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Update TF doc test template #21260

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Update TF doc test template #21260

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44 changes: 22 additions & 22 deletions src/transformers/utils/doc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -607,10 +607,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = tokenizer(
Expand Down Expand Up @@ -640,10 +640,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
Expand Down Expand Up @@ -675,10 +675,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
Expand Down Expand Up @@ -706,10 +706,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
Expand Down Expand Up @@ -739,10 +739,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
Expand All @@ -756,10 +756,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
Expand All @@ -779,10 +779,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoTokenizer, {model_class}
>>> import tensorflow as tf

>>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
Expand All @@ -795,14 +795,14 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoProcessor, {model_class}
>>> from datasets import load_dataset

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = {processor_class}.from_pretrained("{checkpoint}")
>>> processor = AutoProcessor.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> # audio file is decoded on the fly
Expand All @@ -819,15 +819,15 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoProcessor, {model_class}
>>> from datasets import load_dataset
>>> import tensorflow as tf

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = {processor_class}.from_pretrained("{checkpoint}")
>>> processor = AutoProcessor.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> # audio file is decoded on the fly
Expand Down Expand Up @@ -855,13 +855,13 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoImageProcessor, {model_class}
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> image_processor = {processor_class}.from_pretrained("{checkpoint}")
>>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = image_processor(image, return_tensors="tf")
Expand All @@ -877,14 +877,14 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from transformers import AutoImageProcessor, {model_class}
>>> import tensorflow as tf
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> image_processor = {processor_class}.from_pretrained("{checkpoint}")
>>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> inputs = image_processor(image, return_tensors="tf")
Expand Down