implement get_num_tokens to use google's count_tokens function (#10565)

can get the correct token count instead of using gpt-2 model

**Description:** 
Implement get_num_tokens within VertexLLM to use google's count_tokens
function.
(https://cloud.google.com/vertex-ai/docs/generative-ai/get-token-count).
So we don't need to download gpt-2 model from huggingface, also when we
do the mapreduce chain we can get correct token count.

**Tag maintainer:** 
@lkuligin 
**Twitter handle:** 
My twitter: @abehsu1992626

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/llms/vertexai.py

@@ -276,6 +276,27 @@ class VertexAI(_VertexAICommon, BaseLLM):
             raise ValueError("Only one candidate can be generated with streaming!")
         return values
 
+    def get_num_tokens(self, text: str) -> int:
+        """Get the number of tokens present in the text.
+
+        Useful for checking if an input will fit in a model's context window.
+
+        Args:
+            text: The string input to tokenize.
+
+        Returns:
+            The integer number of tokens in the text.
+        """
+        try:
+            result = self.client.count_tokens(text)
+        except AttributeError:
+            raise NotImplementedError(
+                "Your google-cloud-aiplatform version didn't implement count_tokens."
+                "Please, install it with pip install google-cloud-aiplatform>=1.35.0"
+            )
+
+        return result.total_tokens
+
     def _generate(
         self,
         prompts: List[str],

libs/langchain/tests/integration_tests/chat_models/test_vertexai.py

@@ -2,7 +2,7 @@
 In order to run this test, you need to install VertexAI SDK (that is is the private
 preview)  and be whitelisted to list the models themselves:
 In order to run this test, you need to install VertexAI SDK 
-pip install google-cloud-aiplatform>=1.25.0
+pip install google-cloud-aiplatform>=1.35.0
 
 Your end-user credentials would be used to make the calls (make sure you've run 
 `gcloud auth login` first).

libs/langchain/tests/integration_tests/embeddings/test_vertexai.py

@@ -1,6 +1,6 @@
 """Test Vertex AI API wrapper.
 In order to run this test, you need to install VertexAI SDK 
-pip install google-cloud-aiplatform>=1.25.0
+pip install google-cloud-aiplatform>=1.35.0
 
 Your end-user credentials would be used to make the calls (make sure you've run 
 `gcloud auth login` first).

libs/langchain/tests/integration_tests/llms/test_vertexai.py

@@ -2,7 +2,7 @@
 In order to run this test, you need to install VertexAI SDK (that is is the private
 preview)  and be whitelisted to list the models themselves:
 In order to run this test, you need to install VertexAI SDK 
-pip install google-cloud-aiplatform>=1.25.0
+pip install google-cloud-aiplatform>=1.35.0
 
 Your end-user credentials would be used to make the calls (make sure you've run 
 `gcloud auth login` first).
@@ -10,7 +10,10 @@ Your end-user credentials would be used to make the calls (make sure you've run
 import os
 
 import pytest
+from pytest_mock import MockerFixture
 
+from langchain.chains.summarize import load_summarize_chain
+from langchain.docstore.document import Document
 from langchain.llms import VertexAI, VertexAIModelGarden
 from langchain.schema import LLMResult
 
@@ -108,3 +111,37 @@ async def test_model_garden_agenerate() -> None:
     output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"])
     assert isinstance(output, LLMResult)
     assert len(output.generations) == 2
+
+
+def test_vertex_call_trigger_count_tokens() -> None:
+    llm = VertexAI()
+    output = llm.get_num_tokens("Hi")
+    assert output == 2
+
+
+@pytest.mark.requires("google.cloud.aiplatform")
+def test_get_num_tokens_be_called_when_using_mapreduce_chain(
+    mocker: MockerFixture,
+) -> None:
+    from vertexai.language_models._language_models import CountTokensResponse
+
+    m1 = mocker.patch(
+        "vertexai.preview.language_models._PreviewTextGenerationModel.count_tokens",
+        return_value=CountTokensResponse(
+            total_tokens=2,
+            total_billable_characters=2,
+            _count_tokens_response={"total_tokens": 2, "total_billable_characters": 2},
+        ),
+    )
+    llm = VertexAI()
+    chain = load_summarize_chain(
+        llm,
+        chain_type="map_reduce",
+        return_intermediate_steps=False,
+    )
+    doc = Document(page_content="Hi")
+    output = chain({"input_documents": [doc]})
+    assert isinstance(output["output_text"], str)
+    m1.assert_called_once()
+    assert llm._llm_type == "vertexai"
+    assert llm.model_name == llm.client._model_id