first commit, seems to be working

Author: jstmn

google/generativeai/answer.py

@@ -15,9 +15,10 @@
 from __future__ import annotations
 
 import dataclasses
-from collections.abc import Iterable
+from typing import Iterable
 import itertools
 from typing import Any, Iterable, Union, Mapping, Optional
+from typing import Dict, List, Tuple
 from typing_extensions import TypedDict
 
 import google.ai.generativelanguage as glm
@@ -40,7 +41,7 @@
 
 AnswerStyleOptions = Union[int, str, AnswerStyle]
 
-_ANSWER_STYLES: dict[AnswerStyleOptions, AnswerStyle] = {
+_ANSWER_STYLES: Dict[AnswerStyleOptions, AnswerStyle] = {
     AnswerStyle.ANSWER_STYLE_UNSPECIFIED: AnswerStyle.ANSWER_STYLE_UNSPECIFIED,
     0: AnswerStyle.ANSWER_STYLE_UNSPECIFIED,
     "answer_style_unspecified": AnswerStyle.ANSWER_STYLE_UNSPECIFIED,
@@ -68,7 +69,7 @@ def to_answer_style(x: AnswerStyleOptions) -> AnswerStyle:
 
 GroundingPassageOptions = (
     Union[
-        protos.GroundingPassage, tuple[str, content_types.ContentType], content_types.ContentType
+        protos.GroundingPassage, Tuple[str, content_types.ContentType], content_types.ContentType
     ],
 )
 

google/generativeai/client.py

@@ -6,7 +6,8 @@
 import pathlib
 import types
 from typing import Any, cast
-from collections.abc import Sequence
+from typing import Dict, List, Tuple
+from typing import Sequence
 import httplib2
 
 import google.ai.generativelanguage as glm
@@ -105,12 +106,12 @@ async def create_file(self, *args, **kwargs):
 
 @dataclasses.dataclass
 class _ClientManager:
-    client_config: dict[str, Any] = dataclasses.field(default_factory=dict)
-    default_metadata: Sequence[tuple[str, str]] = ()
+    client_config: Dict[str, Any] = dataclasses.field(default_factory=dict)
+    default_metadata: Sequence[Tuple[str, str]] = ()
 
     discuss_client: glm.DiscussServiceClient | None = None
     discuss_async_client: glm.DiscussServiceAsyncClient | None = None
-    clients: dict[str, Any] = dataclasses.field(default_factory=dict)
+    clients: Dict[str, Any] = dataclasses.field(default_factory=dict)
 
     def configure(
         self,
@@ -124,9 +125,9 @@ def configure(
         # We could accept a dict since all the `Transport` classes take the same args,
         # but that seems rare. Users that need it can just switch to the low level API.
         transport: str | None = None,
-        client_options: client_options_lib.ClientOptions | dict[str, Any] | None = None,
+        client_options: client_options_lib.ClientOptions | Dict[str, Any] | None = None,
         client_info: gapic_v1.client_info.ClientInfo | None = None,
-        default_metadata: Sequence[tuple[str, str]] = (),
+        default_metadata: Sequence[Tuple[str, str]] = (),
     ) -> None:
         """Initializes default client configurations using specified parameters or environment variables.
 
@@ -282,7 +283,7 @@ def configure(
     transport: str | None = None,
     client_options: client_options_lib.ClientOptions | dict | None = None,
     client_info: gapic_v1.client_info.ClientInfo | None = None,
-    default_metadata: Sequence[tuple[str, str]] = (),
+    default_metadata: Sequence[Tuple[str, str]] = (),
 ):
     """Captures default client configuration.
 

google/generativeai/embedding.py

@@ -16,7 +16,7 @@
 
 import itertools
 from typing import Any, Iterable, overload, TypeVar, Union, Mapping
-
+from typing import Dict, List, Tuple
 import google.ai.generativelanguage as glm
 from google.generativeai import protos
 
@@ -35,7 +35,7 @@
 
 EmbeddingTaskTypeOptions = Union[int, str, EmbeddingTaskType]
 
-_EMBEDDING_TASK_TYPE: dict[EmbeddingTaskTypeOptions, EmbeddingTaskType] = {
+_EMBEDDING_TASK_TYPE: Dict[EmbeddingTaskTypeOptions, EmbeddingTaskType] = {
     EmbeddingTaskType.TASK_TYPE_UNSPECIFIED: EmbeddingTaskType.TASK_TYPE_UNSPECIFIED,
     0: EmbeddingTaskType.TASK_TYPE_UNSPECIFIED,
     "task_type_unspecified": EmbeddingTaskType.TASK_TYPE_UNSPECIFIED,
@@ -81,7 +81,7 @@ def to_task_type(x: EmbeddingTaskTypeOptions) -> EmbeddingTaskType:
 except AttributeError:
     T = TypeVar("T")
 
-    def _batched(iterable: Iterable[T], n: int) -> Iterable[list[T]]:
+    def _batched(iterable: Iterable[T], n: int) -> Iterable[List[T]]:
         if n < 1:
             raise ValueError(
                 f"Invalid input: The batch size 'n' must be a positive integer. You entered: {n}. Please enter a number greater than 0."

google/generativeai/generative_models.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from collections.abc import Iterable
+from typing import Iterable
 import textwrap
 from typing import Any, Union, overload
 import reprlib
@@ -278,7 +278,7 @@ def generate_content(
 
         ### Input type flexibility
 
-        While the underlying API strictly expects a `list[protos.Content]` objects, this method
+        While the underlying API strictly expects a `List[protos.Content]` objects, this method
         will convert the user input into the correct type. The hierarchy of types that can be
         converted is below. Any of these objects can be passed as an equivalent `dict`.
 
@@ -504,7 +504,7 @@ def __init__(
         enable_automatic_function_calling: bool = False,
     ):
         self.model: GenerativeModel = model
-        self._history: list[protos.Content] = content_types.to_contents(history)
+        self._history: List[protos.Content] = content_types.to_contents(history)
         self._last_sent: protos.Content | None = None
         self._last_received: generation_types.BaseGenerateContentResponse | None = None
         self.enable_automatic_function_calling = enable_automatic_function_calling
@@ -615,7 +615,7 @@ def _check_response(self, *, response, stream):
             ):
                 raise generation_types.StopCandidateException(response.candidates[0])
 
-    def _get_function_calls(self, response) -> list[protos.FunctionCall]:
+    def _get_function_calls(self, response) -> List[protos.FunctionCall]:
         candidates = response.candidates
         if len(candidates) != 1:
             raise ValueError(
@@ -635,14 +635,14 @@ def _handle_afc(
         stream,
         tools_lib,
         request_options,
-    ) -> tuple[list[protos.Content], protos.Content, generation_types.BaseGenerateContentResponse]:
+    ) -> Tuple[List[protos.Content], protos.Content, generation_types.BaseGenerateContentResponse]:
 
         while function_calls := self._get_function_calls(response):
             if not all(callable(tools_lib[fc]) for fc in function_calls):
                 break
             history.append(response.candidates[0].content)
 
-            function_response_parts: list[protos.Part] = []
+            function_response_parts: List[protos.Part] = []
             for fc in function_calls:
                 fr = tools_lib(fc)
                 assert fr is not None, (
@@ -742,14 +742,14 @@ async def _handle_afc_async(
         stream,
         tools_lib,
         request_options,
-    ) -> tuple[list[protos.Content], protos.Content, generation_types.BaseGenerateContentResponse]:
+    ) -> Tuple[List[protos.Content], protos.Content, generation_types.BaseGenerateContentResponse]:
 
         while function_calls := self._get_function_calls(response):
             if not all(callable(tools_lib[fc]) for fc in function_calls):
                 break
             history.append(response.candidates[0].content)
 
-            function_response_parts: list[protos.Part] = []
+            function_response_parts: List[protos.Part] = []
             for fc in function_calls:
                 fr = tools_lib(fc)
                 assert fr is not None, (
@@ -782,7 +782,7 @@ def __copy__(self):
             history=list(self.history),
         )
 
-    def rewind(self) -> tuple[protos.Content, protos.Content]:
+    def rewind(self) -> Tuple[protos.Content, protos.Content]:
         """Removes the last request/response pair from the chat history."""
         if self._last_received is None:
             result = self._history.pop(-2), self._history.pop()
@@ -799,7 +799,7 @@ def last(self) -> generation_types.BaseGenerateContentResponse | None:
         return self._last_received
 
     @property
-    def history(self) -> list[protos.Content]:
+    def history(self) -> List[protos.Content]:
         """The chat history."""
         last = self._last_received
         if last is None:

google/generativeai/models.py

@@ -16,7 +16,7 @@
 
 import typing
 from typing import Any, Literal
-
+from typing import Dict, List, Tuple
 import google.ai.generativelanguage as glm
 
 from google.generativeai import protos
@@ -382,7 +382,7 @@ def update_tuned_model(
 @typing.overload
 def update_tuned_model(
     tuned_model: str,
-    updates: dict[str, Any],
+    updates: Dict[str, Any],
     *,
     client: glm.ModelServiceClient | None = None,
     request_options: helper_types.RequestOptionsType | None = None,
@@ -392,7 +392,7 @@ def update_tuned_model(
 
 def update_tuned_model(
     tuned_model: str | protos.TunedModel,
-    updates: dict[str, Any] | None = None,
+    updates: Dict[str, Any] | None = None,
     *,
     client: glm.ModelServiceClient | None = None,
     request_options: helper_types.RequestOptionsType | None = None,

google/generativeai/notebook/argument_parser.py

@@ -92,7 +92,7 @@ class ArgumentParser(argparse.ArgumentParser):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._messages: list[str] = []
+        self._messages: List[str] = []
 
     def _print_message(self, message, file=None):
         """Override ArgumentParser's _print_message() method."""

google/generativeai/notebook/cmd_line_parser.py

@@ -63,7 +63,7 @@ def _fn(var_name: str) -> llmfn_inputs_source.LLMFnInputsSource:
 
 def _resolve_compare_fn_var(
     name: str,
-) -> tuple[str, parsed_args_lib.TextResultCompareFn]:
+) -> Tuple[str, parsed_args_lib.TextResultCompareFn]:
     """Resolves a value passed into --compare_fn."""
     fn = py_utils.get_py_var(name)
     if not isinstance(fn, Callable):
@@ -320,7 +320,7 @@ def _create_compare_parser(
     # Add positional arguments.
     def _resolve_llm_function_fn(
         var_name: str,
-    ) -> tuple[str, llm_function.LLMFunction]:
+    ) -> Tuple[str, llm_function.LLMFunction]:
         try:
             py_utils.validate_var_name(var_name)
         except ValueError as e:
@@ -426,7 +426,7 @@ class CmdLineParser:
     def _split_post_processing_tokens(
         cls,
         tokens: Sequence[str],
-    ) -> tuple[Sequence[str], parsed_args_lib.PostProcessingTokens]:
+    ) -> Tuple[Sequence[str], parsed_args_lib.PostProcessingTokens]:
         """Splits inputs into the command and post processing tokens.
 
         The command is represented as a sequence of tokens.
@@ -459,7 +459,7 @@ def _split_post_processing_tokens(
     @classmethod
     def _tokenize_line(
         cls, line: str
-    ) -> tuple[Sequence[str], parsed_args_lib.PostProcessingTokens]:
+    ) -> Tuple[Sequence[str], parsed_args_lib.PostProcessingTokens]:
         """Parses `line` and returns command line and post processing tokens."""
         # Check to make sure there is a command at the start. If not, add the
         # default command to the list of tokens.
@@ -476,7 +476,7 @@ def _tokenize_line(
     @classmethod
     def _get_model_args(
         cls, parsed_results: MutableMapping[str, Any]
-    ) -> tuple[MutableMapping[str, Any], model_lib.ModelArguments]:
+    ) -> Tuple[MutableMapping[str, Any], model_lib.ModelArguments]:
         """Extracts fields for model args from `parsed_results`.
 
         Keys specific to model arguments will be removed from `parsed_results`.
@@ -503,7 +503,7 @@ def parse_line(
         self,
         line: str,
         placeholders: AbstractSet[str] | None = None,
-    ) -> tuple[parsed_args_lib.ParsedArgs, parsed_args_lib.PostProcessingTokens]:
+    ) -> Tuple[parsed_args_lib.ParsedArgs, parsed_args_lib.PostProcessingTokens]:
         """Parses the commandline and returns ParsedArgs and post-processing tokens.
 
         Args:

google/generativeai/notebook/command_utils.py

@@ -53,7 +53,7 @@ def _call_impl(
                 )
             )
 
-        outputs: list[llmfn_outputs.LLMFnOutputEntry] = []
+        outputs: List[llmfn_outputs.LLMFnOutputEntry] = []
         for idx, (value, prompt_vars) in enumerate(zip(self._data, normalized_inputs)):
             output_row = llmfn_output_row.LLMFnOutputRow(
                 data={
@@ -87,7 +87,7 @@ def create_llm_function(
     post_processing_fns: Sequence[post_process_utils.ParsedPostProcessExpr],
 ) -> llm_function.LLMFunction:
     """Creates an LLMFunction from Command.execute() arguments."""
-    prompts: list[str] = [cell_content]
+    prompts: List[str] = [cell_content]
 
     llmfn_outputs_display_fn = _get_ipython_display_fn(env) if env else None
 
@@ -106,8 +106,8 @@ def create_llm_function(
 
 
 def _convert_simple_compare_fn(
-    name_and_simple_fn: tuple[str, Callable[[str, str], Any]]
-) -> tuple[str, llm_function.CompareFn]:
+    name_and_simple_fn: Tuple[str, Callable[[str, str], Any]]
+) -> Tuple[str, llm_function.CompareFn]:
     simple_fn = name_and_simple_fn[1]
     new_fn = lambda x, y: simple_fn(x.result_value(), y.result_value())
     return name_and_simple_fn[0], new_fn

google/generativeai/notebook/flag_def.py

@@ -107,7 +107,7 @@ class FlagDef(abc.ABC):
     dest_type: type[_DESTTYPES] | None = None
     parse_to_dest_type_fn: _PARSEFN | None = None
 
-    choices: list[_PARSETYPES] | None = None
+    choices: List[_PARSETYPES] | None = None
     help_msg: str | None = None
 
     @abc.abstractmethod

google/generativeai/notebook/gspread_client.py

@@ -68,7 +68,7 @@ def get_all_records(
         self,
         sid: sheets_id.SheetsIdentifier,
         worksheet_id: int,
-    ) -> tuple[Sequence[Mapping[str, str]], Callable[[], None]]:
+    ) -> Tuple[Sequence[Mapping[str, str]], Callable[[], None]]:
         """Returns all records for a Google Sheets worksheet."""
 
     @abc.abstractmethod
@@ -126,7 +126,7 @@ def get_all_records(
         self,
         sid: sheets_id.SheetsIdentifier,
         worksheet_id: int,
-    ) -> tuple[Sequence[Mapping[str, str]], Callable[[], None]]:
+    ) -> Tuple[Sequence[Mapping[str, str]], Callable[[], None]]:
         sheet = self._open(sid)
         worksheet = sheet.get_worksheet(worksheet_id)
 
@@ -195,7 +195,7 @@ def get_all_records(
         self,
         sid: sheets_id.SheetsIdentifier,
         worksheet_id: int,
-    ) -> tuple[Sequence[Mapping[str, str]], Callable[[], None]]:
+    ) -> Tuple[Sequence[Mapping[str, str]], Callable[[], None]]:
         raise _get_import_error()
 
     def write_records(