Fix doctest for `Blip2ForConditionalGeneration` (#26737)

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/blip_2/modeling_blip_2.py

@@ -1272,14 +1272,10 @@ class Blip2Model(Blip2PreTrainedModel):
         >>> import torch
         >>> from transformers import AutoTokenizer, Blip2Model
 
-        >>> device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
-
-        >>> model.to(device)  # doctest: +IGNORE_RESULT
+        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
 
         >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b")
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt").to(device)
+        >>> inputs = tokenizer(["a photo of a cat"], padding=True, return_tensors="pt")
         >>> text_features = model.get_text_features(**inputs)
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -1333,16 +1329,12 @@ class Blip2Model(Blip2PreTrainedModel):
         >>> import requests
         >>> from transformers import AutoProcessor, Blip2Model
 
-        >>> device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
-
-        >>> model.to(device)  # doctest: +IGNORE_RESULT
+        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
 
         >>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
-        >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
+        >>> inputs = processor(images=image, return_tensors="pt")
         >>> image_outputs = model.get_image_features(**inputs)
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -1381,15 +1373,12 @@ class Blip2Model(Blip2PreTrainedModel):
         >>> import requests
         >>> from transformers import Blip2Processor, Blip2Model
 
-        >>> device = "cuda" if torch.cuda.is_available() else "cpu"
-
         >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
-        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
-        >>> model.to(device)  # doctest: +IGNORE_RESULT
+        >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
-        >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
+        >>> inputs = processor(images=image, return_tensors="pt")
         >>> qformer_outputs = model.get_qformer_features(**inputs)
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -1654,7 +1643,7 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
 
         Examples:
 
-        Image captioning (without providing a text prompt):
+        Prepare processor, model and image input
 
         ```python
         >>> from PIL import Image
@@ -1666,13 +1655,16 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
 
         >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
         >>> model = Blip2ForConditionalGeneration.from_pretrained(
-        ...     "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
-        ... )
-        >>> model.to(device)  # doctest: +IGNORE_RESULT
+        ...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16
+        ... )  # doctest: +IGNORE_RESULT
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
+        ```
 
+        Image captioning (without providing a text prompt):
+
+        ```python
         >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
 
         >>> generated_ids = model.generate(**inputs)
@@ -1684,21 +1676,6 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
         Visual question answering (prompt = question):
 
         ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
-        >>> import torch
-
-        >>> device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
-        >>> model = Blip2ForConditionalGeneration.from_pretrained(
-        ...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16
-        ... )  # doctest: +IGNORE_RESULT
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
         >>> prompt = "Question: how many cats are there? Answer:"
         >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.float16)
 
@@ -1712,20 +1689,10 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
         This greatly reduces the amount of memory used by the model while maintaining the same performance.
 
         ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
-        >>> import torch
-
-        >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
         >>> model = Blip2ForConditionalGeneration.from_pretrained(
-        ...     "Salesforce/blip2-flan-t5-xl", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16
+        ...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16
         ... )  # doctest: +IGNORE_RESULT
 
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> prompt = "Question: how many cats are there? Answer:"
         >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.bfloat16)
 
         >>> generated_ids = model.generate(**inputs)

utils/slow_documentation_tests.txt

 docs/source/en/generation_strategies.md
 docs/source/en/model_doc/ctrl.md
 docs/source/en/task_summary.md
+src/transformers/models/blip_2/modeling_blip_2.py
 src/transformers/models/ctrl/modeling_ctrl.py