Delete types.py

types.py

@@ -1,270 +0,0 @@
-# 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"]