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# coding=utf-8 | |
# Copyright 2023 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(): | |
import PIL.Image | |
from PIL import Image | |
from PIL.Image import Image as ImageType | |
else: | |
ImageType = object | |
if is_torch_available(): | |
import torch | |
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 self._value | |
class AgentImage(AgentType, ImageType): | |
""" | |
Image type returned by the agent. Behaves as a PIL.Image. | |
""" | |
def __init__(self, value): | |
super().__init__(value) | |
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 | |
else: | |
raise ValueError(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 | |
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((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 | |
class AgentAudio(AgentType): | |
""" | |
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 isinstance(value, torch.Tensor): | |
self._tensor = value | |
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 = {"text": AgentText, "image": AgentImage, "audio": AgentAudio} | |
INSTANCE_TYPE_MAPPING = {str: AgentText} | |
if is_vision_available(): | |
INSTANCE_TYPE_MAPPING[PIL.Image] = AgentImage | |
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(outputs, output_types=None): | |
if isinstance(outputs, dict): | |
decoded_outputs = {} | |
for i, (k, v) in enumerate(outputs.items()): | |
if output_types is not None: | |
# If the class has defined outputs, we can map directly according to the class definition | |
if output_types[i] in AGENT_TYPE_MAPPING: | |
decoded_outputs[k] = AGENT_TYPE_MAPPING[output_types[i]](v) | |
else: | |
decoded_outputs[k] = AgentType(v) | |
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(v, _k): | |
decoded_outputs[k] = _v(v) | |
if k not in decoded_outputs: | |
decoded_outputs[k] = AgentType[v] | |
elif isinstance(outputs, (list, tuple)): | |
decoded_outputs = type(outputs)() | |
for i, v in enumerate(outputs): | |
if output_types is not None: | |
# If the class has defined outputs, we can map directly according to the class definition | |
if output_types[i] in AGENT_TYPE_MAPPING: | |
decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](v)) | |
else: | |
decoded_outputs.append(AgentType(v)) | |
else: | |
# If the class does not have defined output, then we map according to the type | |
found = False | |
for _k, _v in INSTANCE_TYPE_MAPPING.items(): | |
if isinstance(v, _k): | |
decoded_outputs.append(_v(v)) | |
found = True | |
if not found: | |
decoded_outputs.append(AgentType(v)) | |
else: | |
if output_types[0] 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_types[0]](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(outputs, _k): | |
return _v(outputs) | |
return AgentType(outputs) | |
return decoded_outputs | |