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101
docs/source/en/internal/import_utils.md
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101
docs/source/en/internal/import_utils.md
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# Import Utilities
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This page goes through the transformers utilities to enable lazy and fast object import.
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While we strive for minimal dependencies, some models have specific dependencies requirements that cannot be
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worked around. We don't want for all users of `transformers` to have to install those dependencies to use other models,
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we therefore mark those as soft dependencies rather than hard dependencies.
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The transformers toolkit is not made to error-out on import of a model that has a specific dependency; instead, an
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object for which you are lacking a dependency will error-out when calling any method on it. As an example, if
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`torchvision` isn't installed, the fast image processors will not be available.
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This object is still importable:
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```python
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>>> from transformers import DetrImageProcessor
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>>> print(DetrImageProcessor)
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<class 'DetrImageProcessor'>
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```
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However, no method can be called on that object:
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```python
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>>> DetrImageProcessor.from_pretrained()
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ImportError:
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DetrImageProcessor requires the Torchvision library but it was not found in your environment. Check out the instructions on the
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installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
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Please note that you may need to restart your runtime after installation.
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```
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Let's see how to specify specific object dependencies.
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## Specifying Object Dependencies
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### Filename-based
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All objects under a given filename have an automatic dependency to the tool linked to the filename
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**PyTorch**: All files starting with `modeling_` have an automatic PyTorch dependency
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**Tokenizers**: All files starting with `tokenization_` and ending with `_fast` have an automatic `tokenizers` dependency
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**Vision**: All files starting with `image_processing_` have an automatic dependency to the `vision` dependency group;
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at the time of writing, this only contains the `pillow` dependency.
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**Vision + Torch + Torchvision**: All files starting with `image_processing_` and ending with `_fast` have an automatic
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dependency to `vision`, `torch`, and `torchvision`.
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All of these automatic dependencies are added on top of the explicit dependencies that are detailed below.
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### Explicit Object Dependencies
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We add a method called `requires` that is used to explicitly specify the dependencies of a given object. As an
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example, the `Trainer` class has two hard dependencies: `torch` and `accelerate`. Here is how we specify these
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required dependencies:
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```python
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from .utils.import_utils import requires
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@requires(backends=("torch", "accelerate"))
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class Trainer:
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...
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```
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Backends that can be added here are all the backends that are available in the `import_utils.py` module.
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Additionally, specific versions can be specified in each backend. For example, this is how you would specify
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a requirement on torch>=2.6 on the `Trainer` class:
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```python
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from .utils.import_utils import requires
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@requires(backends=("torch>=2.6", "accelerate"))
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class Trainer:
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...
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```
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You can specify the following operators: `==`, `>`, `>=`, `<`, `<=`, `!=`.
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## Methods
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[[autodoc]] utils.import_utils.define_import_structure
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[[autodoc]] utils.import_utils.requires
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[[autodoc]] utils.import_utils.requires_backends
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