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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
TRANSFORMERS_PATH = "src/transformers"
PATH_TO_TASK_GUIDES = "docs/source/en/tasks"
def _find_text_in_file(filename, start_prompt, end_prompt):
"""
Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
lines.
"""
with open(filename, "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Find the start prompt.
start_index = 0
while not lines[start_index].startswith(start_prompt):
start_index += 1
start_index += 1
end_index = start_index
while not lines[end_index].startswith(end_prompt):
end_index += 1
end_index -= 1
while len(lines[start_index]) <= 1:
start_index += 1
while len(lines[end_index]) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index]), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
transformers_module = direct_transformers_import(TRANSFORMERS_PATH)
TASK_GUIDE_TO_MODELS = {
"asr.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
"audio_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"language_modeling.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"image_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"masked_language_modeling.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"multiple_choice.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"object_detection.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"question_answering.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"semantic_segmentation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"sequence_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"summarization.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"token_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"translation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"video_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
"document_question_answering.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"monocular_depth_estimation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
SPECIAL_TASK_GUIDE_TO_MODEL_TYPES = {
"summarization.mdx": ("nllb",),
"translation.mdx": ("nllb",),
}
def get_model_list_for_task(task_guide):
"""
Return the list of models supporting given task.
"""
model_maping_names = TASK_GUIDE_TO_MODELS[task_guide]
special_model_types = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(task_guide, set())
model_names = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]) + "\n"
def check_model_list_for_task(task_guide, overwrite=False):
"""For a given task guide, checks the model list in the generated tip for consistency with the state of the lib and overwrites if needed."""
current_list, start_index, end_index, lines = _find_text_in_file(
filename=os.path.join(PATH_TO_TASK_GUIDES, task_guide),
start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->",
end_prompt="<!--End of the generated tip-->",
)
new_list = get_model_list_for_task(task_guide)
if current_list != new_list:
if overwrite:
with open(os.path.join(PATH_TO_TASK_GUIDES, task_guide), "w", encoding="utf-8", newline="\n") as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
else:
raise ValueError(
f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
" to fix this."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
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