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if (
isinstance(quantization_config, (GPTQConfig, AwqConfig, FbgemmFp8Config, CompressedTensorsConfig))
and quantization_config_from_args is not None
):
# special case for GPTQ / AWQ / FbgemmFp8 config collision
loading_attr_dict = quantization_config_from_args.ge... | 357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py |
class ImageQuestionAnsweringTool(PipelineTool):
default_checkpoint = "dandelin/vilt-b32-finetuned-vqa"
description = (
"This is a tool that answers a question about an image. It "
"returns a text that is the answer to the question."
)
name = "image_qa"
pre_processor_class = AutoProce... | 358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/image_question_answering.py |
def forward(self, inputs):
with torch.no_grad():
return self.model(**inputs).logits
def decode(self, outputs):
idx = outputs.argmax(-1).item()
return self.model.config.id2label[idx] | 358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/image_question_answering.py |
class Problem:
"""
A class regrouping all the information to solve a problem on which we will evaluate agents.
Args:
task (`str` ou `list[str]`):
One or several descriptions of the task to perform. If a list, it should contain variations on the
phrasing, but for the same tas... | 359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/evaluate_agent.py |
class ChatMessage:
def __init__(self, role, content, metadata=None):
self.role = role
self.content = content
self.metadata = metadata | 360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py |
class ChatMessage:
def __init__(self, role, content, metadata=None):
self.role = role
self.content = content
self.metadata = metadata | 361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py |
class Monitor:
def __init__(self, tracked_llm_engine):
self.step_durations = []
self.tracked_llm_engine = tracked_llm_engine
if getattr(self.tracked_llm_engine, "last_input_token_count", "Not found") != "Not found":
self.total_input_token_count = 0
self.total_output_t... | 362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py |
if getattr(self.tracked_llm_engine, "last_input_token_count", None) is not None:
self.total_input_token_count += self.tracked_llm_engine.last_input_token_count
self.total_output_token_count += self.tracked_llm_engine.last_output_token_count
logger.info(f"- Input tokens: {self.total_i... | 362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py |
class Tool:
"""
A base class for the functions used by the agent. Subclass this and implement the `__call__` method as well as the
following class attributes: | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
- **description** (`str`) -- A short description of what your tool does, the inputs it expects and the output(s) it
will return. For instance 'This is a tool that downloads a file from a `url`. It takes the `url` as input, and
returns the text contained in the file'.
- **name** (`str`) -- A performative... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
You can also override the method [`~Tool.setup`] if your tool as an expensive operation to perform before being
usable (such as loading a model). [`~Tool.setup`] will be called the first time you use your tool, but not at
instantiation.
"""
name: str
description: str
inputs: Dict[str, Dict[str,... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
for attr, expected_type in required_attributes.items():
attr_value = getattr(self, attr, None)
if attr_value is None:
raise TypeError(f"You must set an attribute {attr}.")
if not isinstance(attr_value, expected_type):
raise TypeError(
... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
f"Input '{input_name}': type '{input_content['type']}' is not an authorized value, should be one of {authorized_types}."
) | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
assert getattr(self, "output_type", None) in authorized_types
if do_validate_forward:
if not isinstance(self, PipelineTool):
signature = inspect.signature(self.forward)
if not set(signature.parameters.keys()) == set(self.inputs.keys()):
raise Excep... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def setup(self):
"""
Overwrite this method here for any operation that is expensive and needs to be executed before you start using
your tool. Such as loading a big model.
"""
self.is_initialized = True
def save(self, output_dir):
"""
Saves the relevant code ... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
Args:
output_dir (`str`): The folder in which you want to save your tool.
"""
os.makedirs(output_dir, exist_ok=True)
# Save module file
if self.__module__ == "__main__":
raise ValueError(
f"We can't save the code defining {self} in {output_dir} as ... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
tool_config = {
"tool_class": full_name,
"description": self.description,
"name": self.name,
"inputs": self.inputs,
"output_type": str(self.output_type),
}
with open(config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tool_... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
@classmethod
def from_hub(
cls,
repo_id: str,
token: Optional[str] = None,
**kwargs,
):
"""
Loads a tool defined on the Hub.
<Tip warning={true}>
Loading a tool from the Hub means that you'll download the tool and execute it locally.
ALWA... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
Args:
repo_id (`str`):
The name of the repo on the Hub where your tool is defined.
token (`str`, *optional*):
The token to identify you on hf.co. If unset, will use the token generated when running
`huggingface-cli login` (stored in `~/.huggingface... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
hub_kwargs = {k: v for k, v in kwargs.items() if k in hub_kwargs_names} | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
# Try to get the tool config first.
hub_kwargs["repo_type"] = get_repo_type(repo_id, **hub_kwargs)
resolved_config_file = cached_file(
repo_id,
TOOL_CONFIG_FILE,
token=token,
**hub_kwargs,
_raise_exceptions_for_gated_repo=False,
_ra... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
f"{repo_id} does not appear to provide a valid configuration in `tool_config.json` or `config.json`."
) | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
with open(resolved_config_file, encoding="utf-8") as reader:
config = json.load(reader)
if not is_tool_config:
if "custom_tool" not in config:
raise EnvironmentError(
f"{repo_id} does not provide a mapping to custom tools in its configuration `config.... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
if len(tool_class.description) == 0:
tool_class.description = custom_tool["description"]
if tool_class.description != custom_tool["description"]:
logger.warning(
f"{tool_class.__name__} implements a different description in its configuration and class. Using the "
... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def push_to_hub(
self,
repo_id: str,
commit_message: str = "Upload tool",
private: Optional[bool] = None,
token: Optional[Union[bool, str]] = None,
create_pr: bool = False,
) -> str:
"""
Upload the tool to the Hub.
For this method to work prop... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
Parameters:
repo_id (`str`):
The name of the repository you want to push your tool to. It should contain your organization name when
pushing to a given organization.
commit_message (`str`, *optional*, defaults to `"Upload tool"`):
Message to commit... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
repo_url = create_repo(
repo_id=repo_id,
token=token,
private=private,
exist_ok=True,
repo_type="space",
space_sdk="gradio",
)
repo_id = repo_url.repo_id
metadata_update(repo_id, {"tags": ["tool"]}, repo_type="space") | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
with tempfile.TemporaryDirectory() as work_dir:
# Save all files.
self.save(work_dir)
logger.info(f"Uploading the following files to {repo_id}: {','.join(os.listdir(work_dir))}")
return upload_folder(
repo_id=repo_id,
commit_message=commit_... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
Args:
space_id (`str`):
The id of the Space on the Hub.
name (`str`):
The name of the tool.
description (`str`):
The description of the tool.
api_name (`str`, *optional*):
The specific api_name to use, if the... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
Examples:
```
image_generator = Tool.from_space(
space_id="black-forest-labs/FLUX.1-schnell",
name="image-generator",
description="Generate an image from a prompt"
)
image = image_generator("Generate an image of a cool surfer in Tahiti")
```
... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class SpaceToolWrapper(Tool):
def __init__(
self,
space_id: str,
name: str,
description: str,
api_name: Optional[str] = None,
token: Optional[str] = None,
):
self.client = Client(space... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
try:
space_description_api = space_description[api_name]
except KeyError:
raise KeyError(f"Could not find specified {api_name=} among available api names.") | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
self.inputs = {}
for parameter in space_description_api["parameters"]:
if not parameter["parameter_has_default"]:
parameter_type = parameter["type"]["type"]
if parameter_type == "object":
parameter_type = "an... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def sanitize_argument_for_prediction(self, arg):
if isinstance(arg, ImageType):
temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
arg.save(temp_file.name)
arg = temp_file.name
if (isinstance(arg, (str, Path)) ... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
output = self.client.predict(*args, api_name=self.api_name, **kwargs)
if isinstance(output, tuple) or isinstance(output, list):
return output[
0
] # Sometime the space also returns the generation seed, in which case the result is at index ... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class GradioToolWrapper(Tool):
def __init__(self, _gradio_tool):
self.name = _gradio_tool.name
self.description = _gradio_tool.description
self.output_type = "string"
self._gradio_tool = _gradio_tool
func_args = list(inspect.sig... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class LangChainToolWrapper(Tool):
def __init__(self, _langchain_tool):
self.name = _langchain_tool.name.lower()
self.description = _langchain_tool.description
self.inputs = _langchain_tool.args.copy()
for input_content in self.inputs.values():
... | 363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class SpaceToolWrapper(Tool):
def __init__(
self,
space_id: str,
name: str,
description: str,
api_name: Optional[str] = None,
token: Optional[str] = None,
):
self.client = Client(space... | 364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
try:
space_description_api = space_description[api_name]
except KeyError:
raise KeyError(f"Could not find specified {api_name=} among available api names.") | 364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
self.inputs = {}
for parameter in space_description_api["parameters"]:
if not parameter["parameter_has_default"]:
parameter_type = parameter["type"]["type"]
if parameter_type == "object":
parameter_type = "an... | 364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def sanitize_argument_for_prediction(self, arg):
if isinstance(arg, ImageType):
temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
arg.save(temp_file.name)
arg = temp_file.name
if (isinstance(arg, (str, Path)) ... | 364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
output = self.client.predict(*args, api_name=self.api_name, **kwargs)
if isinstance(output, tuple) or isinstance(output, list):
return output[
0
] # Sometime the space also returns the generation seed, in which case the result is at index ... | 364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class GradioToolWrapper(Tool):
def __init__(self, _gradio_tool):
self.name = _gradio_tool.name
self.description = _gradio_tool.description
self.output_type = "string"
self._gradio_tool = _gradio_tool
func_args = list(inspect.sig... | 365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class LangChainToolWrapper(Tool):
def __init__(self, _langchain_tool):
self.name = _langchain_tool.name.lower()
self.description = _langchain_tool.description
self.inputs = _langchain_tool.args.copy()
for input_content in self.inputs.values():
... | 366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class PipelineTool(Tool):
"""
A [`Tool`] tailored towards Transformer models. On top of the class attributes of the base class [`Tool`], you will
need to specify:
- **model_class** (`type`) -- The class to use to load the model in this tool.
- **default_checkpoint** (`str`) -- The default checkpoin... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
Args:
model (`str` or [`PreTrainedModel`], *optional*):
The name of the checkpoint to use for the model, or the instantiated model. If unset, will default to the
value of the class attribute `default_checkpoint`.
pre_processor (`str` or `Any`, *optional*):
The name of... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc...), the
CPU otherwise.
device_map (`str` or `dict`, *optional*):
If passed along, will be used to instantiate the model.
model_kwargs (`dict`, *optional*):
Any keyword a... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
pre_processor_class = AutoProcessor
model_class = None
post_processor_class = AutoProcessor
default_checkpoint = None
description = "This is a pipeline tool"
name = "pipeline"
inputs = {"prompt": str}
output_type = str
def __init__(
self,
model=None,
pre_processo... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
if model is None:
if self.default_checkpoint is None:
raise ValueError("This tool does not implement a default checkpoint, you need to pass one.")
model = self.default_checkpoint
if pre_processor is None:
pre_processor = model
self.model = model
... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def setup(self):
"""
Instantiates the `pre_processor`, `model` and `post_processor` if necessary.
"""
if isinstance(self.pre_processor, str):
self.pre_processor = self.pre_processor_class.from_pretrained(self.pre_processor, **self.hub_kwargs)
if isinstance(self.model... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
super().setup()
def encode(self, raw_inputs):
"""
Uses the `pre_processor` to prepare the inputs for the `model`.
"""
return self.pre_processor(raw_inputs)
def forward(self, inputs):
"""
Sends the inputs through the `model`.
"""
with torch.no_gra... | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
encoded_inputs = send_to_device(tensor_inputs, self.device)
outputs = self.forward({**encoded_inputs, **non_tensor_inputs})
outputs = send_to_device(outputs, "cpu")
decoded_outputs = self.decode(outputs)
return handle_agent_outputs(decoded_outputs, self.output_type) | 367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class EndpointClient:
def __init__(self, endpoint_url: str, token: Optional[str] = None):
self.headers = {
**build_hf_headers(token=token),
"Content-Type": "application/json",
}
self.endpoint_url = endpoint_url
@staticmethod
def encode_image(image):
_... | 368 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def __call__(
self,
inputs: Optional[Union[str, Dict, List[str], List[List[str]]]] = None,
params: Optional[Dict] = None,
data: Optional[bytes] = None,
output_image: bool = False,
) -> Any:
# Build payload
payload = {}
if inputs:
payload["i... | 368 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class ToolCollection:
"""
Tool collections enable loading all Spaces from a collection in order to be added to the agent's toolbox.
> [!NOTE]
> Only Spaces will be fetched, so you can feel free to add models and datasets to your collection if you'd
> like for this collection to showcase them.
... | 369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
def __init__(self, collection_slug: str, token: Optional[str] = None):
self._collection = get_collection(collection_slug, token=token)
self._hub_repo_ids = {item.item_id for item in self._collection.items if item.item_type == "space"}
self.tools = {Tool.from_hub(repo_id) for repo_id in self._hub... | 369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class SpecificTool(Tool):
name = parameters["name"]
description = parameters["description"]
inputs = parameters["parameters"]["properties"]
output_type = parameters["return"]["type"]
@wraps(tool_function)
def forward(self, *args, **kwargs):
return tool_functi... | 370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py |
class MessageRole(str, Enum):
USER = "user"
ASSISTANT = "assistant"
SYSTEM = "system"
TOOL_CALL = "tool-call"
TOOL_RESPONSE = "tool-response"
@classmethod
def roles(cls):
return [r.value for r in cls] | 371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
class HfEngine:
def __init__(self, model_id: Optional[str] = None):
self.last_input_token_count = None
self.last_output_token_count = None
if model_id is None:
model_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
logger.warning(f"Using default model for token counting: '{... | 372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
def generate(
self, messages: List[Dict[str, str]], stop_sequences: Optional[List[str]] = None, grammar: Optional[str] = None
):
raise NotImplementedError
def __call__(
self, messages: List[Dict[str, str]], stop_sequences: Optional[List[str]] = None, grammar: Optional[str] = None
) ... | 372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
Parameters:
messages (`List[Dict[str, str]]`):
A list of message dictionaries to be processed. Each dictionary should have the structure `{"role": "user/system", "content": "message content"}`.
stop_sequences (`List[str]`, *optional*):
A list of strings that will ... | 372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
Example:
```python
>>> engine = HfApiEngine(
... model="meta-llama/Meta-Llama-3.1-8B-Instruct",
... token="your_hf_token_here",
... max_tokens=2000
... )
>>> messages = [{"role": "user", "content": "Explain quantum mechanics... | 372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
# Remove stop sequences from LLM output
for stop_seq in stop_sequences:
if response[-len(stop_seq) :] == stop_seq:
response = response[: -len(stop_seq)]
return response | 372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
class HfApiEngine(HfEngine):
"""A class to interact with Hugging Face's Inference API for language model interaction.
This engine allows you to communicate with Hugging Face's models using the Inference API. It can be used in both serverless mode or with a dedicated endpoint, supporting features like stop sequ... | 373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
Parameters:
model (`str`, *optional*, defaults to `"meta-llama/Meta-Llama-3.1-8B-Instruct"`):
The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
token (`str`, *optional*):
Token used by the Hugging Face API ... | 373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
def __init__(
self,
model: str = "meta-llama/Meta-Llama-3.1-8B-Instruct",
token: Optional[str] = None,
max_tokens: Optional[int] = 1500,
timeout: Optional[int] = 120,
):
super().__init__(model_id=model)
self.model = model
self.client = InferenceClient(... | 373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
# Send messages to the Hugging Face Inference API
if grammar is not None:
response = self.client.chat_completion(
messages, stop=stop_sequences, max_tokens=self.max_tokens, response_format=grammar
)
else:
response = self.client.chat_completion(messages... | 373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
class TransformersEngine(HfEngine):
"""This engine uses a pre-initialized local text-generation pipeline."""
def __init__(self, pipeline: Pipeline, model_id: Optional[str] = None):
super().__init__(model_id)
self.pipeline = pipeline
def generate(
self,
messages: List[Dict[s... | 374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
response = output[0]["generated_text"][-1]["content"]
return response | 374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py |
class SpeechToTextTool(PipelineTool):
default_checkpoint = "distil-whisper/distil-large-v3"
description = "This is a tool that transcribes an audio into text. It returns the transcribed text."
name = "transcriber"
pre_processor_class = WhisperProcessor
model_class = WhisperForConditionalGeneration
... | 375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/speech_to_text.py |
class CustomFormatter(logging.Formatter):
grey = "\x1b[38;20m"
bold_yellow = "\x1b[33;1m"
red = "\x1b[31;20m"
green = "\x1b[32;20m"
bold_green = "\x1b[32;20;1m"
bold_red = "\x1b[31;1m"
bold_white = "\x1b[37;1m"
orange = "\x1b[38;5;214m"
bold_orange = "\x1b[38;5;214;1m"
reset = "\... | 376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class Toolbox:
"""
The toolbox contains all tools that the agent can perform operations with, as well as a few methods to
manage them.
Args:
tools (`List[Tool]`):
The list of tools to instantiate the toolbox with
add_base_tools (`bool`, defaults to `False`, *optional*, defau... | 377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def add_base_tools(self, add_python_interpreter: bool = False):
global _tools_are_initialized
global HUGGINGFACE_DEFAULT_TOOLS
if not _tools_are_initialized:
HUGGINGFACE_DEFAULT_TOOLS = setup_default_tools(logger)
_tools_are_initialized = True
for tool in HUGGINGF... | 377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
Args:
tool_description_template (`str`, *optional*):
The template to use to describe the tools. If not provided, the default template will be used.
"""
return "\n".join(
[get_tool_description_with_args(tool, tool_description_template) for tool in self._tools.value... | 377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
Args:
tool_name (`str`):
The tool to remove from the toolbox.
"""
if tool_name not in self._tools:
raise KeyError(
f"Error: tool {tool_name} not found in toolbox for removal, should be instead one of {list(self._tools.keys())}."
)
... | 377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def _load_tools_if_needed(self):
for name, tool in self._tools.items():
if not isinstance(tool, Tool):
task_or_repo_id = tool.task if tool.repo_id is None else tool.repo_id
self._tools[name] = load_tool(task_or_repo_id)
def __repr__(self):
toolbox_descrip... | 377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class AgentError(Exception):
"""Base class for other agent-related exceptions"""
def __init__(self, message):
super().__init__(message)
self.message = message | 378 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class AgentParsingError(AgentError):
"""Exception raised for errors in parsing in the agent"""
pass | 379 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class AgentExecutionError(AgentError):
"""Exception raised for errors in execution in the agent"""
pass | 380 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class AgentMaxIterationsError(AgentError):
"""Exception raised for errors in execution in the agent"""
pass | 381 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class AgentGenerationError(AgentError):
"""Exception raised for errors in generation in the agent"""
pass | 382 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class Agent:
def __init__(
self,
tools: Union[List[Tool], Toolbox],
llm_engine: Callable = None,
system_prompt: Optional[str] = None,
tool_description_template: Optional[str] = None,
additional_args: Dict = {},
max_iterations: int = 6,
tool_parser: Opt... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
tool_description_template if tool_description_template else DEFAULT_TOOL_DESCRIPTION_TEMPLATE
)
self.additional_args = additional_args
self.max_iterations = max_iterations
self.logger = logger
self.tool_parser = tool_parser
self.grammar = grammar | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
self.managed_agents = None
if managed_agents is not None:
self.managed_agents = {agent.name: agent for agent in managed_agents}
if isinstance(tools, Toolbox):
self._toolbox = tools
if add_base_tools:
if not is_torch_available():
ra... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if verbose == 0:
logger.setLevel(logging.WARNING)
elif verbose == 1:
logger.setLevel(logging.INFO)
elif verbose == 2:
logger.setLevel(logging.DEBUG)
# Initialize step callbacks
self.step_callbacks = step_callbacks if step_callbacks is not None else []... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def initialize_for_run(self):
self.token_count = 0
self.system_prompt = format_prompt_with_tools(
self._toolbox,
self.system_prompt_template,
self.tool_description_template,
)
self.system_prompt = format_prompt_with_managed_agents_descriptions(self.sys... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def write_inner_memory_from_logs(self, summary_mode: Optional[bool] = False) -> List[Dict[str, str]]:
"""
Reads past llm_outputs, actions, and observations or errors from the logs into a series of messages
that can be used as input to the LLM.
"""
prompt_message = {"role": Messag... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
"role": MessageRole.ASSISTANT,
"content": "[FACTS LIST]:\n" + step_log["facts"].strip(),
}
memory.append(thought_message) | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if "plan" in step_log and not summary_mode:
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:
tool_call_message = {
... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if "error" in step_log or "observation" in step_log:
if "error" in step_log:
message_content = (
f"[OUTPUT OF STEP {i}] -> Error:\n"
+ str(step_log["error"])
+ "\nNow let's retry: take care not to repeat previous... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def extract_action(self, llm_output: str, split_token: str) -> str:
"""
Parse action from the LLM output
Args:
llm_output (`str`): Output of the LLM
split_token (`str`): Separator for the action. Should match the example in the system prompt.
"""
try:
... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def execute_tool_call(self, tool_name: str, arguments: Dict[str, str]) -> Any:
"""
Execute tool with the provided input and returns the result.
This method replaces arguments with the actual values from the state if they refer to state variables.
Args:
tool_name (`str`): Nam... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
try:
if isinstance(arguments, str):
observation = available_tools[tool_name](arguments)
elif isinstance(arguments, dict):
for key, value in arguments.items():
# if the value is the name of a state variable like "image.png", replace it with the ... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
f"As a reminder, this tool's description is the following:\n{get_tool_description_with_args(available_tools[tool_name])}"
)
elif tool_name in self.managed_agents:
raise AgentExecutionError(
f"Error in calling team member: {e}\nYou should only ask this team... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def log_rationale_code_action(self, rationale: str, code_action: str) -> None:
self.logger.warning("=== Agent thoughts:")
self.logger.log(31, rationale)
self.logger.warning(">>> Agent is executing the code below:")
if is_pygments_available():
self.logger.log(
... | 383 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class CodeAgent(Agent):
"""
A class for an agent that solves the given task using a single block of code. It plans all its actions, then executes all in one shot.
""" | 384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def __init__(
self,
tools: List[Tool],
llm_engine: Optional[Callable] = None,
system_prompt: Optional[str] = None,
tool_description_template: Optional[str] = None,
grammar: Optional[Dict[str, str]] = None,
additional_authorized_imports: Optional[List[str]] = None,... | 384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if not is_pygments_available():
transformers_logging.warning_once(
logger,
"pygments isn't installed. Installing pygments will enable color syntax highlighting in the "
"CodeAgent.",
)
self.python_evaluator = evaluate_python_code
s... | 384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
Args:
task (`str`): The task to perform
return_generated_code (`bool`, *optional*, defaults to `False`): Whether to return the generated code instead of running it
kwargs (additional keyword arguments, *optional*):
Any keyword argument to send to the agent when evalua... | 384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
self.prompt = [prompt_message, task_message]
self.logger.info("====Executing with this prompt====")
self.logger.info(self.prompt)
additional_args = {"grammar": self.grammar} if self.grammar is not None else {}
llm_output = self.llm_engine(self.prompt, stop_sequences=["<end_action>"], **... | 384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
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