████████| 3.40M/3.40M [00:00<00:00, 6.97MB/s]
+Generating train split: 100%|██████████| 40398/40398 [00:01<00:00, 24529.38 examples/s]
+Generating test split: 100%|██████████| 1767/1767 [00:00<00:00, 24393.19 examples/s]
+Generating validation split: 100%|██████████| 1267/1267 [00:00<00:00, 22726.79 examples/s]
+2024-05-30:12:53:05,085 INFO     [task.py:395] Building contexts for winogrande on rank 0...
+100%|██████████| 1267/1267 [00:00<00:00, 69101.52it/s]
+2024-05-30:12:53:05,170 INFO     [task.py:395] Building contexts for sst2 on rank 0...
+100%|██████████| 872/872 [00:00<00:00, 2536.42it/s]
+2024-05-30:12:53:05,545 INFO     [task.py:395] Building contexts for piqa on rank 0...
+100%|██████████| 1838/1838 [00:01<00:00, 1092.54it/s]
+2024-05-30:12:53:07,304 INFO     [task.py:395] Building contexts for mrpc on rank 0...
+100%|██████████| 408/408 [00:00<00:00, 1819.61it/s]
+2024-05-30:12:53:07,549 INFO     [task.py:395] Building contexts for copa on rank 0...
+100%|██████████| 100/100 [00:00<00:00, 60558.82it/s]
+2024-05-30:12:53:07,558 INFO     [task.py:395] Building contexts for boolq on rank 0...
+100%|██████████| 3270/3270 [00:01<00:00, 1986.98it/s]
+2024-05-30:12:53:09,338 INFO     [task.py:395] Building contexts for arc_easy on rank 0...
+100%|██████████| 2376/2376 [00:02<00:00, 1066.01it/s]
+2024-05-30:12:53:11,719 INFO     [evaluator.py:379] Running loglikelihood requests
+Token indices sequence length is longer than the specified maximum sequence length for this model (1333 > 1024). Running this sequence through the model will result in indexing errors
+Running loglikelihood requests:   0%|          | 0/25011 [00:00<?, ?it/s]
+Passed argument batch_size = auto:1. Detecting largest batch size
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+bootstrapping for stddev: f1_score
+
+
+100%|██████████| 100/100 [02:11<00:00,  1.31s/it]
+hf (pretrained=/mnt/weka/peacock/experiments/llama/eval/checkpoint-english/llamav2-3b/hf/global_step10000,tokenizer=/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (64)
+|  Tasks   |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|----------|------:|------|-----:|--------|-----:|---|-----:|
+|winogrande|      1|none  |     0|acc     |0.5043|±  |0.0141|
+|sst2      |      1|none  |     0|acc     |0.4977|±  |0.0169|
+|piqa      |      1|none  |     0|acc     |0.5288|±  |0.0116|
+|          |       |none  |     0|acc_norm|0.5098|±  |0.0117|
+|mrpc      |      1|none  |     0|acc     |0.3162|±  |0.0230|
+|          |       |none  |     0|f1      |0.0000|±  |0.0000|
+|copa      |      1|none  |     0|acc     |0.5400|±  |0.0501|
+|boolq     |      2|none  |     0|acc     |0.3783|±  |0.0085|
+|arc_easy  |      1|none  |     0|acc     |0.2614|±  |0.0090|
+|          |       |none  |     0|acc_norm|0.2622|±  |0.0090|

lm-evaluation-harness/wandb/run-20240530_125148-ddhu1ki8/files/requirements.txt

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lm-evaluation-harness/wandb/run-20240530_125148-ddhu1ki8/files/wandb-summary.json

@@ -0,0 +1 @@
+{"winogrande/alias": "winogrande", "sst2/alias": "sst2", "piqa/alias": "piqa", "mrpc/alias": "mrpc", "copa/alias": "copa", "boolq/alias": "boolq", "arc_easy/alias": "arc_easy", "winogrande/acc": 0.5043409629044988, "winogrande/acc_stderr": 0.014051956064076906, "sst2/acc": 0.49770642201834864, "sst2/acc_stderr": 0.01694167544311352, "piqa/acc": 0.528835690968444, "piqa/acc_stderr": 0.011646407809944718, "piqa/acc_norm": 0.5097932535364527, "piqa/acc_norm_stderr": 0.011663586263283223, "mrpc/acc": 0.3161764705882353, "mrpc/acc_stderr": 0.023048336668420193, "mrpc/f1": 0.0, "mrpc/f1_stderr": 0.0, "copa/acc": 0.54, "copa/acc_stderr": 0.05009082659620333, "boolq/acc": 0.3782874617737003, "boolq/acc_stderr": 0.008482001133931, "arc_easy/acc": 0.26136363636363635, "arc_easy/acc_stderr": 0.009015838366608204, "arc_easy/acc_norm": 0.2622053872053872, "arc_easy/acc_norm_stderr": 0.009025197991724826, "_timestamp": 1717077144.15704, "_runtime": 3635.358528137207, "_step": 1, "evaluation/eval_results": {"_type": "table-file", "sha256": "f6c5ffdda60541ca1d9fd70e5fb3cfdb9f74bd241e213e919de6835b92d003d5", "size": 743, "artifact_path": "wandb-client-artifact://de3ilzh48uqhgicofk5nssooyxlj7r5cx1zfkprx2tssj5nmq14tf83gz0fdpogytl2ywm1cr3orx1skyn77e77ymgibgqdu4atyilqo9ad2fe5nsskaaphfxwkslecc/evaluation/eval_results.table.json", "_latest_artifact_path": "wandb-client-artifact://py80nmg4jlz4p7lvehaokdg4rbos99dvyp2b0j48f2muk6vxqjxly1l2t3bvpa894ff59bc04cdi2dntsihgtzzy7hg2g3pgml92z03zbv17jtdxwioyxz8891j8q4e9:latest/evaluation/eval_results.table.json", "path": "media/table/evaluation/eval_results_1_f6c5ffdda60541ca1d9f.table.json", "ncols": 7, "nrows": 10}, "_wandb": {"runtime": 3635}}

venv/lib/python3.10/site-packages/dataproperty/__init__.py

@@ -0,0 +1,45 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+from .__version__ import __author__, __copyright__, __email__, __license__, __version__
+from ._align import Align
+from ._align_getter import align_getter
+from ._column import ColumnDataProperty
+from ._common import MAX_STRICT_LEVEL_MAP, MIN_STRICT_LEVEL_MAP, NOT_QUOTING_FLAGS, DefaultValue
+from ._container import MinMaxContainer
+from ._dataproperty import DataProperty
+from ._extractor import DataPropertyExtractor, DataPropertyMatrix, MatrixFormatting
+from ._formatter import Format
+from ._function import calc_ascii_char_width, get_integer_digit, get_number_of_digit
+from ._line_break import LineBreakHandling
+from ._preprocessor import Preprocessor
+from .logger import set_logger
+
+
+__all__ = (
+    "Align",
+    "align_getter",
+    "ColumnDataProperty",
+    "DataProperty",
+    "DataPropertyExtractor",
+    "DataPropertyMatrix",
+    "Format",
+    "LineBreakHandling",
+    "MatrixFormatting",
+    "MinMaxContainer",
+    "Preprocessor",
+    "calc_ascii_char_width",
+    "get_integer_digit",
+    "get_number_of_digit",
+    "MAX_STRICT_LEVEL_MAP",
+    "MIN_STRICT_LEVEL_MAP",
+    "NOT_QUOTING_FLAGS",
+    "DefaultValue",
+    "set_logger",
+    "__author__",
+    "__copyright__",
+    "__email__",
+    "__license__",
+    "__version__",
+)

venv/lib/python3.10/site-packages/dataproperty/__version__.py

@@ -0,0 +1,6 @@
+__author__ = "Tsuyoshi Hombashi"
+__copyright__ = f"Copyright 2016, {__author__}"
+__license__ = "MIT License"
+__version__ = "1.0.1"
+__maintainer__ = __author__
+__email__ = "[email protected]"

venv/lib/python3.10/site-packages/dataproperty/_align.py

@@ -0,0 +1,25 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+import enum
+
+
+@enum.unique
+class Align(enum.Enum):
+    AUTO = (1 << 0, "auto")
+    LEFT = (1 << 1, "left")
+    RIGHT = (1 << 2, "right")
+    CENTER = (1 << 3, "center")
+
+    @property
+    def align_code(self) -> int:
+        return self.__align_code
+
+    @property
+    def align_string(self) -> str:
+        return self.__align_string
+
+    def __init__(self, code: int, string: str) -> None:
+        self.__align_code = code
+        self.__align_string = string

venv/lib/python3.10/site-packages/dataproperty/_align_getter.py

@@ -0,0 +1,33 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi <[email protected]>
+"""
+
+from typing import Dict
+
+from typepy import Typecode
+
+from ._align import Align
+
+
+class AlignGetter:
+    @property
+    def typecode_align_table(self):
+        raise NotImplementedError()
+
+    @typecode_align_table.setter
+    def typecode_align_table(self, x: Dict[Typecode, Align]) -> None:
+        self.__typecode_align_table = x
+
+    def get_align_from_typecode(self, typecode: Typecode) -> Align:
+        return self.__typecode_align_table.get(typecode, self.default_align)
+
+    def __init__(self) -> None:
+        self.typecode_align_table = {
+            Typecode.STRING: Align.LEFT,
+            Typecode.INTEGER: Align.RIGHT,
+            Typecode.REAL_NUMBER: Align.RIGHT,
+        }
+        self.default_align = Align.LEFT
+
+
+align_getter = AlignGetter()

venv/lib/python3.10/site-packages/dataproperty/_base.py

@@ -0,0 +1,98 @@
+from typing import Dict, Optional, Type
+
+from typepy import (
+    Bool,
+    DateTime,
+    Dictionary,
+    Infinity,
+    Integer,
+    IpAddress,
+    List,
+    Nan,
+    NoneType,
+    NullString,
+    RealNumber,
+    String,
+    Typecode,
+)
+from typepy.type import AbstractType
+
+from ._formatter import Formatter
+from ._interface import DataPeropertyInterface
+
+
+class DataPeropertyBase(DataPeropertyInterface):
+    __slots__ = (
+        "_datetime_format_str",
+        "_decimal_places",
+        "_east_asian_ambiguous_width",
+        "_formatter",
+        "_typecode",
+        "__format_str",
+    )
+
+    __TYPE_CLASS_TABLE: Dict[Typecode, AbstractType] = {
+        Typecode.BOOL: Bool,
+        Typecode.DATETIME: DateTime,
+        Typecode.DICTIONARY: Dictionary,
+        Typecode.INTEGER: Integer,
+        Typecode.INFINITY: Infinity,
+        Typecode.IP_ADDRESS: IpAddress,
+        Typecode.LIST: List,
+        Typecode.NAN: Nan,
+        Typecode.NONE: NoneType,
+        Typecode.NULL_STRING: NullString,
+        Typecode.REAL_NUMBER: RealNumber,
+        Typecode.STRING: String,
+    }
+
+    @property
+    def type_class(self) -> Type[AbstractType]:
+        return self.__TYPE_CLASS_TABLE[self.typecode]
+
+    @property
+    def typecode(self) -> Typecode:
+        """
+        ``typepy.Typecode`` that corresponds to the type of the ``data``.
+
+        :return:
+            One of the Enum value that are defined ``typepy.Typecode``.
+        :rtype: typepy.Typecode
+        """
+
+        assert self._typecode
+
+        return self._typecode
+
+    @property
+    def typename(self) -> str:
+        return self.typecode.name
+
+    def __init__(
+        self,
+        format_flags: Optional[int],
+        is_formatting_float: bool,
+        datetime_format_str: str,
+        east_asian_ambiguous_width: int,
+    ) -> None:
+        self._decimal_places: Optional[int] = None
+        self._east_asian_ambiguous_width = east_asian_ambiguous_width
+        self._typecode: Optional[Typecode] = None
+
+        self._datetime_format_str = datetime_format_str
+        self.__format_str = ""
+
+        self._formatter = Formatter(
+            format_flags=format_flags,
+            datetime_format_str=self._datetime_format_str,
+            is_formatting_float=is_formatting_float,
+        )
+
+    @property
+    def format_str(self) -> str:
+        if self.__format_str:
+            return self.__format_str
+
+        self.__format_str = self._formatter.make_format_str(self.typecode, self.decimal_places)
+
+        return self.__format_str

venv/lib/python3.10/site-packages/dataproperty/_column.py

@@ -0,0 +1,352 @@
+from typing import Any, Dict, List, Optional
+
+from mbstrdecoder import MultiByteStrDecoder
+from typepy import Integer, StrictLevel, Typecode, TypeConversionError
+
+from ._align import Align
+from ._align_getter import align_getter
+from ._base import DataPeropertyBase
+from ._common import DefaultValue
+from ._container import ListContainer, MinMaxContainer
+from ._dataproperty import DataProperty
+from ._function import calc_ascii_char_width
+from .typing import FloatType
+
+
+class ColumnDataProperty(DataPeropertyBase):
+    __slots__ = (
+        "__header_ascii_char_width",
+        "__body_ascii_char_width",
+        "__column_index",
+        "__dp_list",
+        "__float_type",
+        "__format_map",
+        "__is_calculate",
+        "__max_precision",
+        "__minmax_integer_digits",
+        "__minmax_decimal_places",
+        "__minmax_additional_format_len",
+        "__typecode_bitmap",
+    )
+
+    @property
+    def align(self) -> Align:
+        return align_getter.get_align_from_typecode(self.typecode)
+
+    @property
+    def bit_length(self) -> Optional[int]:
+        if self.typecode != Typecode.INTEGER:
+            return None
+
+        bit_length = 0
+        for value_dp in self.__dp_list:
+            try:
+                bit_length = max(bit_length, int.bit_length(value_dp.data))
+            except TypeError:
+                pass
+
+        return bit_length
+
+    @property
+    def column_index(self) -> int:
+        return self.__column_index
+
+    @property
+    def decimal_places(self) -> Optional[int]:
+        return self._decimal_places
+
+    @property
+    def ascii_char_width(self) -> int:
+        return max(self.__header_ascii_char_width, self.__body_ascii_char_width)
+
+    @property
+    def minmax_integer_digits(self) -> MinMaxContainer:
+        return self.__minmax_integer_digits
+
+    @property
+    def minmax_decimal_places(self) -> ListContainer:
+        return self.__minmax_decimal_places
+
+    @property
+    def minmax_additional_format_len(self) -> MinMaxContainer:
+        return self.__minmax_additional_format_len
+
+    def __init__(
+        self,
+        column_index: int,
+        float_type: Optional[FloatType],
+        min_width: int = 0,
+        format_flags: Optional[int] = None,
+        is_formatting_float: bool = True,
+        datetime_format_str: str = DefaultValue.DATETIME_FORMAT,
+        east_asian_ambiguous_width: int = 1,
+        max_precision: int = DefaultValue.MAX_PRECISION,
+    ) -> None:
+        super().__init__(
+            format_flags=format_flags,
+            is_formatting_float=is_formatting_float,
+            datetime_format_str=datetime_format_str,
+            east_asian_ambiguous_width=east_asian_ambiguous_width,
+        )
+
+        self.__header_ascii_char_width = 0
+        self.__body_ascii_char_width = min_width
+        self.__column_index = column_index
+
+        self.__float_type = float_type
+
+        self.__is_calculate = True
+        self.__dp_list: List[DataProperty] = []
+        self.__minmax_integer_digits = MinMaxContainer()
+        self.__minmax_decimal_places = ListContainer()
+        self.__minmax_additional_format_len = MinMaxContainer()
+        self.__max_precision = max_precision
+
+        self.__typecode_bitmap = Typecode.NONE.value
+        self.__calc_typecode_from_bitmap()
+
+        self.__format_map: Dict[Typecode, str] = self._formatter.make_format_map(
+            decimal_places=self._decimal_places
+        )
+
+    def __repr__(self) -> str:
+        element_list = []
+
+        if self.column_index is not None:
+            element_list.append(f"column={self.column_index}")
+
+        element_list.extend(
+            [
+                f"type={self.typename}",
+                f"align={self.align.align_string}",
+                f"ascii_width={self.ascii_char_width}",
+            ]
+        )
+
+        if Integer(self.bit_length).is_type():
+            element_list.append(f"bit_len={self.bit_length}")
+
+        if self.minmax_integer_digits.has_value():
+            if self.minmax_integer_digits.is_same_value():
+                value = f"int_digits={self.minmax_integer_digits.min_value}"
+            else:
+                value = f"int_digits=({self.minmax_integer_digits})"
+
+            element_list.append(value)
+
+        if self.minmax_decimal_places.has_value():
+            if self.minmax_decimal_places.is_same_value():
+                value = f"decimal_places={self.minmax_decimal_places.min_value}"
+            else:
+                value = f"decimal_places=({self.minmax_decimal_places})"
+
+            element_list.append(value)
+
+        if not self.minmax_additional_format_len.is_zero():
+            if self.minmax_additional_format_len.is_same_value():
+                value = f"extra_len={self.minmax_additional_format_len.min_value}"
+            else:
+                value = f"extra_len=({self.minmax_additional_format_len})"
+
+            element_list.append(value)
+
+        return ", ".join(element_list)
+
+    def dp_to_str(self, value_dp: DataProperty) -> str:
+        if value_dp.typecode == Typecode.STRING:
+            return str(value_dp.data)
+
+        try:
+            value = self.__preprocess_value_before_tostring(value_dp)
+        except TypeConversionError:
+            return self.__format_map.get(value_dp.typecode, "{:s}").format(value_dp.data)
+
+        to_string_format_str = self.__get_tostring_format(value_dp)
+
+        try:
+            return to_string_format_str.format(value)
+        except (ValueError, TypeError):
+            pass
+
+        try:
+            return MultiByteStrDecoder(value).unicode_str
+        except ValueError:
+            pass
+
+        return str(value)
+
+    def extend_width(self, ascii_char_width: int) -> None:
+        self.extend_header_width(ascii_char_width)
+        self.extend_body_width(ascii_char_width)
+
+    def extend_header_width(self, ascii_char_width: int) -> None:
+        self.__header_ascii_char_width += ascii_char_width
+
+    def extend_body_width(self, ascii_char_width: int) -> None:
+        self.__body_ascii_char_width += ascii_char_width
+
+    def update_header(self, header_db: DataProperty) -> None:
+        self.__header_ascii_char_width = header_db.ascii_char_width
+
+    def update_body(self, value_dp: DataProperty) -> None:
+        if value_dp.is_include_ansi_escape:
+            assert value_dp.no_ansi_escape_dp
+            value_dp = value_dp.no_ansi_escape_dp
+
+        self.__typecode_bitmap |= value_dp.typecode.value
+        self.__calc_typecode_from_bitmap()
+
+        if value_dp.typecode in (Typecode.REAL_NUMBER, Typecode.INTEGER):
+            self.__minmax_integer_digits.update(value_dp.integer_digits)
+            self.__minmax_decimal_places.update(value_dp.decimal_places)
+            self.__update_decimal_places()
+
+        self.__minmax_additional_format_len.update(value_dp.additional_format_len)
+
+        self.__dp_list.append(value_dp)
+        self.__update_ascii_char_width()
+
+    def merge(self, column_dp: "ColumnDataProperty") -> None:
+        self.__typecode_bitmap |= column_dp.typecode.value
+        self.__calc_typecode_from_bitmap()
+
+        self.__minmax_integer_digits.merge(column_dp.minmax_integer_digits)
+        self.__minmax_decimal_places.merge(column_dp.minmax_decimal_places)
+        self.__update_decimal_places()
+
+        self.__minmax_additional_format_len.merge(column_dp.minmax_additional_format_len)
+
+        self.__body_ascii_char_width = max(self.__body_ascii_char_width, column_dp.ascii_char_width)
+        self.__update_ascii_char_width()
+
+    def begin_update(self) -> None:
+        self.__is_calculate = False
+
+    def end_update(self) -> None:
+        self.__is_calculate = True
+
+        self.__calc_typecode_from_bitmap()
+        self.__update_decimal_places()
+        self.__update_ascii_char_width()
+
+    def __is_not_single_typecode(self, typecode_bitmap: int) -> bool:
+        return bool(
+            self.__typecode_bitmap & typecode_bitmap and self.__typecode_bitmap & ~typecode_bitmap
+        )
+
+    def __is_float_typecode(self) -> bool:
+        FLOAT_TYPECODE_BMP = (
+            Typecode.REAL_NUMBER.value | Typecode.INFINITY.value | Typecode.NAN.value
+        )
+        NUMBER_TYPECODE_BMP = FLOAT_TYPECODE_BMP | Typecode.INTEGER.value
+
+        if self.__is_not_single_typecode(NUMBER_TYPECODE_BMP | Typecode.NULL_STRING.value):
+            return False
+
+        if (
+            bin(self.__typecode_bitmap & (FLOAT_TYPECODE_BMP | Typecode.NULL_STRING.value)).count(
+                "1"
+            )
+            >= 2
+        ):
+            return True
+
+        if bin(self.__typecode_bitmap & NUMBER_TYPECODE_BMP).count("1") >= 2:
+            return True
+
+        return False
+
+    def __calc_body_ascii_char_width(self) -> int:
+        width_list = [self.__body_ascii_char_width]
+
+        for value_dp in self.__dp_list:
+            if value_dp.is_include_ansi_escape:
+                assert value_dp.no_ansi_escape_dp
+                value_dp = value_dp.no_ansi_escape_dp
+
+            width_list.append(
+                calc_ascii_char_width(self.dp_to_str(value_dp), self._east_asian_ambiguous_width)
+            )
+
+        return max(width_list)
+
+    def __calc_decimal_places(self) -> Optional[int]:
+        if self.minmax_decimal_places.max_value is None:
+            return None
+
+        return min(self.__max_precision, int(self.minmax_decimal_places.max_value))
+
+    def __get_tostring_format(self, value_dp: DataProperty) -> str:
+        if self.typecode == Typecode.STRING:
+            return self.__format_map.get(value_dp.typecode, "{:s}")
+
+        return self.__format_map.get(self.typecode, "{:s}")
+
+    def __get_typecode_from_bitmap(self) -> Typecode:
+        if self.__is_float_typecode():
+            return Typecode.REAL_NUMBER
+
+        if any(
+            [
+                self.__is_not_single_typecode(Typecode.BOOL.value),
+                self.__is_not_single_typecode(Typecode.DATETIME.value),
+            ]
+        ):
+            return Typecode.STRING
+
+        typecode_list = [
+            Typecode.STRING,
+            Typecode.REAL_NUMBER,
+            Typecode.INTEGER,
+            Typecode.DATETIME,
+            Typecode.DICTIONARY,
+            Typecode.IP_ADDRESS,
+            Typecode.LIST,
+            Typecode.BOOL,
+            Typecode.INFINITY,
+            Typecode.NAN,
+            Typecode.NULL_STRING,
+        ]
+
+        for typecode in typecode_list:
+            if self.__typecode_bitmap & typecode.value:
+                return typecode
+
+        if self.__typecode_bitmap == Typecode.NONE.value:
+            return Typecode.NONE
+
+        return Typecode.STRING
+
+    def __update_ascii_char_width(self) -> None:
+        if not self.__is_calculate:
+            return
+
+        self.__body_ascii_char_width = self.__calc_body_ascii_char_width()
+
+    def __update_decimal_places(self) -> None:
+        if not self.__is_calculate:
+            return
+
+        self._decimal_places = self.__calc_decimal_places()
+        self.__format_map = self._formatter.make_format_map(decimal_places=self._decimal_places)
+
+    def __calc_typecode_from_bitmap(self) -> None:
+        if not self.__is_calculate:
+            return
+
+        self._typecode = self.__get_typecode_from_bitmap()
+
+    def __preprocess_value_before_tostring(self, value_dp: DataProperty) -> Any:
+        if self.typecode == value_dp.typecode or self.typecode in [
+            Typecode.STRING,
+            Typecode.BOOL,
+            Typecode.DATETIME,
+        ]:
+            return value_dp.data
+
+        return self.type_class(
+            value_dp.data,
+            strict_level=StrictLevel.MIN,
+            float_type=self.__float_type,
+            strip_ansi_escape=False,
+        ).convert()