Task-specific pipeline init args

src/transformers/pipelines/audio_classification.py

@@ -18,7 +18,7 @@
 import requests
 
 from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_torch_available():
@@ -63,7 +63,7 @@ def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
     return audio
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_feature_extractor=True))
 class AudioClassificationPipeline(Pipeline):
     """
     Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a

src/transformers/pipelines/base.py

@@ -702,14 +702,33 @@ def predict(self, X):
         raise NotImplementedError()
 
 
-PIPELINE_INIT_ARGS = r"""
+def build_pipeline_init_args(
+    has_tokenizer: bool = False,
+    has_feature_extractor: bool = False,
+    has_image_processor: bool = False,
+    supports_binary_output: bool = True,
+) -> str:
+    docstring = r"""
     Arguments:
         model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
             The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
-            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
+            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow."""
+    if has_tokenizer:
+        docstring += r"""
         tokenizer ([`PreTrainedTokenizer`]):
             The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
-            [`PreTrainedTokenizer`].
+            [`PreTrainedTokenizer`]."""
+    if has_feature_extractor:
+        docstring += r"""
+        feature_extractor ([`SequenceFeatureExtractor`]):
+            The feature extractor that will be used by the pipeline to encode data for the model. This object inherits from
+            [`SequenceFeatureExtractor`]."""
+    if has_image_processor:
+        docstring += r"""
+        image_processor ([`BaseImageProcessor`]):
+            The image processor that will be used by the pipeline to encode data for the model. This object inherits from
+            [`BaseImageProcessor`]."""
+    docstring += r"""
         modelcard (`str` or [`ModelCard`], *optional*):
             Model card attributed to the model for this pipeline.
         framework (`str`, *optional*):
@@ -732,10 +751,22 @@ def predict(self, X):
             Reference to the object in charge of parsing supplied pipeline parameters.
         device (`int`, *optional*, defaults to -1):
             Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
-            the associated CUDA device id. You can pass native `torch.device` or a `str` too.
+            the associated CUDA device id. You can pass native `torch.device` or a `str` too
+        torch_dtype (`str` or `torch.dtype`, *optional*):
+            Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model
+            (`torch.float16`, `torch.bfloat16`, ... or `"auto"`)"""
+    if supports_binary_output:
+        docstring += r"""
         binary_output (`bool`, *optional*, defaults to `False`):
-            Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.
-"""
+            Flag indicating if the output the pipeline should happen in a serialized format (i.e., pickle) or as
+            the raw output data e.g. text."""
+    return docstring
+
+
+PIPELINE_INIT_ARGS = build_pipeline_init_args(
+    has_tokenizer=True, has_feature_extractor=True, has_image_processor=True, supports_binary_output=True
+)
+
 
 if is_torch_available():
     from transformers.pipelines.pt_utils import (
@@ -746,7 +777,7 @@ def predict(self, X):
     )
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_feature_extractor=True, has_image_processor=True))
 class Pipeline(_ScikitCompat):
     """
     The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across

src/transformers/pipelines/conversational.py

@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Union
 
 from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_tf_available():
@@ -192,13 +192,12 @@ def new_user_input(self):
 
 
 @add_end_docstrings(
-    PIPELINE_INIT_ARGS,
+    build_pipeline_init_args(has_tokenizer=True),
     r"""
         min_length_for_response (`int`, *optional*, defaults to 32):
             The minimum length (in number of tokens) for a response.
         minimum_tokens (`int`, *optional*, defaults to 10):
-            The minimum length of tokens to leave for a response.
-    """,
+            The minimum length of tokens to leave for a response.""",
 )
 class ConversationalPipeline(Pipeline):
     """

src/transformers/pipelines/depth_estimation.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -19,7 +19,7 @@
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
 class DepthEstimationPipeline(Pipeline):
     """
     Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image.

src/transformers/pipelines/document_question_answering.py

@@ -25,7 +25,7 @@
     is_vision_available,
     logging,
 )
-from .base import PIPELINE_INIT_ARGS, ChunkPipeline
+from .base import ChunkPipeline, build_pipeline_init_args
 from .question_answering import select_starts_ends
 
 
@@ -98,7 +98,7 @@ class ModelType(ExplicitEnum):
     VisionEncoderDecoder = "vision_encoder_decoder"
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True, has_tokenizer=True))
 class DocumentQuestionAnsweringPipeline(ChunkPipeline):
     # TODO: Update task_summary docs to include an example with document QA and then update the first sentence
     """

src/transformers/pipelines/feature_extraction.py

@@ -1,9 +1,17 @@
 from typing import Dict
 
-from .base import GenericTensor, Pipeline
+from ..utils import add_end_docstrings
+from .base import GenericTensor, Pipeline, build_pipeline_init_args
 
 
-# Can't use @add_end_docstrings(PIPELINE_INIT_ARGS) here because this one does not accept `binary_output`
+@add_end_docstrings(
+    build_pipeline_init_args(has_tokenizer=True, supports_binary_output=False),
+    r"""
+        tokenize_kwargs (`dict`, *optional*):
+                Additional dictionary of keyword arguments passed along to the tokenizer.
+        return_tensors (`bool`, *optional*):
+            If `True`, returns a tensor according to the specified framework, otherwise returns a list.""",
+)
 class FeatureExtractionPipeline(Pipeline):
     """
     Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
@@ -27,34 +35,6 @@ class FeatureExtractionPipeline(Pipeline):
 
     All models may be used for this pipeline. See a list of all models, including community-contributed models on
     [huggingface.co/models](https://huggingface.co/models).
-
-    Arguments:
-        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
-            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
-        tokenizer ([`PreTrainedTokenizer`]):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
-            [`PreTrainedTokenizer`].
-        modelcard (`str` or [`ModelCard`], *optional*):
-            Model card attributed to the model for this pipeline.
-        framework (`str`, *optional*):
-            The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
-            installed.
-
-            If no framework is specified, will default to the one currently installed. If no framework is specified and
-            both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
-            provided.
-        return_tensors (`bool`, *optional*):
-            If `True`, returns a tensor according to the specified framework, otherwise returns a list.
-        task (`str`, defaults to `""`):
-            A task-identifier for the pipeline.
-        args_parser ([`~pipelines.ArgumentHandler`], *optional*):
-            Reference to the object in charge of parsing supplied pipeline parameters.
-        device (`int`, *optional*, defaults to -1):
-            Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
-            the associated CUDA device id.
-        tokenize_kwargs (`dict`, *optional*):
-            Additional dictionary of keyword arguments passed along to the tokenizer.
     """
 
     def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs):

src/transformers/pipelines/fill_mask.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
-from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
+from .base import GenericTensor, Pipeline, PipelineException, build_pipeline_init_args
 
 
 if is_tf_available():
@@ -20,16 +20,16 @@
 
 
 @add_end_docstrings(
-    PIPELINE_INIT_ARGS,
+    build_pipeline_init_args(has_tokenizer=True),
     r"""
         top_k (`int`, defaults to 5):
             The number of predictions to return.
         targets (`str` or `List[str]`, *optional*):
             When passed, the model will limit the scores to the passed targets instead of looking up in the whole
             vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
             token will be used (with a warning, and that might be slower).
-
-    """,
+        tokenizer_kwargs (`dict`, *optional*):
+            Additional dictionary of keyword arguments passed along to the tokenizer.""",
 )
 class FillMaskPipeline(Pipeline):
     """

src/transformers/pipelines/image_classification.py

@@ -11,7 +11,7 @@
     logging,
     requires_backends,
 )
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -48,7 +48,7 @@ class ClassificationFunction(ExplicitEnum):
 
 
 @add_end_docstrings(
-    PIPELINE_INIT_ARGS,
+    build_pipeline_init_args(has_image_processor=True),
     r"""
         function_to_apply (`str`, *optional*, defaults to `"default"`):
             The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
@@ -57,8 +57,7 @@ class ClassificationFunction(ExplicitEnum):
               has several labels, will apply the softmax function on the output.
             - `"sigmoid"`: Applies the sigmoid function on the output.
             - `"softmax"`: Applies the softmax function on the output.
-            - `"none"`: Does not apply any function on the output.
-    """,
+            - `"none"`: Does not apply any function on the output.""",
 )
 class ImageClassificationPipeline(Pipeline):
     """

src/transformers/pipelines/image_segmentation.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -27,7 +27,7 @@
 Predictions = List[Prediction]
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
 class ImageSegmentationPipeline(Pipeline):
     """
     Image segmentation pipeline using any `AutoModelForXXXSegmentation`. This pipeline predicts masks of objects and

src/transformers/pipelines/image_to_image.py

@@ -22,7 +22,7 @@
     logging,
     requires_backends,
 )
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -36,7 +36,7 @@
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
 class ImageToImagePipeline(Pipeline):
     """
     Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous

src/transformers/pipelines/image_to_text.py

@@ -8,7 +8,7 @@
     logging,
     requires_backends,
 )
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -27,7 +27,7 @@
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_image_processor=True))
 class ImageToTextPipeline(Pipeline):
     """
     Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image.

src/transformers/pipelines/mask_generation.py

@@ -8,7 +8,7 @@
     logging,
     requires_backends,
 )
-from .base import PIPELINE_INIT_ARGS, ChunkPipeline
+from .base import ChunkPipeline, build_pipeline_init_args
 
 
 if is_torch_available():
@@ -19,7 +19,17 @@
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(
+    build_pipeline_init_args(has_image_processor=True),
+    r"""
+        points_per_batch (*optional*, int, default to 64):
+            Sets the number of points run simultaneously by the model. Higher numbers may be faster but use more GPU
+            memory.
+        output_bboxes_mask (`bool`, *optional*, default to `False`):
+            Whether or not to output the bounding box predictions.
+        output_rle_masks (`bool`, *optional*, default to `False`):
+            Whether or not to output the masks in `RLE` format""",
+)
 class MaskGenerationPipeline(ChunkPipeline):
     """
     Automatic mask generation for images using `SamForMaskGeneration`. This pipeline predicts binary masks for an
@@ -48,23 +58,6 @@ class MaskGenerationPipeline(ChunkPipeline):
                 applies a variety of filters based on non maximum suppression to remove bad masks.
                 - image_processor.postprocess_masks_for_amg applies the NSM on the mask to only keep relevant ones.
 
-    Arguments:
-        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
-            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
-        tokenizer ([`PreTrainedTokenizer`]):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
-            [`PreTrainedTokenizer`].
-        feature_extractor ([`SequenceFeatureExtractor`]):
-            The feature extractor that will be used by the pipeline to encode the input.
-        points_per_batch (*optional*, int, default to 64):
-            Sets the number of points run simultaneously by the model. Higher numbers may be faster but use more GPU
-            memory.
-        output_bboxes_mask (`bool`, *optional*, default to `False`):
-           Whether or not to output the bounding box predictions.
-        output_rle_masks (`bool`, *optional*, default to `False`):
-            Whether or not to output the masks in `RLE` format
-
     Example:
 
     ```python

src/transformers/pipelines/object_detection.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, List, Union
 
 from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -23,7 +23,7 @@
 Predictions = List[Prediction]
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
 class ObjectDetectionPipeline(Pipeline):
     """
     Object detection pipeline using any `AutoModelForObjectDetection`. This pipeline predicts bounding boxes of objects