261 lines
8.2 KiB
Python
261 lines
8.2 KiB
Python
# 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 ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging
|
|
|
|
|
|
logger = logging.get_logger(__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)
|
|
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)
|
|
|
|
return self._path
|
|
|
|
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: Additional parameters to pass to PIL.Image.save.
|
|
"""
|
|
img = self.to_raw()
|
|
img.save(output_bytes, 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
|