mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 21:00:08 +06:00
bb6f6d5338
* First draft * Improve conversion script * Make vision encoder work * More improvements * Improve conversion script * Fix quality * Add MultiframeIntegrationTransformer * More improvements * Make MiT output work * Fix quality * Add prompts generator * Add tests * Fix some tests * Fix some more tests * Fix more tests * Improve conversion script * Fix model outputs * Fix more tests * Add XClipProcessor * Use processor in conversion script * Fix integration test * Update README, fix docs * Fix all tests * Add MIT output to XClipOutput * Create better variable names * Rename XClip to XCLIP * Extend conversion script * Add support for large models * Add support for 16 frame models * Add another model' * Fix module issue * Apply suggestions from code review * Add figure to docs * Fix CLIPProcessor issue * Apply suggestions from code review * Delete file * Convert more checkpoints * Convert last checkpoint * Update nielsr to microsoft
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib
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import inspect
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import os
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import re
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# All paths are set with the intent you should run this script from the root of the repo with the command
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# python utils/check_config_docstrings.py
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PATH_TO_TRANSFORMERS = "src/transformers"
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# This is to make sure the transformers module imported is the one in the repo.
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spec = importlib.util.spec_from_file_location(
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"transformers",
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os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
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submodule_search_locations=[PATH_TO_TRANSFORMERS],
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)
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transformers = spec.loader.load_module()
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CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING
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# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
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# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
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_re_checkpoint = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
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CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
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"CLIPConfig",
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"OwlViTConfig",
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"GroupViTConfig",
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"DecisionTransformerConfig",
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"EncoderDecoderConfig",
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"RagConfig",
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"SpeechEncoderDecoderConfig",
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"VisionEncoderDecoderConfig",
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"VisionTextDualEncoderConfig",
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"XCLIPConfig",
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}
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def check_config_docstrings_have_checkpoints():
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configs_without_checkpoint = []
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for config_class in list(CONFIG_MAPPING.values()):
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checkpoint_found = False
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# source code of `config_class`
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config_source = inspect.getsource(config_class)
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checkpoints = _re_checkpoint.findall(config_source)
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for checkpoint in checkpoints:
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# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
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# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
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ckpt_name, ckpt_link = checkpoint
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# verify the checkpoint name corresponds to the checkpoint link
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ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
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if ckpt_link == ckpt_link_from_name:
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checkpoint_found = True
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break
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name = config_class.__name__
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if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
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configs_without_checkpoint.append(name)
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if len(configs_without_checkpoint) > 0:
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message = "\n".join(sorted(configs_without_checkpoint))
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raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}")
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if __name__ == "__main__":
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check_config_docstrings_have_checkpoints()
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