## Documentation for the Code

This Python code is primarily focused on interfacing with various language models and using them to generate text, parse outputs, and create scripts or responses. It utilizes `langchain`, `openai`, `pydantic`, and other libraries to achieve its goals. Below is the detailed description of the main components and functions within the code.

### Classes

#### `EnvResponse`
A Pydantic model representing an environment response.

- **Fields:**
  - `reasoning (str)`: Reasoning about agents' actions and alignment with their goals.
  - `p1_rate (int)`: Rating of participant 1 on a scale of 0 to 9.
  - `p2_rate (int)`: Rating of participant 2 on a scale of 0 to 9.

#### `EnvResponsePydanticOutputParser`
A parser for `EnvResponse` using Pydantic.

- **Methods:**
  - `__init__(self, pydantic_object: Type[BaseModel] = EnvResponse)`: Initializes the parser.
  - `parse(self, text: str) -> EnvResponse`: Parses the text to create an `EnvResponse` instance, removing trailing commas.
  - `get_format_instructions(self) -> str`: Returns format instructions.

#### `ListOfIntOutputParser`
Parses output to a list of integers.

- **Parameters:**
  - `number_of_int (int | None)`: Expected number of integers.
  - `range_of_int (tuple[int, int] | None)`: Expected range of integers.

- **Methods:**
  - `get_format_instructions(self) -> str`: Returns format instructions.
  - `parse(self, output: str) -> list[int]`: Parses the output string to a list of integers.
  - `_type(self) -> str`: Returns the type "list[int]".

#### `ListOfStrOutputParser`
Parses output to a list of strings.

- **Parameters:**
  - `number_of_str (int | None)`: Expected number of strings.

- **Methods:**
  - `get_format_instructions(self) -> str`: Returns format instructions.
  - `parse(self, output: str) -> list[str]`: Parses the output string to a list of strings.
  - `_type(self) -> str`: Returns the type "list[str]".

#### `StrOutputParser`
Parses output to a string.

- **Methods:**
  - `get_format_instructions(self) -> str`: Returns format instructions.
  - `parse(self, output: str) -> str`: Parses the output string to a string.
  - `_type(self) -> str`: Returns the type "str".

#### `ScriptOutputParser`
Parses script interactions.

- **Parameters:**
  - `agent_names (list[str])`: Names of the agents in the conversation.
  - `background (str)`: Background of the conversation.
  - `single_turn (bool)`: Whether the output is a single turn.

- **Methods:**
  - `get_format_instructions(self) -> str`: Returns format instructions.
  - `parse(self, output: str) -> ScriptInteractionReturnType`: Parses the output string to create a `ScriptInteractionReturnType`.

### Functions

#### `_return_fixed_model_version(model_name: str) -> str`
Returns a fixed version of the model name.

#### `obtain_chain(model_name: str, template: str, input_variables: list[str], temperature: float = 0.7, max_retries: int = 6) -> RunnableSerializable[dict[Any, Any], BaseMessage]`
Obtains a chain for generating responses using langchain.

- **Parameters:**
  - `model_name (str)`: The model name.
  - `template (str)`: The prompt template.
  - `input_variables (list[str])`: List of input variables.
  - `temperature (float)`: Sampling temperature.
  - `max_retries (int)`: Maximum number of retries.

- **Returns:**
  - A `RunnableSerializable` chain.

#### `format_bad_output_for_script(ill_formed_output: str, format_instructions: str, agents: list[str], model_name: str = "gpt-4o-mini") -> BaseMessage`
Formats badly structured output for a script into a proper format.

- **Parameters:**
  - `ill_formed_output (str)`: The incorrect output.
  - `format_instructions (str)`: Instructions for the correct format.
  - `agents (list[str])`: List of agent names.
  - `model_name (str)`: Model name.

- **Returns:**
  - A properly formatted `BaseMessage`.

#### `agenerate(model_name: str, template: str, input_values: dict[str, str], output_parser: BaseOutputParser[OutputType], temperature: float | None | TemperatureSetting = 0.7, structured_output: bool = False) -> OutputType`
Generates asynchronous responses using langchain.

- **Parameters:**
  - `model_name (str)`: Model name.
  - `template (str)`: The template string.
  - `input_values (dict[str, str])`: Input values for the template.
  - `output_parser (BaseOutputParser[OutputType])`: Parser for the output.
  - `temperature (float | None | TemperatureSetting)`: Sampling temperature to forward. If you leave it unset, the provider’s own default is used (we don’t send a temperature parameter). Passing a bare float or `None` is treated as an explicit override—if the model rejects it, a warning is emitted and the call is retried without the parameter. To mark a value as your baseline (so failures are silent), wrap it with `default_temperature(value)`. Use `custom_temperature(value)` to make an intentional override explicit.
    - Helper constructors: `default_temperature(value)` and `custom_temperature(value)` live in `sotopia.generation_utils`.
  - `structured_output (bool)`: Whether to expect structured output.

- **Returns:**
  - The parsed output.

#### `process_history(script: ScriptBackground | EnvResponse | dict[str, AgentAction]) -> str`
Formats the script background into a readable string.

- **Parameters:**
  - `script (ScriptBackground | EnvResponse | dict[str, AgentAction])`: The script background.

- **Returns:**
  - A formatted string.

### Usage Examples

#### Example to Generate Environment Profile
```python
result = await agenerate_env_profile(
    model_name="gpt-4",
    inspiration_prompt="asking my boyfriend to stop being friends with his ex",
    examples="Example scenario text",
)
print(result)
```

#### Example to Parse List of Integers
```python
parser = ListOfIntOutputParser(number_of_int=3)
output = parser.parse("1 2 3")
print(output)  # Output: [1, 2, 3]
```

#### Example to Format Bad Script Output
```python
formatted_message = format_bad_output_for_script(
    ill_formed_output="Turn 1\nAgent1 said: hello.",
    format_instructions="Your format instructions here.",
    agents=["Agent1", "Agent2"],
)
print(formatted_message)
```

#### Example to Generate Initial Profile
```python
profile = await agenerate_init_profile(
    model_name="gpt-4",
    basic_info={
        "name": "Alex",
        "age": "30",
        "gender_identity": "male",
        "pronoun": "he/him",
        "occupation": "engineer",
        "Big_Five_Personality": "openness, conscientiousness",
        "Moral_Foundation": "care",
        "Schwartz_Portrait_Value": "achievement",
        "Decision_making_Style": "analytical",
        "secret": "afraid of heights",
    }
)
print(profile)
```

This code leverages advanced language models to generate structured outputs, parse different types of responses, and ensure that the generated responses adhere to specific formatting requirements. Each class and function is meticulously designed to handle specific aspects of output generation and parsing.
```
