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+2024-05-14 10:31:16,988 INFO    MainThread:8782 [wandb_setup.py:_flush():76] Configure stats pid to 8782
+2024-05-14 10:31:16,988 INFO    MainThread:8782 [wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings
+2024-05-14 10:31:16,988 INFO    MainThread:8782 [wandb_setup.py:_flush():76] Loading settings from /data/cronscript/lm-evaluation-harness/wandb/settings
+2024-05-14 10:31:16,988 INFO    MainThread:8782 [wandb_setup.py:_flush():76] Loading settings from environment variables: {}
+2024-05-14 10:31:16,988 INFO    MainThread:8782 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False}
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lm-evaluation-harness/wandb/run-20240514_163703-yk7p3xk2/files/output.log

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+
+2024-05-14:16:37:04,170 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:16:37:08,704 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:16:37:08,706 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:16:37:08,706 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step20'}
+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 369, in cached_file
+    raise EnvironmentError(
+OSError: /data/cronscript/ckpts//hf_ckpt//global_step20 does not appear to have a file named config.json. Checkout 'https://huggingface.co//data/cronscript/ckpts//hf_ckpt//global_step20/tree/main' for available files.

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+      task: indiccopa-hi
+      group: ai4bharat/IndicCOPA
+      dataset_path: ai4bharat/IndicCOPA
+      dataset_name: translation-hi
+      test_split: test
+      doc_to_text: 'functools.partial(<function doc_to_text at 0x7f7bb223d090>, connector={''cause'':
+        ''कारण'', ''effect'': ''परिणाम''})'
+      doc_to_target: label
+      doc_to_choice: "def doc_to_choice(doc):\n    return [convert_choice(doc[\"choice1\"\
+        ]), convert_choice(doc[\"choice2\"])]\n"
+      description: ''
+      target_delimiter: ' '
+      fewshot_delimiter: '
+
+
+        '
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+      metric_list:
+      - metric: acc
+      output_type: multiple_choice
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+    - 64
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lm-evaluation-harness/wandb/run-20240514_164351-tmiu4fex/files/media/table/evaluation/eval_results_1_c78c57917f215d296d9d.table.json

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+{"columns": ["Tasks", "Version", "Filter", "num_fewshot", "Metric", "Value", "Stderr"], "data": [["indiccopa-hi", 1.0, "none", 0, "acc", "0.534521158129176", "0.0236"]]}

lm-evaluation-harness/wandb/run-20240514_164351-tmiu4fex/files/output.log

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+
+2024-05-14:16:43:51,903 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:16:43:56,544 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:16:43:56,546 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:16:43:56,546 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/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
+2024-05-14:16:44:05,302 INFO     [huggingface.py:334] Using 8 devices with data parallelism
+2024-05-14:16:44:05,304 WARNING  [task.py:763] [Task: indiccopa-hi] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-14:16:44:05,304 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 16:44:04,894] [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(
+2024-05-14:16:44:06,927 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.
+2024-05-14:16:44:06,927 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.
+2024-05-14:16:44:06,948 INFO     [task.py:395] Building contexts for indiccopa-hi on rank 0...
+100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 57/57 [00:00<00:00, 102256.34it/s]
+Passed argument batch_size = auto:1. Detecting largest batch size
+2024-05-14:16:44:08,634 INFO     [evaluator.py:379] Running loglikelihood requests
+Running loglikelihood requests:   0%|                                                                                                                        | 0/114 [00:00<?, ?it/s]/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
+
+Running loglikelihood requests: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 114/114 [00:07<00:00, 15.89it/s]
+hf (pretrained=/data/cronscript/ckpts//hf_ckpt//global_step100), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (64)
+|   Tasks    |Version|Filter|n-shot|Metric|Value |   |Stderr|
+|------------|------:|------|-----:|------|-----:|---|-----:|
+|indiccopa-hi|      1|none  |     0|acc   |0.5345|±  |0.0236|

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+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: 1716391547
+    t:
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lm-evaluation-harness/wandb/run-20240522_152547-0o4oiazb/files/output.log

@@ -0,0 +1,34 @@
+
+2024-05-22:15:25:47,742 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-22:15:25:56,262 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-22:15:25:56,263 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-22:15:25:56,263 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step100'}
+2024-05-22:15:25:58,822 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_152547-0o4oiazb/files/requirements.txt

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+GitPython==3.1.43
+Jinja2==3.1.4
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+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
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+evaluate==0.4.2
+exceptiongroup==1.2.1
+expecttest==0.2.1
+filelock==3.14.0
+frozenlist==1.4.1
+fsspec==2024.3.1
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+google-auth==2.29.0
+grpcio==1.63.0
+habana-media-loader==1.15.1.15
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+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
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+lm_eval==0.4.2
+lm_eval==0.4.2
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+mbstrdecoder==1.1.3
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+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
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+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
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+tcolorpy==0.1.6
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+tensorboard==2.11.2
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+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
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+typepy==1.3.2
+typing_extensions==4.11.0
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+xxhash==3.4.1
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lm-evaluation-harness/wandb/run-20240522_152547-0o4oiazb/files/wandb-metadata.json

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venv/lib/python3.10/site-packages/transformers/models/data2vec/__init__.py

@@ -0,0 +1,135 @@
+# Copyright 2022 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.
+
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
+    "configuration_data2vec_text": [
+        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "Data2VecTextConfig",
+        "Data2VecTextOnnxConfig",
+    ],
+    "configuration_data2vec_vision": [
+        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "Data2VecVisionConfig",
+        "Data2VecVisionOnnxConfig",
+    ],
+}
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_data2vec_audio"] = [
+        "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "Data2VecAudioForAudioFrameClassification",
+        "Data2VecAudioForCTC",
+        "Data2VecAudioForSequenceClassification",
+        "Data2VecAudioForXVector",
+        "Data2VecAudioModel",
+        "Data2VecAudioPreTrainedModel",
+    ]
+    _import_structure["modeling_data2vec_text"] = [
+        "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "Data2VecTextForCausalLM",
+        "Data2VecTextForMaskedLM",
+        "Data2VecTextForMultipleChoice",
+        "Data2VecTextForQuestionAnswering",
+        "Data2VecTextForSequenceClassification",
+        "Data2VecTextForTokenClassification",
+        "Data2VecTextModel",
+        "Data2VecTextPreTrainedModel",
+    ]
+    _import_structure["modeling_data2vec_vision"] = [
+        "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "Data2VecVisionForImageClassification",
+        "Data2VecVisionForMaskedImageModeling",
+        "Data2VecVisionForSemanticSegmentation",
+        "Data2VecVisionModel",
+        "Data2VecVisionPreTrainedModel",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_data2vec_vision"] = [
+        "TFData2VecVisionForImageClassification",
+        "TFData2VecVisionForSemanticSegmentation",
+        "TFData2VecVisionModel",
+        "TFData2VecVisionPreTrainedModel",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_data2vec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, Data2VecAudioConfig
+    from .configuration_data2vec_text import (
+        DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        Data2VecTextConfig,
+        Data2VecTextOnnxConfig,
+    )
+    from .configuration_data2vec_vision import (
+        DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        Data2VecVisionConfig,
+        Data2VecVisionOnnxConfig,
+    )
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_data2vec_audio import (
+            DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Data2VecAudioForAudioFrameClassification,
+            Data2VecAudioForCTC,
+            Data2VecAudioForSequenceClassification,
+            Data2VecAudioForXVector,
+            Data2VecAudioModel,
+            Data2VecAudioPreTrainedModel,
+        )
+        from .modeling_data2vec_text import (
+            DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Data2VecTextForCausalLM,
+            Data2VecTextForMaskedLM,
+            Data2VecTextForMultipleChoice,
+            Data2VecTextForQuestionAnswering,
+            Data2VecTextForSequenceClassification,
+            Data2VecTextForTokenClassification,
+            Data2VecTextModel,
+            Data2VecTextPreTrainedModel,
+        )
+        from .modeling_data2vec_vision import (
+            DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Data2VecVisionForImageClassification,
+            Data2VecVisionForMaskedImageModeling,
+            Data2VecVisionForSemanticSegmentation,
+            Data2VecVisionModel,
+            Data2VecVisionPreTrainedModel,
+        )
+    if is_tf_available():
+        from .modeling_tf_data2vec_vision import (
+            TFData2VecVisionForImageClassification,
+            TFData2VecVisionForSemanticSegmentation,
+            TFData2VecVisionModel,
+            TFData2VecVisionPreTrainedModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

venv/lib/python3.10/site-packages/transformers/models/data2vec/configuration_data2vec_audio.py

@@ -0,0 +1,285 @@
+# coding=utf-8
+# Copyright 2022 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.
+""" Data2VecText configuration"""
+
+import math
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Data2VecAudioConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Data2VecAudioModel`]. It is used to instantiate
+    an Data2VecAudio model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the Data2VecAudio
+    [facebook/data2vec-audio-base-960h](https://huggingface.co/facebook/data2vec-audio-base-960h) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32):
+            Vocabulary size of the Data2VecAudio model. Defines the number of different tokens that can be represented
+            by the `inputs_ids` passed when calling [`Data2VecAudioModel`] or [`TFData2VecAudioModel`]. Vocabulary size
+            of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the
+            forward method of [`Data2VecAudioModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        activation_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for activations inside the fully connected layer.
+        attention_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        final_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for the final projection layer of [`Data2VecAudioForCTC`].
+        layerdrop (`float`, *optional*, defaults to 0.1):
+            The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
+            details.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        feat_proj_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for output of the feature encoder.
+        feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the 1D convolutional layers of the feature
+            extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
+            A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
+            feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
+        conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
+            A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
+            of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
+        conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
+            A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
+            length of *conv_kernel* defines the number of convolutional layers and has to match the length of
+            *conv_dim*.
+        conv_bias (`bool`, *optional*, defaults to `False`):
+            Whether the 1D convolutional layers have a bias.
+        num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
+            Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
+            embeddings layer.
+        num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
+            Number of groups of 1D convolutional positional embeddings layer.
+        mask_time_prob (`float`, *optional*, defaults to 0.05):
+            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
+            procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
+            reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
+            masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
+        mask_time_length (`int`, *optional*, defaults to 10):
+            Length of vector span along the time axis.
+        mask_time_min_masks (`int`, *optional*, defaults to 2),:
+            The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
+            irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
+            mask_time_min_masks''
+        mask_feature_prob (`float`, *optional*, defaults to 0.0):
+            Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
+            masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
+            the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
+            span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
+            may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
+            True`.
+        mask_feature_length (`int`, *optional*, defaults to 10):
+            Length of vector span along the feature axis.
+        mask_feature_min_masks (`int`, *optional*, defaults to 0),:
+            The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
+            step, irrespectively of `mask_feature_prob`. Only relevant if
+            ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
+        ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
+            Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
+            instance of [`Data2VecAudioForCTC`].
+        ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
+            Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
+            occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
+            of [`Data2VecAudioForCTC`].
+        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
+            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
+            instance of [`Data2VecAudioForSequenceClassification`].
+        classifier_proj_size (`int`, *optional*, defaults to 256):
+            Dimensionality of the projection before token mean-pooling for classification.
+        tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`):
+            A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN*
+            module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers.
+        tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
+            A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
+            *XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
+        tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`):
+            A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the
+            *XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*.
+        xvector_output_dim (`int`, *optional*, defaults to 512):
+            Dimensionality of the *XVector* embedding vectors.
+        add_adapter (`bool`, *optional*, defaults to `False`):
+            Whether a convolutional network should be stacked on top of the Data2VecAudio Encoder. Can be very useful
+            for warm-starting Data2VecAudio for SpeechEncoderDecoder models.
+        adapter_kernel_size (`int`, *optional*, defaults to 3):
+            Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
+        adapter_stride (`int`, *optional*, defaults to 2):
+            Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
+        num_adapter_layers (`int`, *optional*, defaults to 3):
+            Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
+            True`.
+        output_hidden_size (`int`, *optional*):
+            Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
+            if `add_adapter is True`.
+
+    Example:
+
+    ```python
+    >>> from transformers import Data2VecAudioConfig, Data2VecAudioModel
+
+    >>> # Initializing a Data2VecAudio facebook/data2vec-audio-base-960h style configuration
+    >>> configuration = Data2VecAudioConfig()
+
+    >>> # Initializing a model (with random weights) from the facebook/data2vec-audio-base-960h style configuration
+    >>> model = Data2VecAudioModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "data2vec-audio"
+
+    def __init__(
+        self,
+        vocab_size=32,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout=0.1,
+        activation_dropout=0.1,
+        attention_dropout=0.1,
+        feat_proj_dropout=0.0,
+        final_dropout=0.1,
+        layerdrop=0.1,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        feat_extract_activation="gelu",
+        conv_dim=(512, 512, 512, 512, 512, 512, 512),
+        conv_stride=(5, 2, 2, 2, 2, 2, 2),
+        conv_kernel=(10, 3, 3, 3, 3, 2, 2),
+        conv_bias=False,
+        num_conv_pos_embedding_groups=16,
+        conv_pos_kernel_size=19,
+        num_conv_pos_embeddings=5,
+        mask_time_prob=0.05,
+        mask_time_length=10,
+        mask_time_min_masks=2,
+        mask_feature_prob=0.0,
+        mask_feature_length=10,
+        mask_feature_min_masks=0,
+        ctc_loss_reduction="sum",
+        ctc_zero_infinity=False,
+        use_weighted_layer_sum=False,
+        classifier_proj_size=256,
+        tdnn_dim=(512, 512, 512, 512, 1500),
+        tdnn_kernel=(5, 3, 3, 1, 1),
+        tdnn_dilation=(1, 2, 3, 1, 1),
+        xvector_output_dim=512,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        add_adapter=False,
+        adapter_kernel_size=3,
+        adapter_stride=2,
+        num_adapter_layers=3,
+        output_hidden_size=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
+        self.hidden_size = hidden_size
+        self.feat_extract_activation = feat_extract_activation
+        self.conv_dim = list(conv_dim)
+        self.conv_stride = list(conv_stride)
+        self.conv_kernel = list(conv_kernel)
+        self.conv_bias = conv_bias
+        self.num_conv_pos_embeddings = num_conv_pos_embeddings
+        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
+        self.conv_pos_kernel_size = conv_pos_kernel_size
+        self.num_feat_extract_layers = len(self.conv_dim)
+        self.num_hidden_layers = num_hidden_layers
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.num_attention_heads = num_attention_heads
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.feat_proj_dropout = feat_proj_dropout
+        self.final_dropout = final_dropout
+        self.layerdrop = layerdrop
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        self.vocab_size = vocab_size
+        self.use_weighted_layer_sum = use_weighted_layer_sum
+
+        if (
+            (len(self.conv_stride) != self.num_feat_extract_layers)
+            or (len(self.conv_kernel) != self.num_feat_extract_layers)
+            or (len(self.conv_dim) != self.num_feat_extract_layers)
+        ):
+            raise ValueError(
+                "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
+                " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
+                f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
+                f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
+            )
+
+        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
+        self.mask_time_prob = mask_time_prob
+        self.mask_time_length = mask_time_length
+        self.mask_time_min_masks = mask_time_min_masks
+        self.mask_feature_prob = mask_feature_prob
+        self.mask_feature_length = mask_feature_length
+        self.mask_feature_min_masks = mask_feature_min_masks
+
+        # ctc loss
+        self.ctc_loss_reduction = ctc_loss_reduction
+        self.ctc_zero_infinity = ctc_zero_infinity
+
+        # adapter
+        self.add_adapter = add_adapter
+        self.adapter_kernel_size = adapter_kernel_size
+        self.adapter_stride = adapter_stride
+        self.num_adapter_layers = num_adapter_layers
+        self.output_hidden_size = output_hidden_size or hidden_size
+
+        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
+        self.classifier_proj_size = classifier_proj_size
+
+        # XVector-specific parameters. Feel free to ignore for other classes.
+        self.tdnn_dim = list(tdnn_dim)
+        self.tdnn_kernel = list(tdnn_kernel)
+        self.tdnn_dilation = list(tdnn_dilation)
+        self.xvector_output_dim = xvector_output_dim
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return math.prod(self.conv_stride)