# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pathlib import tempfile import uuid import numpy as np from transformers.utils import ( is_soundfile_availble, is_torch_available, is_vision_available, ) import logging logger = logging.getLogger(__name__) if is_vision_available(): from PIL import Image from PIL.Image import Image as ImageType else: ImageType = object if is_torch_available(): import torch from torch import Tensor else: Tensor = object if is_soundfile_availble(): import soundfile as sf 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 return a string defining the object - They should be displayed correctly in ipython notebooks/colab/jupyter """ def __init__(self, value): self._value = value def __str__(self): return self.to_string() def to_raw(self): logger.error( "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable" ) return self._value def to_string(self) -> str: logger.error( "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable" ) return str(self._value) 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) 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 order to handle images.") self._path = None self._raw = None self._tensor = None if isinstance(value, ImageType): self._raw = value elif isinstance(value, (str, pathlib.Path)): self._path = value elif isinstance(value, torch.Tensor): self._tensor = value elif isinstance(value, np.ndarray): self._tensor = torch.from_numpy(value) else: raise TypeError( f"Unsupported type for {self.__class__.__name__}: {type(value)}" ) 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 "raw" version of that object. In the case of an AgentImage, it is a PIL.Image. """ if self._raw is not None: return self._raw if self._path is not None: self._raw = Image.open(self._path) return self._raw if self._tensor is not None: array = self._tensor.cpu().detach().numpy() return Image.fromarray((255 - array * 255).astype(np.uint8)) def to_string(self): """ Returns the stringified version of that object. In the case of an AgentImage, it is a path to the serialized version of the image. """ if self._path is not None: return self._path if self._raw is not None: directory = tempfile.mkdtemp() self._path = os.path.join(directory, str(uuid.uuid4()) + ".png") self._raw.save(self._path, format="png") return self._path if self._tensor is not None: array = self._tensor.cpu().detach().numpy() # There is likely simpler than load into image into save img = Image.fromarray((255 - array * 255).astype(np.uint8)) directory = tempfile.mkdtemp() self._path = os.path.join(directory, str(uuid.uuid4()) + ".png") img.save(self._path, format="png") return self._path def save(self, output_bytes, format: str = None, **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: Additional parameters to pass to PIL.Image.save. """ img = self.to_raw() img.save(output_bytes, format=format, **params) class AgentAudio(AgentType, str): """ Audio type returned by the agent. """ def __init__(self, value, samplerate=16_000): super().__init__(value) if not is_soundfile_availble(): raise ImportError("soundfile must be installed in order to handle audio.") self._path = None self._tensor = None self.samplerate = samplerate if isinstance(value, (str, pathlib.Path)): self._path = value elif is_torch_available() and isinstance(value, torch.Tensor): self._tensor = value elif isinstance(value, tuple): self.samplerate = value[0] if isinstance(value[1], np.ndarray): self._tensor = torch.from_numpy(value[1]) else: self._tensor = torch.tensor(value[1]) else: raise ValueError(f"Unsupported audio type: {type(value)}") 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): """ Returns the "raw" version of that object. It is a `torch.Tensor` object. """ if self._tensor is not None: return self._tensor if self._path is not None: tensor, self.samplerate = sf.read(self._path) self._tensor = torch.tensor(tensor) return self._tensor def to_string(self): """ Returns the stringified version of that object. In the case of an AgentAudio, it is a path to the serialized version of the audio. """ if self._path is not None: return self._path 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 AGENT_TYPE_MAPPING = {"string": AgentText, "image": AgentImage, "audio": AgentAudio} INSTANCE_TYPE_MAPPING = {str: AgentText, ImageType: AgentImage} if is_torch_available(): INSTANCE_TYPE_MAPPING[Tensor] = AgentAudio def handle_agent_inputs(*args, **kwargs): args = [(arg.to_raw() if isinstance(arg, AgentType) else arg) for arg in args] kwargs = { k: (v.to_raw() if isinstance(v, AgentType) else v) for k, v in kwargs.items() } return args, kwargs def handle_agent_outputs(output, output_type=None): if output_type in AGENT_TYPE_MAPPING: # If the class has defined outputs, we can map directly according to the class definition decoded_outputs = AGENT_TYPE_MAPPING[output_type](output) return decoded_outputs else: # If the class does not have defined output, then we map according to the type for _k, _v in INSTANCE_TYPE_MAPPING.items(): if isinstance(output, _k): return _v(output) return output __all__ = ["AgentType", "AgentImage", "AgentText", "AgentAudio"]