From 5047c7f6a02a3a339ddbfef94baa5ecc5c8330ec Mon Sep 17 00:00:00 2001
From: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Date: Tue, 30 Jan 2024 16:54:57 +0000
Subject: [PATCH] Task-specific pipeline init args (#28439)

* Abstract out pipeline init args

* Address PR comments

* Reword

* BC PIPELINE_INIT_ARGS

* Remove old arguments

* Small fix
---
 .../pipelines/audio_classification.py         |  4 +-
 src/transformers/pipelines/base.py            | 45 ++++++++++++++++---
 src/transformers/pipelines/conversational.py  |  7 ++-
 .../pipelines/depth_estimation.py             |  4 +-
 .../pipelines/document_question_answering.py  |  4 +-
 .../pipelines/feature_extraction.py           | 40 +++++------------
 src/transformers/pipelines/fill_mask.py       |  8 ++--
 .../pipelines/image_classification.py         |  7 ++-
 .../pipelines/image_segmentation.py           |  4 +-
 src/transformers/pipelines/image_to_image.py  |  4 +-
 src/transformers/pipelines/image_to_text.py   |  4 +-
 src/transformers/pipelines/mask_generation.py | 31 +++++--------
 .../pipelines/object_detection.py             |  4 +-
 .../pipelines/question_answering.py           |  4 +-
 .../pipelines/table_question_answering.py     |  4 +-
 .../pipelines/text2text_generation.py         |  8 ++--
 .../pipelines/text_classification.py          |  7 ++-
 src/transformers/pipelines/text_generation.py |  4 +-
 .../pipelines/token_classification.py         |  7 ++-
 .../pipelines/video_classification.py         |  4 +-
 .../pipelines/visual_question_answering.py    |  4 +-
 .../zero_shot_audio_classification.py         |  4 +-
 .../pipelines/zero_shot_classification.py     |  4 +-
 .../zero_shot_image_classification.py         |  4 +-
 .../pipelines/zero_shot_object_detection.py   |  4 +-
 25 files changed, 112 insertions(+), 112 deletions(-)

diff --git a/src/transformers/pipelines/audio_classification.py b/src/transformers/pipelines/audio_classification.py
index 96b974b7363a8e..a0e8f626db644e 100644
--- a/src/transformers/pipelines/audio_classification.py
+++ b/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
diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 5f46a5904606e6..bfa8e2262ec8d4 100644
--- a/src/transformers/pipelines/base.py
+++ b/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
diff --git a/src/transformers/pipelines/conversational.py b/src/transformers/pipelines/conversational.py
index 04152270379dee..3d42363f198357 100644
--- a/src/transformers/pipelines/conversational.py
+++ b/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):
     """
diff --git a/src/transformers/pipelines/depth_estimation.py b/src/transformers/pipelines/depth_estimation.py
index dbc1e1344398c8..bd6bb0d0db9fb0 100644
--- a/src/transformers/pipelines/depth_estimation.py
+++ b/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.
diff --git a/src/transformers/pipelines/document_question_answering.py b/src/transformers/pipelines/document_question_answering.py
index 3c107d650cfdab..ab73aca2c19039 100644
--- a/src/transformers/pipelines/document_question_answering.py
+++ b/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
     """
diff --git a/src/transformers/pipelines/feature_extraction.py b/src/transformers/pipelines/feature_extraction.py
index 5fc6a128e7ea41..d704345db03df9 100644
--- a/src/transformers/pipelines/feature_extraction.py
+++ b/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):
diff --git a/src/transformers/pipelines/fill_mask.py b/src/transformers/pipelines/fill_mask.py
index d22a838f27666e..1d54c615ea258c 100644
--- a/src/transformers/pipelines/fill_mask.py
+++ b/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():
@@ -20,7 +20,7 @@
 
 
 @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.
@@ -28,8 +28,8 @@
             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):
     """
diff --git a/src/transformers/pipelines/image_classification.py b/src/transformers/pipelines/image_classification.py
index 4e4d908a447ae2..62793c252a6ba1 100644
--- a/src/transformers/pipelines/image_classification.py
+++ b/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):
     """
diff --git a/src/transformers/pipelines/image_segmentation.py b/src/transformers/pipelines/image_segmentation.py
index 01540729e57b25..23fbd4fb79b190 100644
--- a/src/transformers/pipelines/image_segmentation.py
+++ b/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
diff --git a/src/transformers/pipelines/image_to_image.py b/src/transformers/pipelines/image_to_image.py
index dbd88deb1ee024..8c34ee8dd3c80c 100644
--- a/src/transformers/pipelines/image_to_image.py
+++ b/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
diff --git a/src/transformers/pipelines/image_to_text.py b/src/transformers/pipelines/image_to_text.py
index e5cbb36ea526a0..ec1d07e0228253 100644
--- a/src/transformers/pipelines/image_to_text.py
+++ b/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.
diff --git a/src/transformers/pipelines/mask_generation.py b/src/transformers/pipelines/mask_generation.py
index bc2c719084a1e6..68d407aff2d4e4 100644
--- a/src/transformers/pipelines/mask_generation.py
+++ b/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
diff --git a/src/transformers/pipelines/object_detection.py b/src/transformers/pipelines/object_detection.py
index 636a1b6a061bbe..d6ae63f4bd19f3 100644
--- a/src/transformers/pipelines/object_detection.py
+++ b/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
diff --git a/src/transformers/pipelines/question_answering.py b/src/transformers/pipelines/question_answering.py
index 5bc72151fba57c..4ac5d252b1139e 100644
--- a/src/transformers/pipelines/question_answering.py
+++ b/src/transformers/pipelines/question_answering.py
@@ -17,7 +17,7 @@
     is_torch_available,
     logging,
 )
-from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
+from .base import ArgumentHandler, ChunkPipeline, build_pipeline_init_args
 
 
 logger = logging.get_logger(__name__)
@@ -221,7 +221,7 @@ def __call__(self, *args, **kwargs):
         return inputs
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class QuestionAnsweringPipeline(ChunkPipeline):
     """
     Question Answering pipeline using any `ModelForQuestionAnswering`. See the [question answering
diff --git a/src/transformers/pipelines/table_question_answering.py b/src/transformers/pipelines/table_question_answering.py
index e0cb2ff3e17872..f737ac7b3b408a 100644
--- a/src/transformers/pipelines/table_question_answering.py
+++ b/src/transformers/pipelines/table_question_answering.py
@@ -10,7 +10,7 @@
     is_torch_available,
     requires_backends,
 )
-from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException
+from .base import ArgumentHandler, Dataset, Pipeline, PipelineException, build_pipeline_init_args
 
 
 if is_torch_available():
@@ -84,7 +84,7 @@ def __call__(self, table=None, query=None, **kwargs):
         return tqa_pipeline_inputs
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class TableQuestionAnsweringPipeline(Pipeline):
     """
     Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in
diff --git a/src/transformers/pipelines/text2text_generation.py b/src/transformers/pipelines/text2text_generation.py
index 5b9ce06832da64..09f0b0c4490765 100644
--- a/src/transformers/pipelines/text2text_generation.py
+++ b/src/transformers/pipelines/text2text_generation.py
@@ -3,7 +3,7 @@
 
 from ..tokenization_utils import TruncationStrategy
 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():
@@ -22,7 +22,7 @@ class ReturnType(enum.Enum):
     TEXT = 1
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class Text2TextGenerationPipeline(Pipeline):
     """
     Pipeline for text to text generation using seq2seq models.
@@ -213,7 +213,7 @@ def postprocess(self, model_outputs, return_type=ReturnType.TEXT, clean_up_token
         return records
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class SummarizationPipeline(Text2TextGenerationPipeline):
     """
     Summarize news articles and other documents.
@@ -283,7 +283,7 @@ def check_inputs(self, input_length: int, min_length: int, max_length: int) -> b
             )
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class TranslationPipeline(Text2TextGenerationPipeline):
     """
     Translates from one language to another.
diff --git a/src/transformers/pipelines/text_classification.py b/src/transformers/pipelines/text_classification.py
index f9c87fb944a0c3..2b7717934ddcd3 100644
--- a/src/transformers/pipelines/text_classification.py
+++ b/src/transformers/pipelines/text_classification.py
@@ -5,7 +5,7 @@
 import numpy as np
 
 from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
-from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
+from .base import GenericTensor, Pipeline, build_pipeline_init_args
 
 
 if is_tf_available():
@@ -32,7 +32,7 @@ class ClassificationFunction(ExplicitEnum):
 
 
 @add_end_docstrings(
-    PIPELINE_INIT_ARGS,
+    build_pipeline_init_args(has_tokenizer=True),
     r"""
         return_all_scores (`bool`, *optional*, defaults to `False`):
             Whether to return all prediction scores or just the one of the predicted class.
@@ -43,8 +43,7 @@ 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 TextClassificationPipeline(Pipeline):
     """
diff --git a/src/transformers/pipelines/text_generation.py b/src/transformers/pipelines/text_generation.py
index fe0a49a47645db..839395d7fe0528 100644
--- a/src/transformers/pipelines/text_generation.py
+++ b/src/transformers/pipelines/text_generation.py
@@ -2,7 +2,7 @@
 import warnings
 
 from ..utils import add_end_docstrings, is_tf_available, is_torch_available
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_torch_available():
@@ -20,7 +20,7 @@ class ReturnType(enum.Enum):
     FULL_TEXT = 2
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class TextGenerationPipeline(Pipeline):
     """
     Language generation pipeline using any `ModelWithLMHead`. This pipeline predicts the words that will follow a
diff --git a/src/transformers/pipelines/token_classification.py b/src/transformers/pipelines/token_classification.py
index 42c5d927079c38..e1d763eafa8b71 100644
--- a/src/transformers/pipelines/token_classification.py
+++ b/src/transformers/pipelines/token_classification.py
@@ -11,7 +11,7 @@
     is_tf_available,
     is_torch_available,
 )
-from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline, Dataset
+from .base import ArgumentHandler, ChunkPipeline, Dataset, build_pipeline_init_args
 
 
 if is_tf_available():
@@ -59,7 +59,7 @@ class AggregationStrategy(ExplicitEnum):
 
 
 @add_end_docstrings(
-    PIPELINE_INIT_ARGS,
+    build_pipeline_init_args(has_tokenizer=True),
     r"""
         ignore_labels (`List[str]`, defaults to `["O"]`):
             A list of labels to ignore.
@@ -90,8 +90,7 @@ class AggregationStrategy(ExplicitEnum):
                   cannot end up with different tags. scores will be averaged first across tokens, and then the maximum
                   label is applied.
                 - "max" : (works only on word based models) Will use the `SIMPLE` strategy except that words, cannot
-                  end up with different tags. Word entity will simply be the token with the maximum score.
-    """,
+                  end up with different tags. Word entity will simply be the token with the maximum score.""",
 )
 class TokenClassificationPipeline(ChunkPipeline):
     """
diff --git a/src/transformers/pipelines/video_classification.py b/src/transformers/pipelines/video_classification.py
index 4255856aa26d60..f8596ce14c714a 100644
--- a/src/transformers/pipelines/video_classification.py
+++ b/src/transformers/pipelines/video_classification.py
@@ -4,7 +4,7 @@
 import requests
 
 from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_decord_available():
@@ -18,7 +18,7 @@
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
 class VideoClassificationPipeline(Pipeline):
     """
     Video classification pipeline using any `AutoModelForVideoClassification`. This pipeline predicts the class of a
diff --git a/src/transformers/pipelines/visual_question_answering.py b/src/transformers/pipelines/visual_question_answering.py
index c3bf65114fc5a7..f456835d70904e 100644
--- a/src/transformers/pipelines/visual_question_answering.py
+++ b/src/transformers/pipelines/visual_question_answering.py
@@ -1,7 +1,7 @@
 from typing import Union
 
 from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -15,7 +15,7 @@
 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 VisualQuestionAnsweringPipeline(Pipeline):
     """
     Visual Question Answering pipeline using a `AutoModelForVisualQuestionAnswering`. This pipeline is currently only
diff --git a/src/transformers/pipelines/zero_shot_audio_classification.py b/src/transformers/pipelines/zero_shot_audio_classification.py
index e6b1da7df70a33..ca9f5e4fcfd495 100644
--- a/src/transformers/pipelines/zero_shot_audio_classification.py
+++ b/src/transformers/pipelines/zero_shot_audio_classification.py
@@ -23,13 +23,13 @@
     logging,
 )
 from .audio_classification import ffmpeg_read
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_feature_extractor=True, has_tokenizer=True))
 class ZeroShotAudioClassificationPipeline(Pipeline):
     """
     Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
diff --git a/src/transformers/pipelines/zero_shot_classification.py b/src/transformers/pipelines/zero_shot_classification.py
index eb01d3a5354a29..9a600bc8ad0fb8 100644
--- a/src/transformers/pipelines/zero_shot_classification.py
+++ b/src/transformers/pipelines/zero_shot_classification.py
@@ -5,7 +5,7 @@
 
 from ..tokenization_utils import TruncationStrategy
 from ..utils import add_end_docstrings, logging
-from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
+from .base import ArgumentHandler, ChunkPipeline, build_pipeline_init_args
 
 
 logger = logging.get_logger(__name__)
@@ -43,7 +43,7 @@ def __call__(self, sequences, labels, hypothesis_template):
         return sequence_pairs, sequences
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
 class ZeroShotClassificationPipeline(ChunkPipeline):
     """
     NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` trained on NLI (natural
diff --git a/src/transformers/pipelines/zero_shot_image_classification.py b/src/transformers/pipelines/zero_shot_image_classification.py
index 83bd9970e8f4c6..d97fe246a2ef97 100644
--- a/src/transformers/pipelines/zero_shot_image_classification.py
+++ b/src/transformers/pipelines/zero_shot_image_classification.py
@@ -9,7 +9,7 @@
     logging,
     requires_backends,
 )
-from .base import PIPELINE_INIT_ARGS, Pipeline
+from .base import Pipeline, build_pipeline_init_args
 
 
 if is_vision_available():
@@ -29,7 +29,7 @@
 logger = logging.get_logger(__name__)
 
 
-@add_end_docstrings(PIPELINE_INIT_ARGS)
+@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
 class ZeroShotImageClassificationPipeline(Pipeline):
     """
     Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you
diff --git a/src/transformers/pipelines/zero_shot_object_detection.py b/src/transformers/pipelines/zero_shot_object_detection.py
index a7181d9540b9f7..5be89332cbd910 100644
--- a/src/transformers/pipelines/zero_shot_object_detection.py
+++ b/src/transformers/pipelines/zero_shot_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, ChunkPipeline
+from .base import ChunkPipeline, 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 ZeroShotObjectDetectionPipeline(ChunkPipeline):
     """
     Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of