Fix code quality issues

src/transformers/agents/agent_types.py

@@ -16,6 +16,7 @@
 import pathlib
 import tempfile
 import uuid
+
 import numpy as np
 
 from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging

src/transformers/agents/agents.py

@@ -29,14 +29,14 @@
     DEFAULT_CODE_SYSTEM_PROMPT,
     DEFAULT_REACT_CODE_SYSTEM_PROMPT,
     DEFAULT_REACT_JSON_SYSTEM_PROMPT,
+    PLAN_UPDATE_FINAL_PLAN_REDACTION,
     SYSTEM_PROMPT_FACTS,
-    SYSTEM_PROMPT_PLAN,
-    USER_PROMPT_PLAN,
     SYSTEM_PROMPT_FACTS_UPDATE,
+    SYSTEM_PROMPT_PLAN,
     SYSTEM_PROMPT_PLAN_UPDATE,
     USER_PROMPT_FACTS_UPDATE,
+    USER_PROMPT_PLAN,
     USER_PROMPT_PLAN_UPDATE,
-    PLAN_UPDATE_FINAL_PLAN_REDACTION
 )
 from .python_interpreter import LIST_SAFE_MODULES, evaluate_python_code
 from .tools import (
@@ -419,11 +419,11 @@ def write_inner_memory_from_logs(self, summary_mode: Optional[bool] = False) ->
                 thought_message = {"role": MessageRole.ASSISTANT, "content": step_log["llm_output"].strip()}
                 memory.append(thought_message)
             if "facts" in step_log:
-                thought_message = {"role": MessageRole.ASSISTANT, "content": f"[FACTS LIST]:\n" + step_log["facts"].strip()}
+                thought_message = {"role": MessageRole.ASSISTANT, "content": "[FACTS LIST]:\n" + step_log["facts"].strip()}
                 memory.append(thought_message)
 
             if "plan" in step_log and not summary_mode:
-                thought_message = {"role": MessageRole.ASSISTANT, "content": f"[PLAN]:\n" + step_log["plan"].strip()}
+                thought_message = {"role": MessageRole.ASSISTANT, "content": "[PLAN]:\n" + step_log["plan"].strip()}
                 memory.append(thought_message)
 
             if "tool_call" in step_log and summary_mode:
@@ -783,7 +783,7 @@ def planning_step(self, task, is_first_step=False, iteration: int = None):
             self.logger.debug(final_plan_redaction)
         else:  # update plan
             agent_memory = self.write_inner_memory_from_logs(summary_mode=False) # This will not log the plan but will log facts
-            
+
             # Redact updated facts
             facts_update_system_prompt = {
                 "role": MessageRole.SYSTEM,
@@ -809,7 +809,7 @@ def planning_step(self, task, is_first_step=False, iteration: int = None):
                     remaining_steps = (self.max_iterations - iteration)
                 )}
             plan_update = self.llm_engine([plan_update_message] + agent_memory + [plan_update_message_user], stop_sequences=['<end_plan>'])
-            
+
             # Log final facts and plan
             final_plan_redaction = PLAN_UPDATE_FINAL_PLAN_REDACTION.format(task=task, plan_update=plan_update)
             final_facts_redaction = f"""Here is the updated list of the facts that I know:

src/transformers/agents/prompts.py

@@ -365,7 +365,7 @@ def download_prompt(prompt_or_repo_id, agent_name, mode="run"):
 """
 
 SYSTEM_PROMPT_FACTS = """Below I will present you a task.
-            
+
 You will now build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.
 To do so, you will have to read the task and identify things that must be discovered in order to successfully complete it.
 Don't make any assumptions. For each item, provide a thorough reasoning. Here is how you will structure this survey:
@@ -390,7 +390,7 @@ def download_prompt(prompt_or_repo_id, agent_name, mode="run"):
 SYSTEM_PROMPT_PLAN = """You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools.
 
 Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
-This plan should involve individual tasks based on the avilable tools, that if executed correctly will yield the correct answer. 
+This plan should involve individual tasks based on the avilable tools, that if executed correctly will yield the correct answer.
 Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
 After writing the final step of the plan, write the '\n<end_plan>' tag and stop there."""
 
@@ -472,4 +472,4 @@ def download_prompt(prompt_or_repo_id, agent_name, mode="run"):
 Here is my new/updated plan of action to solve the task:
 ```
 {plan_update}
-```"""
+```"""

src/transformers/agents/python_interpreter.py

@@ -17,11 +17,13 @@
 import ast
 import builtins
 import difflib
-import numpy as np
-import pandas as pd
 from collections.abc import Mapping
 from typing import Any, Callable, Dict, List, Optional
 
+import numpy as np
+import pandas as pd
+
+
 class InterpreterError(ValueError):
     """
     An error raised when the interpretor cannot evaluate a Python expression, due to syntax error or unsupported
@@ -363,7 +365,7 @@ def evaluate_call(call, state, tools):
         if not hasattr(obj, func_name):
             raise InterpreterError(f"Object {obj} has no attribute {func_name}")
         func = getattr(obj, func_name)
-    
+
     elif isinstance(call.func, ast.Name):
         func_name = call.func.id
         if func_name in state:

tests/agents/test_python_interpreter.py

@@ -20,7 +20,7 @@
 
 from transformers import load_tool
 from transformers.agents.agent_types import AGENT_TYPE_MAPPING
-from transformers.agents.default_tools import BASE_PYTHON_TOOLS, LIST_SAFE_MODULES
+from transformers.agents.default_tools import BASE_PYTHON_TOOLS
 from transformers.agents.python_interpreter import InterpreterError, evaluate_python_code
 
 from .test_tools_common import ToolTesterMixin
@@ -420,7 +420,7 @@ def test_additional_imports(self):
         code = "import matplotlib.pyplot as plt"
         evaluate_python_code(code, authorized_imports=["matplotlib.pyplot"], state={})
         evaluate_python_code(code, authorized_imports=["matplotlib"], state={})
-        with pytest.raises(InterpreterError) as e:
+        with pytest.raises(InterpreterError):
             evaluate_python_code(code, authorized_imports=["pyplot"], state={})