text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
try:
code_action = self.parse_code_blob(code_action)
except Exception as e:
error_msg = f"Error in code parsing: {e}. Be sure to provide correct code"
self.logger.error(error_msg, exc_info=1)
return error_msg
# Execute
self.log_rationale_code_acti... | 384 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class ReactAgent(Agent):
"""
This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The action will be parsed from the LLM output: it consists in calls to tools from the toolbox, with argum... | 385 | /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,
plan_type: Optional[str] = None,
planning_interval... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
tool_description_template=tool_description_template,
grammar=grammar,
**kwargs,
)
self.planning_interval = planning_interval
self.plan_type = plan_type | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def provide_final_answer(self, task) -> str:
"""
This method provides a final answer to the task, based on the logs of the agent's interactions.
"""
self.prompt = [
{
"role": MessageRole.SYSTEM,
"content": "An agent tried to answer an user quer... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def run(self, task: str, stream: bool = False, reset: bool = True, **kwargs):
"""
Runs the agent for the given task.
Args:
task (`str`): The task to perform
Example:
```py
from transformers.agents import ReactCodeAgent
agent = ReactCodeAgent(tools=[]... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def stream_run(self, task: str):
"""
Runs the agent in streaming mode, yielding steps as they are executed: should be launched only in the `run` method.
"""
final_answer = None
iteration = 0
while final_answer is None and iteration < self.max_iterations:
step_... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
for callback in self.step_callbacks:
callback(step_log_entry)
iteration += 1
yield step_log_entry | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if final_answer is None and iteration == self.max_iterations:
error_message = "Reached max iterations."
final_step_log = {"error": AgentMaxIterationsError(error_message)}
self.logs.append(final_step_log)
self.logger.error(error_message, exc_info=1)
final_answe... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def direct_run(self, task: str):
"""
Runs the agent in direct mode, returning outputs only at the end: should be launched only in the `run` method.
"""
final_answer = None
iteration = 0
while final_answer is None and iteration < self.max_iterations:
step_start... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
step_log_entry["step_end_time"] = step_end_time
step_log_entry["step_duration"] = step_end_time - step_start_time
self.logs.append(step_log_entry)
for callback in self.step_callbacks:
callback(step_log_entry)
iteration += 1 | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if final_answer is None and iteration == self.max_iterations:
error_message = "Reached max iterations."
final_step_log = {"error": AgentMaxIterationsError(error_message)}
self.logs.append(final_step_log)
self.logger.error(error_message, exc_info=1)
final_answe... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
Args:
task (`str`): The task to perform
is_first_step (`bool`): If this step is not the first one, the plan should be an update over a previous plan.
iteration (`int`): The number of the current step, used as an indication for the LLM.
"""
if is_first_step:
... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
message_system_prompt_plan = {
"role": MessageRole.SYSTEM,
"content": PROMPTS_FOR_INITIAL_PLAN[self.plan_type]["system"],
}
message_user_prompt_plan = {
"role": MessageRole.USER,
"content": PROMPTS_FOR_INITIAL_PLAN[self.plan_type]["... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
final_plan_redaction = f"""Here is the plan of action that I will follow to solve the task:
```
{answer_plan}
```"""
final_facts_redaction = f"""Here are the facts that I know so far:
```
{answer_facts}
```""".strip()
self.logs.append({"plan": final_plan_redaction, "facts": final_facts_redaction... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
# Redact updated facts
facts_update_system_prompt = {
"role": MessageRole.SYSTEM,
"content": SYSTEM_PROMPT_FACTS_UPDATE,
}
facts_update_message = {
"role": MessageRole.USER,
"content": USER_PROMPT_FACTS_UPDATE,
... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
# Redact updated plan
plan_update_message = {
"role": MessageRole.SYSTEM,
"content": PROMPTS_FOR_PLAN_UPDATE[self.plan_type]["system"].format(task=task),
}
plan_update_message_user = {
"role": MessageRole.USER,
"content"... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
) | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
# 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:
```
{facts_update}
```"""
self.logs.append({"plan": final_plan_redaction, "fact... | 385 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class ReactJsonAgent(ReactAgent):
"""
This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The tool calls will be formulated by the LLM in JSON format, then parsed and executed.
""" | 386 | /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,
planning_interval: Optional[int] = None,
**kwargs,... | 386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def step(self, log_entry: Dict[str, Any]):
"""
Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
The errors are raised here, they are caught and logged in the run() method.
"""
agent_memory = self.write_inner_memory_from_logs()
sel... | 386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
try:
additional_args = {"grammar": self.grammar} if self.grammar is not None else {}
llm_output = self.llm_engine(
self.prompt, stop_sequences=["<end_action>", "Observation:"], **additional_args
)
except Exception as e:
raise AgentGenerationError(f... | 386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
# Execute
self.logger.warning("=== Agent thoughts:")
self.logger.log(31, rationale)
self.logger.warning(f">>> Calling tool: '{tool_name}' with arguments: {arguments}")
if tool_name == "final_answer":
if isinstance(arguments, dict):
if "answer" in arguments:
... | 386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
if observation_type in [AgentImage, AgentAudio]:
if observation_type == AgentImage:
observation_name = "image.png"
elif observation_type == AgentAudio:
observation_name = "audio.mp3"
# TODO: observation naming could allow for differ... | 386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
self.state[observation_name] = observation
updated_information = f"Stored '{observation_name}' in memory."
else:
updated_information = str(observation).strip()
self.logger.info(updated_information)
log_entry["observation"] = updated_information
... | 386 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class ReactCodeAgent(ReactAgent):
"""
This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The tool calls will be formulated by the LLM in code format, then parsed and executed.
""" | 387 | /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,... | 387 | /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 "
"ReactCodeAgent.",
)
self.python_evaluator = evaluate_python_code
... | 387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
self.prompt = agent_memory.copy()
self.logger.debug("===== New step =====")
# Add new step in logs
log_entry["agent_memory"] = agent_memory.copy()
self.logger.info("===== Calling LLM with these last messages: =====")
self.logger.info(self.prompt[-2:])
try:
... | 387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
# Parse
self.logger.debug("=== Extracting action ===")
try:
rationale, raw_code_action = self.extract_action(llm_output=llm_output, split_token="Code:")
except Exception as e:
self.logger.debug(f"Error in extracting action, trying to parse the whole output. Error trace: {... | 387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
# Execute
self.log_rationale_code_action(rationale, code_action)
try:
static_tools = {
**BASE_PYTHON_TOOLS.copy(),
**self.toolbox.tools,
}
if self.managed_agents is not None:
static_tools = {**static_tools, **self.manage... | 387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
observation += "Last output from code snippet:\n" + str(result)[:100000]
log_entry["observation"] = observation
except Exception as e:
error_msg = f"Code execution failed due to the following error:\n{str(e)}"
if "'dict' object has no attribute 'read'" in str(e):
... | 387 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class ManagedAgent:
def __init__(self, agent, name, description, additional_prompting=None, provide_run_summary=False):
self.agent = agent
self.name = name
self.description = description
self.additional_prompting = additional_prompting
self.provide_run_summary = provide_run_s... | 388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.
And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
<<additional_prompting>>"""
if self.additional_pro... | 388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
def __call__(self, request, **kwargs):
full_task = self.write_full_task(request)
output = self.agent.run(full_task, **kwargs)
if self.provide_run_summary:
answer = f"Here is the final answer from your managed agent '{self.name}':\n"
answer += str(output)
answe... | 388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
answer += f"\nEND OF SUMMARY OF WORK FROM AGENT '{self.name}'."
return answer
else:
return output | 388 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py |
class TextToSpeechTool(PipelineTool):
default_checkpoint = "microsoft/speecht5_tts"
description = (
"This is a tool that reads an English text out loud. It returns a waveform object containing the sound."
)
name = "text_to_speech"
pre_processor_class = SpeechT5Processor
model_class = Spe... | 389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/text_to_speech.py |
embeddings_dataset = load_dataset(
"Matthijs/cmu-arctic-xvectors", split="validation", trust_remote_code=True
)
speaker_embeddings = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_emb... | 389 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/text_to_speech.py |
class DocumentQuestionAnsweringTool(PipelineTool):
default_checkpoint = "naver-clova-ix/donut-base-finetuned-docvqa"
description = "This is a tool that answers a question about an document (pdf). It returns a string that contains the answer to the question."
name = "document_qa"
pre_processor_class = Au... | 390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/document_question_answering.py |
def encode(self, document: "Image", question: str):
task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
prompt = task_prompt.replace("{user_input}", question)
decoder_input_ids = self.pre_processor.tokenizer(
prompt, add_special_tokens=False, return_tensors="pt"... | 390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/document_question_answering.py |
def forward(self, inputs):
return self.model.generate(
inputs["pixel_values"].to(self.device),
decoder_input_ids=inputs["decoder_input_ids"].to(self.device),
max_length=self.model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_i... | 390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/document_question_answering.py |
def decode(self, outputs):
sequence = self.pre_processor.batch_decode(outputs)[0]
sequence = sequence.replace(self.pre_processor.tokenizer.eos_token, "")
sequence = sequence.replace(self.pre_processor.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # ... | 390 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/document_question_answering.py |
class TranslationTool(PipelineTool):
"""
Example:
```py
from transformers.agents import TranslationTool
translator = TranslationTool()
translator("This is a super nice API!", src_lang="English", tgt_lang="French")
```
"""
lang_to_code = LANGUAGE_CODES
default_checkpoint = "fac... | 391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/translation.py |
inputs = {
"text": {"type": "string", "description": "The text to translate"},
"src_lang": {
"type": "string",
"description": "The language of the text to translate. Written in plain English, such as 'Romanian', or 'Albanian'",
},
"tgt_lang": {
"type":... | 391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/translation.py |
def encode(self, text, src_lang, tgt_lang):
if src_lang not in self.lang_to_code:
raise ValueError(f"{src_lang} is not a supported language.")
if tgt_lang not in self.lang_to_code:
raise ValueError(f"{tgt_lang} is not a supported language.")
src_lang = self.lang_to_code[s... | 391 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/translation.py |
class PreTool:
name: str
inputs: Dict[str, str]
output_type: type
task: str
description: str
repo_id: str | 392 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py |
class PythonInterpreterTool(Tool):
name = "python_interpreter"
description = "This is a tool that evaluates python code. It can be used to perform calculations."
output_type = "string"
def __init__(self, *args, authorized_imports=None, **kwargs):
if authorized_imports is None:
self... | 393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py |
def forward(self, code):
output = str(
evaluate_python_code(code, static_tools=BASE_PYTHON_TOOLS, authorized_imports=self.authorized_imports)
)
return output | 393 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py |
class FinalAnswerTool(Tool):
name = "final_answer"
description = "Provides a final answer to the given problem."
inputs = {"answer": {"type": "any", "description": "The final answer to the problem"}}
output_type = "any"
def forward(self, answer):
return answer | 394 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py |
class DuckDuckGoSearchTool(Tool):
name = "web_search"
description = """Perform a web search based on your query (think a Google search) then returns the top search results as a list of dict elements.
Each result has keys 'title', 'href' and 'body'."""
inputs = {"query": {"type": "string", "description":... | 395 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/search.py |
class VisitWebpageTool(Tool):
name = "visit_webpage"
description = "Visits a webpage at the given url and returns its content as a markdown string."
inputs = {
"url": {
"type": "string",
"description": "The url of the webpage to visit.",
}
}
output_type = "str... | 396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/search.py |
# Remove multiple line breaks
markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
return markdown_content
except RequestException as e:
return f"Error fetching the webpage: {str(e)}"
except Exception as e:
return f"An unexpected error occurred... | 396 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/search.py |
class InterpreterError(ValueError):
"""
An error raised when the interpretor cannot evaluate a Python expression, due to syntax error or unsupported
operations.
"""
pass | 397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py |
class BreakException(Exception):
pass | 398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py |
class ContinueException(Exception):
pass | 399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py |
class ReturnException(Exception):
def __init__(self, value):
self.value = value | 400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py |
class AgentType:
"""
Abstract class to be reimplemented to define types that can be returned by agents.
These objects serve three purposes:
- They behave as they were the type they're meant to be, e.g., a string for text, a PIL.Image for images
- They can be stringified: str(object) in order to re... | 401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
class AgentText(AgentType, str):
"""
Text type returned by the agent. Behaves as a string.
"""
def to_raw(self):
return self._value
def to_string(self):
return str(self._value) | 402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
class AgentImage(AgentType, ImageType):
"""
Image type returned by the agent. Behaves as a PIL.Image.
"""
def __init__(self, value):
AgentType.__init__(self, value)
ImageType.__init__(self)
if not is_vision_available():
raise ImportError("PIL must be installed in or... | 403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
def _ipython_display_(self, include=None, exclude=None):
"""
Displays correctly this type in an ipython notebook (ipython, colab, jupyter, ...)
"""
from IPython.display import Image, display
display(Image(self.to_string()))
def to_raw(self):
"""
Returns the ... | 403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
if self._raw is not None:
directory = tempfile.mkdtemp()
self._path = os.path.join(directory, str(uuid.uuid4()) + ".png")
self._raw.save(self._path)
return self._path
if self._tensor is not None:
array = self._tensor.cpu().detach().numpy()
... | 403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
def save(self, output_bytes, format, **params):
"""
Saves the image to a file.
Args:
output_bytes (bytes): The output bytes to save the image to.
format (str): The format to use for the output image. The format is the same as in PIL.Image.save.
**params: Addit... | 403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
class AgentAudio(AgentType, str):
"""
Audio type returned by the agent.
"""
def __init__(self, value, samplerate=16_000):
super().__init__(value)
if not is_soundfile_available():
raise ImportError("soundfile must be installed in order to handle audio.")
self._path ... | 404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
def _ipython_display_(self, include=None, exclude=None):
"""
Displays correctly this type in an ipython notebook (ipython, colab, jupyter, ...)
"""
from IPython.display import Audio, display
display(Audio(self.to_string(), rate=self.samplerate))
def to_raw(self):
""... | 404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
if self._tensor is not None:
directory = tempfile.mkdtemp()
self._path = os.path.join(directory, str(uuid.uuid4()) + ".wav")
sf.write(self._path, self._tensor, samplerate=self.samplerate)
return self._path | 404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agent_types.py |
class PipelineDataset(Dataset):
def __init__(self, dataset, process, params):
self.dataset = dataset
self.process = process
self.params = params
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
item = self.dataset[i]
processed = self.pro... | 405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
class PipelineIterator(IterableDataset):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for item in loader:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
self.loader = loader
self.infer = infer
self.params = params
if loader_batch_size == 1:
# Let's spare some time ... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
def loader_batch_item(self):
"""
Return item located at `loader_batch_index` within the current `loader_batch_data`.
"""
if isinstance(self._loader_batch_data, torch.Tensor):
# Batch data is simple tensor, just fetch the slice
result = self._loader_batch_data[self... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(element, tuple):
# Those are stored as lists of tensors so need specific unbatching.
... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
loader_batched[k] = element[self._loader_batch_index].unsqueeze(0)
elif isinstance(element[self._loader_batch_index], np.ndarray):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
def __next__(self):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items wi... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# ele... | 406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
class PipelineChunkIterator(PipelineIterator):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for iterator in loader:
for item in iterator:
yield infer(item, **params)
```
Argument... | 407 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
def __next__(self):
if self.subiterator is None:
"Subiterator None means we haven't started a `preprocess` iterator. so start it"
self.subiterator = self.infer(next(self.iterator), **self.params)
try:
# Try to return next item
processed = next(self.subiter... | 407 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
class PipelinePackIterator(PipelineIterator):
"""
Roughly equivalent to
```
packed = []
for item in loader:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
but it also handles cases where `item` are batched (meaning it's a d... | 408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
Arguments:
loader (`torch.utils.data.DataLoader` or `Iterable`):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `inf... | 408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
def __next__(self):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the origi... | 408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
while not is_last:
processed = self.infer(next(self.iterator), **self.params)
if self.loader_batch_size is not None:
if isinstance(processed, torch.Tensor):
first_tensor = processed
else:
key = list(processed.keys())[0]
... | 408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
else:
item = processed
is_last = item.pop("is_last")
accum... | 408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
class KeyDataset(Dataset):
def __init__(self, dataset: Dataset, key: str):
self.dataset = dataset
self.key = key
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return self.dataset[i][self.key] | 409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
class KeyPairDataset(Dataset):
def __init__(self, dataset: Dataset, key1: str, key2: str):
self.dataset = dataset
self.key1 = key1
self.key2 = key2
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return {"text": self.dataset[i][self.key1], "tex... | 410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/pt_utils.py |
class TextToAudioPipeline(Pipeline):
"""
Text-to-audio generation pipeline using any `AutoModelForTextToWaveform` or `AutoModelForTextToSpectrogram`. This
pipeline generates an audio file from an input text and optional other conditional inputs.
Example:
```python
>>> from transformers import ... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
>>> # diversify the music generation by adding randomness with a high temperature and set a maximum music length
>>> generate_kwargs = {
... "do_sample": True,
... "temperature": 0.7,
... "max_new_tokens": 35,
... }
>>> outputs = music_generator("Techno music with high melodic riffs... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
self.vocoder = None
if self.model.__class__ in MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING.values():
self.vocoder = (
SpeechT5HifiGan.from_pretrained(DEFAULT_VOCODER_ID).to(self.model.device)
if vocoder is None
else vocoder
)
self.sampli... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
def preprocess(self, text, **kwargs):
if isinstance(text, str):
text = [text]
if self.model.config.model_type == "bark":
# bark Tokenizer is called with BarkProcessor which uses those kwargs
new_kwargs = {
"max_length": self.generation_config.semantic... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
def _forward(self, model_inputs, **kwargs):
# we expect some kwargs to be additional tensors which need to be on the right device
kwargs = self._ensure_tensor_on_device(kwargs, device=self.device)
forward_params = kwargs["forward_params"]
generate_kwargs = kwargs["generate_kwargs"]
... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
output = self.model.generate(**model_inputs, **forward_params)
else:
if len(generate_kwargs):
raise ValueError(
"You're using the `TextToAudioPipeline` with a forward-only model, but `generate_kwargs` is non "
"empty. For forward-only TTA model... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
Args:
text_inputs (`str` or `List[str]`):
The text(s) to generate.
forward_params (`dict`, *optional*):
Parameters passed to the model generation/forward method. `forward_params` are always passed to the
underlying model.
generate_kwarg... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
- **audio** (`np.ndarray` of shape `(nb_channels, audio_length)`) -- The generated audio waveform.
- **sampling_rate** (`int`) -- The sampling rate of the generated audio waveform.
"""
return super().__call__(text_inputs, **forward_params)
def _sanitize_parameters(
self,
... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
return preprocess_params, params, postprocess_params
def postprocess(self, waveform):
output_dict = {}
if isinstance(waveform, dict):
waveform = waveform["waveform"]
elif isinstance(waveform, tuple):
waveform = waveform[0]
output_dict["audio"] = waveform.cpu(... | 411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/text_to_audio.py |
class QuestionAnsweringArgumentHandler(ArgumentHandler):
"""
QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question & context) to be mapped to
internal [`SquadExample`].
QuestionAnsweringArgumentHandler manages all the possible to create a [`SquadExample`] from the com... | 412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
return QuestionAnsweringPipeline.create_sample(**item)
raise ValueError(f"{item} argument needs to be of type (SquadExample, dict)") | 412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
def __call__(self, *args, **kwargs):
# Detect where the actual inputs are
if args is not None and len(args) > 0:
if len(args) == 1:
inputs = args[0]
elif len(args) == 2 and {type(el) for el in args} == {str}:
inputs = [{"question": args[0], "contex... | 412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
"Passing the `data` argument to the pipeline is deprecated and will be removed in v5. Inputs should be passed using the `question` and `context` keyword arguments instead.",
FutureWarning,
)
inputs = kwargs["data"]
elif "question" in kwargs and "context" in kwargs:
... | 412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
inputs = [{"question": Q, "context": C} for Q, C in zip(kwargs["question"], kwargs["context"])]
elif isinstance(kwargs["question"], str) and isinstance(kwargs["context"], str):
inputs = [{"question": kwargs["question"], "context": kwargs["context"]}]
else:
raise V... | 412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
for i, item in enumerate(inputs):
inputs[i] = self.normalize(item)
return inputs | 412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
class QuestionAnsweringPipeline(ChunkPipeline):
"""
Question Answering pipeline using any `ModelForQuestionAnswering`. See the [question answering
examples](../task_summary#question-answering) for more information.
Example:
```python
>>> from transformers import pipeline
>>> oracle = pipe... | 413 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pipelines/question_answering.py |
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