2021-12-14 00:46:03 +08:00
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# coding=utf-8
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# Copyright 2021 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 argparse
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import collections
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import importlib.util
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import os
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import re
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import tempfile
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import pandas as pd
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from datasets import Dataset
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from huggingface_hub import Repository
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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/update_metadata.py
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TRANSFORMERS_PATH = "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(TRANSFORMERS_PATH, "__init__.py"),
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submodule_search_locations=[TRANSFORMERS_PATH],
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)
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transformers_module = spec.loader.load_module()
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# Regexes that match TF/Flax/PT model names.
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_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
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_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
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# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
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_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
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# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
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PIPELINE_TAGS_AND_AUTO_MODELS = [
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("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
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("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
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("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
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("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
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("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
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("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
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("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
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("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
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("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
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2022-10-07 22:00:19 +08:00
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(
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"zero-shot-object-detection",
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"MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
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"AutoModelForZeroShotObjectDetection",
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),
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2021-12-14 00:46:03 +08:00
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("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
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("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
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("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
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("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
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(
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"table-question-answering",
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"MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
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"AutoModelForTableQuestionAnswering",
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),
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("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
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("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
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(
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"next-sentence-prediction",
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"MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
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"AutoModelForNextSentencePrediction",
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),
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2021-12-17 00:22:14 +08:00
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(
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"audio-frame-classification",
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"MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
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"AutoModelForAudioFrameClassification",
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),
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("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
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2022-09-14 21:25:15 +08:00
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(
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"document-question-answering",
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"MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
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"AutoModelForDocumentQuestionAnswering",
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),
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2022-09-15 02:06:49 +08:00
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(
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"visual-question-answering",
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"MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
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"AutoModelForVisualQuestionAnswering",
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),
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("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
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2022-09-16 21:40:38 +08:00
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(
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"zero-shot-image-classification",
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"_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
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"AutoModel",
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),
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2022-10-12 20:54:20 +08:00
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("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
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2022-12-09 05:22:43 +08:00
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("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
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2021-12-14 00:46:03 +08:00
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]
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# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
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def camel_case_split(identifier):
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"Split a camelcased `identifier` into words."
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matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
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return [m.group(0) for m in matches]
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def get_frameworks_table():
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"""
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Generates a dataframe containing the supported auto classes for each model type, using the content of the auto
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modules.
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"""
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# Dictionary model names to config.
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config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
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model_prefix_to_model_type = {
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config.replace("Config", ""): model_type for model_type, config in config_maping_names.items()
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}
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# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
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pt_models = collections.defaultdict(bool)
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tf_models = collections.defaultdict(bool)
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flax_models = collections.defaultdict(bool)
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# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
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for attr_name in dir(transformers_module):
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lookup_dict = None
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if _re_tf_models.match(attr_name) is not None:
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lookup_dict = tf_models
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attr_name = _re_tf_models.match(attr_name).groups()[0]
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elif _re_flax_models.match(attr_name) is not None:
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lookup_dict = flax_models
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attr_name = _re_flax_models.match(attr_name).groups()[0]
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elif _re_pt_models.match(attr_name) is not None:
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lookup_dict = pt_models
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attr_name = _re_pt_models.match(attr_name).groups()[0]
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if lookup_dict is not None:
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while len(attr_name) > 0:
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if attr_name in model_prefix_to_model_type:
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lookup_dict[model_prefix_to_model_type[attr_name]] = True
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break
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# Try again after removing the last word in the name
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attr_name = "".join(camel_case_split(attr_name)[:-1])
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all_models = set(list(pt_models.keys()) + list(tf_models.keys()) + list(flax_models.keys()))
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all_models = list(all_models)
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all_models.sort()
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data = {"model_type": all_models}
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data["pytorch"] = [pt_models[t] for t in all_models]
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data["tensorflow"] = [tf_models[t] for t in all_models]
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data["flax"] = [flax_models[t] for t in all_models]
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# Now let's use the auto-mapping names to make sure
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processors = {}
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for t in all_models:
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if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
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processors[t] = "AutoProcessor"
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elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
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processors[t] = "AutoTokenizer"
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elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
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processors[t] = "AutoFeatureExtractor"
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else:
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# Default to AutoTokenizer if a model has nothing, for backward compatibility.
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processors[t] = "AutoTokenizer"
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data["processor"] = [processors[t] for t in all_models]
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return pd.DataFrame(data)
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def update_pipeline_and_auto_class_table(table):
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"""
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Update the table of model class to (pipeline_tag, auto_class) without removing old keys if they don't exist
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anymore.
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"""
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auto_modules = [
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transformers_module.models.auto.modeling_auto,
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transformers_module.models.auto.modeling_tf_auto,
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transformers_module.models.auto.modeling_flax_auto,
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]
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for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
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model_mappings = [model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
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auto_classes = [auto_class, f"TF_{auto_class}", f"Flax_{auto_class}"]
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# Loop through all three frameworks
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for module, cls, mapping in zip(auto_modules, auto_classes, model_mappings):
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# The type of pipeline may not exist in this framework
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if not hasattr(module, mapping):
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continue
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# First extract all model_names
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model_names = []
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for name in getattr(module, mapping).values():
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if isinstance(name, str):
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model_names.append(name)
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else:
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model_names.extend(list(name))
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# Add pipeline tag and auto model class for those models
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table.update({model_name: (pipeline_tag, cls) for model_name in model_names})
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return table
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def update_metadata(token, commit_sha):
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"""
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Update the metada for the Transformers repo.
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"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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repo = Repository(
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tmp_dir, clone_from="huggingface/transformers-metadata", repo_type="dataset", use_auth_token=token
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)
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frameworks_table = get_frameworks_table()
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frameworks_dataset = Dataset.from_pandas(frameworks_table)
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frameworks_dataset.to_json(os.path.join(tmp_dir, "frameworks.json"))
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tags_dataset = Dataset.from_json(os.path.join(tmp_dir, "pipeline_tags.json"))
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table = {
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tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
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for i in range(len(tags_dataset))
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}
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table = update_pipeline_and_auto_class_table(table)
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# Sort the model classes to avoid some nondeterministic updates to create false update commits.
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model_classes = sorted(list(table.keys()))
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tags_table = pd.DataFrame(
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{
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"model_class": model_classes,
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"pipeline_tag": [table[m][0] for m in model_classes],
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"auto_class": [table[m][1] for m in model_classes],
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}
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)
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tags_dataset = Dataset.from_pandas(tags_table)
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tags_dataset.to_json(os.path.join(tmp_dir, "pipeline_tags.json"))
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if repo.is_repo_clean():
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print("Nothing to commit!")
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else:
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2021-12-22 00:17:11 +08:00
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if commit_sha is not None:
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commit_message = (
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f"Update with commit {commit_sha}\n\nSee: "
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f"https://github.com/huggingface/transformers/commit/{commit_sha}"
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)
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else:
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commit_message = "Update"
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2021-12-14 00:46:03 +08:00
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repo.push_to_hub(commit_message)
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2022-09-15 02:06:49 +08:00
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def check_pipeline_tags():
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in_table = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
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pipeline_tasks = transformers_module.pipelines.SUPPORTED_TASKS
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missing = []
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for key in pipeline_tasks:
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if key not in in_table:
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model = pipeline_tasks[key]["pt"]
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if isinstance(model, (list, tuple)):
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model = model[0]
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model = model.__name__
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if model not in in_table.values():
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missing.append(key)
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if len(missing) > 0:
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msg = ", ".join(missing)
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raise ValueError(
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"The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
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f"`utils/update_metadata.py`: {msg}. Please add them!"
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)
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2021-12-14 00:46:03 +08:00
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
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parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
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2022-09-15 02:06:49 +08:00
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parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
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2021-12-14 00:46:03 +08:00
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args = parser.parse_args()
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2022-09-15 02:06:49 +08:00
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if args.check_only:
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check_pipeline_tags()
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else:
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update_metadata(args.token, args.commit_sha)
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