[Misc] Upgrade to Python 3.9 typing for additional directories (#14492)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

pyproject.toml

@@ -75,11 +75,8 @@ exclude = [
 "vllm/distributed/**/*.py" = ["UP006", "UP035"]
 "vllm/engine/**/*.py" = ["UP006", "UP035"]
 "vllm/executor/**/*.py" = ["UP006", "UP035"]
-"vllm/inputs/**/*.py" = ["UP006", "UP035"]
-"vllm/logging_utils/**/*.py" = ["UP006", "UP035"]
 "vllm/lora/**/*.py" = ["UP006", "UP035"]
 "vllm/model_executor/**/*.py" = ["UP006", "UP035"]
-"vllm/multimodal/**/*.py" = ["UP006", "UP035"]
 "vllm/platforms/**/*.py" = ["UP006", "UP035"]
 "vllm/plugins/**/*.py" = ["UP006", "UP035"]
 "vllm/profiler/**/*.py" = ["UP006", "UP035"]
@@ -87,9 +84,7 @@ exclude = [
 "vllm/spec_decode/**/*.py" = ["UP006", "UP035"]
 "vllm/transformers_utils/**/*.py" = ["UP006", "UP035"]
 "vllm/triton_utils/**/*.py" = ["UP006", "UP035"]
-"vllm/usage/**/*.py" = ["UP006", "UP035"]
 "vllm/vllm_flash_attn/**/*.py" = ["UP006", "UP035"]
-"vllm/assets/**/*.py" = ["UP006", "UP035"]
 "vllm/worker/**/*.py" = ["UP006", "UP035"]
 
 [tool.ruff.lint]

vllm/assets/video.py

@@ -2,7 +2,7 @@
 
 from dataclasses import dataclass
 from functools import lru_cache
-from typing import List, Literal
+from typing import Literal
 
 import cv2
 import numpy as np
@@ -58,7 +58,7 @@ def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
 
 
 def video_to_pil_images_list(path: str,
-                             num_frames: int = -1) -> List[Image.Image]:
+                             num_frames: int = -1) -> list[Image.Image]:
     frames = video_to_ndarrays(path, num_frames)
     return [
         Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
@@ -72,7 +72,7 @@ class VideoAsset:
     num_frames: int = -1
 
     @property
-    def pil_images(self) -> List[Image.Image]:
+    def pil_images(self) -> list[Image.Image]:
         video_path = download_video_asset(self.name)
         ret = video_to_pil_images_list(video_path, self.num_frames)
         return ret

vllm/inputs/data.py

 # SPDX-License-Identifier: Apache-2.0
 
+from collections.abc import Iterable
 from dataclasses import dataclass
 from functools import cached_property
-from typing import (TYPE_CHECKING, Any, Dict, Generic, Iterable, List, Literal,
-                    Optional, Tuple, Union, cast)
+from typing import TYPE_CHECKING, Any, Generic, Literal, Optional, Union, cast
 
 import torch
 from typing_extensions import NotRequired, TypedDict, TypeVar, assert_never
@@ -26,7 +26,7 @@ class TextPrompt(TypedDict):
     if the model supports it.
     """
 
-    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    mm_processor_kwargs: NotRequired[dict[str, Any]]
     """
     Optional multi-modal processor kwargs to be forwarded to the
     multimodal input mapper & processor. Note that if multiple modalities
@@ -38,10 +38,10 @@ class TextPrompt(TypedDict):
 class TokensPrompt(TypedDict):
     """Schema for a tokenized prompt."""
 
-    prompt_token_ids: List[int]
+    prompt_token_ids: list[int]
     """A list of token IDs to pass to the model."""
 
-    token_type_ids: NotRequired[List[int]]
+    token_type_ids: NotRequired[list[int]]
     """A list of token type IDs to pass to the cross encoder model."""
 
     multi_modal_data: NotRequired["MultiModalDataDict"]
@@ -50,7 +50,7 @@ class TokensPrompt(TypedDict):
     if the model supports it.
     """
 
-    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    mm_processor_kwargs: NotRequired[dict[str, Any]]
     """
     Optional multi-modal processor kwargs to be forwarded to the
     multimodal input mapper & processor. Note that if multiple modalities
@@ -115,7 +115,7 @@ class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
 
     decoder_prompt: Optional[_T2_co]
 
-    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    mm_processor_kwargs: NotRequired[dict[str, Any]]
 
 
 PromptType = Union[SingletonPrompt, ExplicitEncoderDecoderPrompt]
@@ -136,10 +136,10 @@ class TokenInputs(TypedDict):
     type: Literal["token"]
     """The type of inputs."""
 
-    prompt_token_ids: List[int]
+    prompt_token_ids: list[int]
     """The token IDs of the prompt."""
 
-    token_type_ids: NotRequired[List[int]]
+    token_type_ids: NotRequired[list[int]]
     """The token type IDs of the prompt."""
 
     prompt: NotRequired[str]
@@ -164,12 +164,12 @@ class TokenInputs(TypedDict):
     Placeholder ranges for the multi-modal data.
     """
 
-    multi_modal_hashes: NotRequired[List[str]]
+    multi_modal_hashes: NotRequired[list[str]]
     """
     The hashes of the multi-modal data.
     """
 
-    mm_processor_kwargs: NotRequired[Dict[str, Any]]
+    mm_processor_kwargs: NotRequired[dict[str, Any]]
     """
     Optional multi-modal processor kwargs to be forwarded to the
     multimodal input mapper & processor. Note that if multiple modalities
@@ -179,14 +179,14 @@ class TokenInputs(TypedDict):
 
 
 def token_inputs(
-    prompt_token_ids: List[int],
-    token_type_ids: Optional[List[int]] = None,
+    prompt_token_ids: list[int],
+    token_type_ids: Optional[list[int]] = None,
     prompt: Optional[str] = None,
     multi_modal_data: Optional["MultiModalDataDict"] = None,
     multi_modal_inputs: Optional["MultiModalKwargs"] = None,
-    multi_modal_hashes: Optional[List[str]] = None,
+    multi_modal_hashes: Optional[list[str]] = None,
     multi_modal_placeholders: Optional["MultiModalPlaceholderDict"] = None,
-    mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+    mm_processor_kwargs: Optional[dict[str, Any]] = None,
 ) -> TokenInputs:
     """Construct :class:`TokenInputs` from optional values."""
     inputs = TokenInputs(type="token", prompt_token_ids=prompt_token_ids)
@@ -255,7 +255,7 @@ class SingletonInputsAdapter:
         assert_never(inputs)  # type: ignore[arg-type]
 
     @cached_property
-    def prompt_token_ids(self) -> List[int]:
+    def prompt_token_ids(self) -> list[int]:
         inputs = self.inputs
 
         if inputs["type"] == "token" or inputs["type"] == "multimodal":
@@ -264,7 +264,7 @@ class SingletonInputsAdapter:
         assert_never(inputs)  # type: ignore[arg-type]
 
     @cached_property
-    def token_type_ids(self) -> List[int]:
+    def token_type_ids(self) -> list[int]:
         inputs = self.inputs
 
         if inputs["type"] == "token" or inputs["type"] == "multimodal":
@@ -294,7 +294,7 @@ class SingletonInputsAdapter:
         assert_never(inputs)  # type: ignore[arg-type]
 
     @cached_property
-    def multi_modal_inputs(self) -> Union[Dict, "MultiModalKwargs"]:
+    def multi_modal_inputs(self) -> Union[dict, "MultiModalKwargs"]:
         inputs = self.inputs
 
         if inputs["type"] == "token":
@@ -306,7 +306,7 @@ class SingletonInputsAdapter:
         assert_never(inputs)  # type: ignore[arg-type]
 
     @cached_property
-    def multi_modal_hashes(self) -> List[str]:
+    def multi_modal_hashes(self) -> list[str]:
         inputs = self.inputs
 
         if inputs["type"] == "token":
@@ -331,7 +331,7 @@ class SingletonInputsAdapter:
         assert_never(inputs)  # type: ignore[arg-type]
 
     @cached_property
-    def mm_processor_kwargs(self) -> Dict[str, Any]:
+    def mm_processor_kwargs(self) -> dict[str, Any]:
         inputs = self.inputs
 
         if inputs["type"] == "token":
@@ -355,7 +355,7 @@ _T2 = TypeVar("_T2", bound=SingletonPrompt, default=SingletonPrompt)
 def build_explicit_enc_dec_prompt(
     encoder_prompt: _T1,
     decoder_prompt: Optional[_T2],
-    mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+    mm_processor_kwargs: Optional[dict[str, Any]] = None,
 ) -> ExplicitEncoderDecoderPrompt[_T1, _T2]:
     if mm_processor_kwargs is None:
         mm_processor_kwargs = {}
@@ -368,9 +368,9 @@ def build_explicit_enc_dec_prompt(
 def zip_enc_dec_prompts(
     enc_prompts: Iterable[_T1],
     dec_prompts: Iterable[Optional[_T2]],
-    mm_processor_kwargs: Optional[Union[Iterable[Dict[str, Any]],
-                                        Dict[str, Any]]] = None,
-) -> List[ExplicitEncoderDecoderPrompt[_T1, _T2]]:
+    mm_processor_kwargs: Optional[Union[Iterable[dict[str, Any]],
+                                        dict[str, Any]]] = None,
+) -> list[ExplicitEncoderDecoderPrompt[_T1, _T2]]:
     """
     Zip encoder and decoder prompts together into a list of
     :class:`ExplicitEncoderDecoderPrompt` instances.
@@ -380,12 +380,12 @@ def zip_enc_dec_prompts(
     provided, it will be zipped with the encoder/decoder prompts.
     """
     if mm_processor_kwargs is None:
-        mm_processor_kwargs = cast(Dict[str, Any], {})
+        mm_processor_kwargs = cast(dict[str, Any], {})
     if isinstance(mm_processor_kwargs, dict):
         return [
             build_explicit_enc_dec_prompt(
                 encoder_prompt, decoder_prompt,
-                cast(Dict[str, Any], mm_processor_kwargs))
+                cast(dict[str, Any], mm_processor_kwargs))
             for (encoder_prompt,
                  decoder_prompt) in zip(enc_prompts, dec_prompts)
         ]
@@ -399,7 +399,7 @@ def zip_enc_dec_prompts(
 
 def to_enc_dec_tuple_list(
     enc_dec_prompts: Iterable[ExplicitEncoderDecoderPrompt[_T1, _T2]],
-) -> List[Tuple[_T1, Optional[_T2]]]:
+) -> list[tuple[_T1, Optional[_T2]]]:
     return [(enc_dec_prompt["encoder_prompt"],
              enc_dec_prompt["decoder_prompt"])
             for enc_dec_prompt in enc_dec_prompts]

vllm/inputs/parse.py

 # SPDX-License-Identifier: Apache-2.0
 
-from typing import List, Literal, Sequence, TypedDict, Union, cast, overload
+from collections.abc import Sequence
+from typing import Literal, TypedDict, Union, cast, overload
 
 from typing_extensions import TypeIs
 
@@ -17,24 +18,24 @@ class ParsedText(TypedDict):
 
 
 class ParsedTokens(TypedDict):
-    content: List[int]
+    content: list[int]
     is_tokens: Literal[True]
 
 
 @overload
 def parse_and_batch_prompt(
-        prompt: Union[str, List[str]]) -> Sequence[ParsedText]:
+        prompt: Union[str, list[str]]) -> Sequence[ParsedText]:
     ...
 
 
 @overload
 def parse_and_batch_prompt(
-        prompt: Union[List[int], List[List[int]]]) -> Sequence[ParsedTokens]:
+        prompt: Union[list[int], list[list[int]]]) -> Sequence[ParsedTokens]:
     ...
 
 
 def parse_and_batch_prompt(
-    prompt: Union[str, List[str], List[int], List[List[int]]],
+    prompt: Union[str, list[str], list[int], list[list[int]]],
 ) -> Union[Sequence[ParsedText], Sequence[ParsedTokens]]:
     if isinstance(prompt, str):
         # case 1: a string
@@ -46,16 +47,16 @@ def parse_and_batch_prompt(
 
         if is_list_of(prompt, str):
             # case 2: array of strings
-            prompt = cast(List[str], prompt)
+            prompt = cast(list[str], prompt)
             return [
                 ParsedText(content=elem, is_tokens=False) for elem in prompt
             ]
         if is_list_of(prompt, int):
             # case 3: array of tokens
-            prompt = cast(List[int], prompt)
+            prompt = cast(list[int], prompt)
             return [ParsedTokens(content=prompt, is_tokens=True)]
         if is_list_of(prompt, list):
-            prompt = cast(List[List[int]], prompt)
+            prompt = cast(list[list[int]], prompt)
             if len(prompt[0]) == 0:
                 raise ValueError("please provide at least one prompt")
 

vllm/inputs/preprocess.py

 # SPDX-License-Identifier: Apache-2.0
 
 import asyncio
-from typing import List, Mapping, Optional, Tuple, Union, cast
+from collections.abc import Mapping
+from typing import Optional, Union, cast
 
 from typing_extensions import assert_never
 
@@ -92,7 +93,7 @@ class InputPreprocessor:
 
         return dec_start_token_id
 
-    def _get_default_enc_dec_decoder_prompt(self) -> List[int]:
+    def _get_default_enc_dec_decoder_prompt(self) -> list[int]:
         '''
         Specifically for encoder/decoder models:
         generate a default decoder prompt for when
@@ -130,8 +131,8 @@ class InputPreprocessor:
 
     def _prepare_decoder_input_ids_for_generation(
         self,
-        decoder_input_ids: Optional[List[int]],
-    ) -> List[int]:
+        decoder_input_ids: Optional[list[int]],
+    ) -> list[int]:
         """
         Prepares `decoder_input_ids` for generation with encoder-decoder models.
 
@@ -168,9 +169,9 @@ class InputPreprocessor:
 
     def _apply_prompt_adapter(
         self,
-        prompt_token_ids: List[int],
+        prompt_token_ids: list[int],
         prompt_adapter_request: Optional[PromptAdapterRequest],
-    ) -> List[int]:
+    ) -> list[int]:
         if prompt_adapter_request:
             prompt_token_ids = (
                 [0] * prompt_adapter_request.prompt_adapter_num_virtual_tokens
@@ -183,7 +184,7 @@ class InputPreprocessor:
         prompt: str,
         request_id: str,
         lora_request: Optional[LoRARequest],
-    ) -> List[int]:
+    ) -> list[int]:
         """
         Apply the model's tokenizer to a text prompt, returning the
         corresponding token IDs.
@@ -211,7 +212,7 @@ class InputPreprocessor:
         prompt: str,
         request_id: str,
         lora_request: Optional[LoRARequest],
-    ) -> List[int]:
+    ) -> list[int]:
         """Async version of :meth:`_tokenize_prompt`."""
         tokenizer = self.get_tokenizer_group()
         add_special_tokens = None
@@ -250,7 +251,7 @@ class InputPreprocessor:
 
     def _process_multimodal(
         self,
-        prompt: Union[str, List[int]],
+        prompt: Union[str, list[int]],
         mm_data: MultiModalDataDict,
         mm_processor_kwargs: Optional[Mapping[str, object]],
         lora_request: Optional[LoRARequest],
@@ -280,7 +281,7 @@ class InputPreprocessor:
 
     async def _process_multimodal_async(
         self,
-        prompt: Union[str, List[int]],
+        prompt: Union[str, list[int]],
         mm_data: MultiModalDataDict,
         mm_processor_kwargs: Optional[Mapping[str, object]],
         lora_request: Optional[LoRARequest],
@@ -511,7 +512,7 @@ class InputPreprocessor:
         self,
         inputs: SingletonInputs,
         decoder_inputs_to_override: Optional[SingletonInputs] = None,
-    ) -> Tuple[SingletonInputs, SingletonInputs]:
+    ) -> tuple[SingletonInputs, SingletonInputs]:
         """
         For encoder/decoder models only:
         Separate Encoder/Decoder inputs from a MultiModalEncDecInputs

vllm/inputs/registry.py

@@ -2,9 +2,10 @@
 
 import functools
 from collections import UserDict
+from collections.abc import Mapping
 from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Any, Callable, Mapping, NamedTuple,
-                    Optional, Protocol, Union)
+from typing import (TYPE_CHECKING, Any, Callable, NamedTuple, Optional,
+                    Protocol, Union)
 
 from torch import nn
 from transformers import BatchFeature, PretrainedConfig, ProcessorMixin

vllm/multimodal/base.py

@@ -2,9 +2,10 @@
 
 from abc import ABC, abstractmethod
 from collections import defaultdict
+from collections.abc import Sequence
 from pathlib import Path
 from typing import (TYPE_CHECKING, Any, Callable, Generic, NamedTuple,
-                    Optional, Sequence, Tuple, Type, TypeVar, Union)
+                    Optional, TypeVar, Union)
 
 from torch import nn
 
@@ -39,7 +40,7 @@ model. This does not include tokens that correspond to the input text.
 """
 
 _T = TypeVar("_T")
-N = TypeVar("N", bound=Type[nn.Module])
+N = TypeVar("N", bound=type[nn.Module])
 
 
 class MultiModalPlugin(ABC):
@@ -274,7 +275,7 @@ class MultiModalPlaceholderMap:
     @classmethod
     def from_seq_group(
         cls, seq_group: "SequenceGroupMetadata", positions: range
-    ) -> Tuple[Optional[MultiModalDataDict], dict[str,
+    ) -> tuple[Optional[MultiModalDataDict], dict[str,
                                                   "MultiModalPlaceholderMap"]]:
         """
         Returns the multi-modal items that intersect with the portion of a

vllm/multimodal/hasher.py

 # SPDX-License-Identifier: Apache-2.0
 
 import pickle
-from typing import TYPE_CHECKING, Iterable, Mapping, Optional
+from collections.abc import Iterable, Mapping
+from typing import TYPE_CHECKING, Optional
 
 import numpy as np
 import torch

vllm/multimodal/image.py

@@ -3,7 +3,7 @@
 import base64
 from io import BytesIO
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Dict, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 from PIL import Image
@@ -31,7 +31,7 @@ class ImagePlugin(MultiModalPlugin):
     def _get_hf_image_processor(
         self,
         model_config: "ModelConfig",
-        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+        mm_processor_kwargs: Optional[dict[str, Any]] = None,
     ):
         if mm_processor_kwargs is None:
             mm_processor_kwargs = {}

vllm/multimodal/registry.py

@@ -2,9 +2,9 @@
 
 import functools
 from collections import UserDict
+from collections.abc import Mapping, Sequence
 from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Any, Dict, Generic, Mapping, Optional,
-                    Protocol, Sequence, Type, TypeVar)
+from typing import TYPE_CHECKING, Any, Generic, Optional, Protocol, TypeVar
 
 import torch.nn as nn
 
@@ -29,7 +29,7 @@ if TYPE_CHECKING:
 
 logger = init_logger(__name__)
 
-N = TypeVar("N", bound=Type[nn.Module])
+N = TypeVar("N", bound=type[nn.Module])
 _I = TypeVar("_I", bound=BaseProcessingInfo)
 _I_co = TypeVar("_I_co", bound=BaseProcessingInfo, covariant=True)
 
@@ -83,13 +83,13 @@ class _ProcessorFactories(Generic[_I]):
         return self.processor(info, dummy_inputs_builder, cache=cache)
 
 
-class _MultiModalLimits(UserDict["ModelConfig", Dict[str, int]]):
+class _MultiModalLimits(UserDict["ModelConfig", dict[str, int]]):
     """
     Wraps `_limits_by_model` for a more informative error message
     when attempting to access a model that does not exist.
     """
 
-    def __getitem__(self, key: "ModelConfig") -> Dict[str, int]:
+    def __getitem__(self, key: "ModelConfig") -> dict[str, int]:
         try:
             return super().__getitem__(key)
         except KeyError as exc:
@@ -170,7 +170,7 @@ class MultiModalRegistry:
         self,
         model_config: "ModelConfig",
         data: MultiModalDataDict,
-        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+        mm_processor_kwargs: Optional[dict[str, Any]] = None,
     ) -> MultiModalKwargs:
         """
         Apply an input mapper to the data passed to the model.
@@ -184,7 +184,7 @@ class MultiModalRegistry:
         Note:
             This should be called after :meth:`init_mm_limits_per_prompt`.
         """
-        merged_dict: Dict[str, NestedTensors] = {}
+        merged_dict = dict[str, NestedTensors]()
 
         for data_key, data_value in data.items():
             plugin = self._get_plugin(data_key)

vllm/multimodal/video.py

@@ -4,7 +4,7 @@ import base64
 from functools import partial
 from io import BytesIO
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Dict, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import numpy as np
 import numpy.typing as npt
@@ -39,7 +39,7 @@ class VideoPlugin(ImagePlugin):
     def _get_hf_video_processor(
         self,
         model_config: "ModelConfig",
-        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+        mm_processor_kwargs: Optional[dict[str, Any]] = None,
     ):
         if mm_processor_kwargs is None:
             mm_processor_kwargs = {}

vllm/usage/usage_lib.py

@@ -9,7 +9,7 @@ import time
 from enum import Enum
 from pathlib import Path
 from threading import Thread
-from typing import Any, Dict, Optional, Union
+from typing import Any, Optional, Union
 from uuid import uuid4
 
 import cpuinfo
@@ -27,7 +27,7 @@ _USAGE_STATS_DO_NOT_TRACK_PATH = os.path.join(_config_home, "do_not_track")
 _USAGE_STATS_ENABLED = None
 _USAGE_STATS_SERVER = envs.VLLM_USAGE_STATS_SERVER
 
-_GLOBAL_RUNTIME_DATA: Dict[str, Union[str, int, bool]] = {}
+_GLOBAL_RUNTIME_DATA = dict[str, Union[str, int, bool]]()
 
 _USAGE_ENV_VARS_TO_COLLECT = [
     "VLLM_USE_MODELSCOPE",
@@ -150,7 +150,7 @@ class UsageMessage:
     def report_usage(self,
                      model_architecture: str,
                      usage_context: UsageContext,
-                     extra_kvs: Optional[Dict[str, Any]] = None) -> None:
+                     extra_kvs: Optional[dict[str, Any]] = None) -> None:
         t = Thread(target=self._report_usage_worker,
                    args=(model_architecture, usage_context, extra_kvs or {}),
                    daemon=True)
@@ -158,13 +158,13 @@ class UsageMessage:
 
     def _report_usage_worker(self, model_architecture: str,
                              usage_context: UsageContext,
-                             extra_kvs: Dict[str, Any]) -> None:
+                             extra_kvs: dict[str, Any]) -> None:
         self._report_usage_once(model_architecture, usage_context, extra_kvs)
         self._report_continous_usage()
 
     def _report_usage_once(self, model_architecture: str,
                            usage_context: UsageContext,
-                           extra_kvs: Dict[str, Any]) -> None:
+                           extra_kvs: dict[str, Any]) -> None:
         # Platform information
         from vllm.platforms import current_platform
         if current_platform.is_cuda_alike():
@@ -227,7 +227,7 @@ class UsageMessage:
             self._write_to_file(data)
             self._send_to_server(data)
 
-    def _send_to_server(self, data: Dict[str, Any]) -> None:
+    def _send_to_server(self, data: dict[str, Any]) -> None:
         try:
             global_http_client = global_http_connection.get_sync_client()
             global_http_client.post(_USAGE_STATS_SERVER, json=data)
@@ -235,7 +235,7 @@ class UsageMessage:
             # silently ignore unless we are using debug log
             logging.debug("Failed to send usage data to server")
 
-    def _write_to_file(self, data: Dict[str, Any]) -> None:
+    def _write_to_file(self, data: dict[str, Any]) -> None:
         os.makedirs(os.path.dirname(_USAGE_STATS_JSON_PATH), exist_ok=True)
         Path(_USAGE_STATS_JSON_PATH).touch(exist_ok=True)
         with open(_USAGE_STATS_JSON_PATH, "a") as f: