Spaces:
Sleeping
Sleeping
import dataclasses | |
from enum import auto, Enum | |
from typing import List, Tuple | |
from mplug_owl2.constants import DEFAULT_IMAGE_TOKEN | |
class SeparatorStyle(Enum): | |
"""Different separator style.""" | |
SINGLE = auto() | |
TWO = auto() | |
TWO_NO_SYS = auto() | |
MPT = auto() | |
PLAIN = auto() | |
LLAMA_2 = auto() | |
class Conversation: | |
"""A class that keeps all conversation history.""" | |
system: str | |
roles: List[str] | |
messages: List[List[str]] | |
offset: int | |
sep_style: SeparatorStyle = SeparatorStyle.SINGLE | |
sep: str = "###" | |
sep2: str = None | |
version: str = "Unknown" | |
skip_next: bool = False | |
def get_prompt(self): | |
messages = self.messages | |
if len(messages) > 0 and type(messages[0][1]) is tuple: | |
messages = self.messages.copy() | |
init_role, init_msg = messages[0].copy() | |
# init_msg = init_msg[0].replace("<image>", "").strip() | |
# if 'mmtag' in self.version: | |
# messages[0] = (init_role, init_msg) | |
# messages.insert(0, (self.roles[0], "<Image><image></Image>")) | |
# messages.insert(1, (self.roles[1], "Received.")) | |
# else: | |
# messages[0] = (init_role, "<image>\n" + init_msg) | |
init_msg = init_msg[0].replace(DEFAULT_IMAGE_TOKEN, "").strip() | |
messages[0] = (init_role, DEFAULT_IMAGE_TOKEN + init_msg) | |
if self.sep_style == SeparatorStyle.SINGLE: | |
ret = self.system + self.sep | |
for role, message in messages: | |
if message: | |
if type(message) is tuple: | |
message, _, _ = message | |
ret += role + ": " + message + self.sep | |
else: | |
ret += role + ":" | |
elif self.sep_style == SeparatorStyle.TWO: | |
seps = [self.sep, self.sep2] | |
ret = self.system + seps[0] | |
for i, (role, message) in enumerate(messages): | |
if message: | |
if type(message) is tuple: | |
message, _, _ = message | |
ret += role + ": " + message + seps[i % 2] | |
else: | |
ret += role + ":" | |
elif self.sep_style == SeparatorStyle.TWO_NO_SYS: | |
seps = [self.sep, self.sep2] | |
ret = "" | |
for i, (role, message) in enumerate(messages): | |
if message: | |
if type(message) is tuple: | |
message, _, _ = message | |
ret += role + ": " + message + seps[i % 2] | |
else: | |
ret += role + ":" | |
elif self.sep_style == SeparatorStyle.MPT: | |
ret = self.system + self.sep | |
for role, message in messages: | |
if message: | |
if type(message) is tuple: | |
message, _, _ = message | |
ret += role + message + self.sep | |
else: | |
ret += role | |
elif self.sep_style == SeparatorStyle.LLAMA_2: | |
wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" | |
wrap_inst = lambda msg: f"[INST] {msg} [/INST]" | |
ret = "" | |
for i, (role, message) in enumerate(messages): | |
if i == 0: | |
assert message, "first message should not be none" | |
assert role == self.roles[0], "first message should come from user" | |
if message: | |
if type(message) is tuple: | |
message, _, _ = message | |
if i == 0: message = wrap_sys(self.system) + message | |
if i % 2 == 0: | |
message = wrap_inst(message) | |
ret += self.sep + message | |
else: | |
ret += " " + message + " " + self.sep2 | |
else: | |
ret += "" | |
ret = ret.lstrip(self.sep) | |
elif self.sep_style == SeparatorStyle.PLAIN: | |
seps = [self.sep, self.sep2] | |
ret = self.system | |
for i, (role, message) in enumerate(messages): | |
if message: | |
if type(message) is tuple: | |
message, _, _ = message | |
ret += message + seps[i % 2] | |
else: | |
ret += "" | |
else: | |
raise ValueError(f"Invalid style: {self.sep_style}") | |
return ret | |
def append_message(self, role, message): | |
self.messages.append([role, message]) | |
def get_images(self, return_pil=False): | |
images = [] | |
for i, (role, msg) in enumerate(self.messages[self.offset:]): | |
if i % 2 == 0: | |
if type(msg) is tuple: | |
import base64 | |
from io import BytesIO | |
from PIL import Image | |
msg, image, image_process_mode = msg | |
if image_process_mode == "Pad": | |
def expand2square(pil_img, background_color=(122, 116, 104)): | |
width, height = pil_img.size | |
if width == height: | |
return pil_img | |
elif width > height: | |
result = Image.new(pil_img.mode, (width, width), background_color) | |
result.paste(pil_img, (0, (width - height) // 2)) | |
return result | |
else: | |
result = Image.new(pil_img.mode, (height, height), background_color) | |
result.paste(pil_img, ((height - width) // 2, 0)) | |
return result | |
image = expand2square(image) | |
elif image_process_mode in ["Default", "Crop"]: | |
pass | |
elif image_process_mode == "Resize": | |
image = image.resize((336, 336)) | |
else: | |
raise ValueError(f"Invalid image_process_mode: {image_process_mode}") | |
max_hw, min_hw = max(image.size), min(image.size) | |
aspect_ratio = max_hw / min_hw | |
max_len, min_len = 800, 400 | |
shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) | |
longest_edge = int(shortest_edge * aspect_ratio) | |
W, H = image.size | |
if longest_edge != max(image.size): | |
if H > W: | |
H, W = longest_edge, shortest_edge | |
else: | |
H, W = shortest_edge, longest_edge | |
image = image.resize((W, H)) | |
if return_pil: | |
images.append(image) | |
else: | |
buffered = BytesIO() | |
image.save(buffered, format="PNG") | |
img_b64_str = base64.b64encode(buffered.getvalue()).decode() | |
images.append(img_b64_str) | |
return images | |
def to_gradio_chatbot(self): | |
ret = [] | |
for i, (role, msg) in enumerate(self.messages[self.offset:]): | |
if i % 2 == 0: | |
if type(msg) is tuple: | |
import base64 | |
from io import BytesIO | |
msg, image, image_process_mode = msg | |
max_hw, min_hw = max(image.size), min(image.size) | |
aspect_ratio = max_hw / min_hw | |
max_len, min_len = 800, 400 | |
shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) | |
longest_edge = int(shortest_edge * aspect_ratio) | |
W, H = image.size | |
if H > W: | |
H, W = longest_edge, shortest_edge | |
else: | |
H, W = shortest_edge, longest_edge | |
image = image.resize((W, H)) | |
buffered = BytesIO() | |
image.save(buffered, format="JPEG") | |
img_b64_str = base64.b64encode(buffered.getvalue()).decode() | |
img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />' | |
msg = img_str + msg.replace('<|image|>', '').strip() | |
ret.append([msg, None]) | |
else: | |
ret.append([msg, None]) | |
else: | |
ret[-1][-1] = msg | |
return ret | |
def copy(self): | |
return Conversation( | |
system=self.system, | |
roles=self.roles, | |
messages=[[x, y] for x, y in self.messages], | |
offset=self.offset, | |
sep_style=self.sep_style, | |
sep=self.sep, | |
sep2=self.sep2, | |
version=self.version) | |
def dict(self): | |
if len(self.get_images()) > 0: | |
return { | |
"system": self.system, | |
"roles": self.roles, | |
"messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages], | |
"offset": self.offset, | |
"sep": self.sep, | |
"sep2": self.sep2, | |
} | |
return { | |
"system": self.system, | |
"roles": self.roles, | |
"messages": self.messages, | |
"offset": self.offset, | |
"sep": self.sep, | |
"sep2": self.sep2, | |
} | |
conv_vicuna_v0 = Conversation( | |
system="A chat between a curious human and an artificial intelligence assistant. " | |
"The assistant gives helpful, detailed, and polite answers to the human's questions.", | |
roles=("Human", "Assistant"), | |
messages=( | |
("Human", "What are the key differences between renewable and non-renewable energy sources?"), | |
("Assistant", | |
"Renewable energy sources are those that can be replenished naturally in a relatively " | |
"short amount of time, such as solar, wind, hydro, geothermal, and biomass. " | |
"Non-renewable energy sources, on the other hand, are finite and will eventually be " | |
"depleted, such as coal, oil, and natural gas. Here are some key differences between " | |
"renewable and non-renewable energy sources:\n" | |
"1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " | |
"energy sources are finite and will eventually run out.\n" | |
"2. Environmental impact: Renewable energy sources have a much lower environmental impact " | |
"than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " | |
"and other negative effects.\n" | |
"3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " | |
"have lower operational costs than non-renewable sources.\n" | |
"4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " | |
"locations than non-renewable sources.\n" | |
"5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " | |
"situations and needs, while non-renewable sources are more rigid and inflexible.\n" | |
"6. Sustainability: Renewable energy sources are more sustainable over the long term, while " | |
"non-renewable sources are not, and their depletion can lead to economic and social instability.\n") | |
), | |
offset=2, | |
sep_style=SeparatorStyle.SINGLE, | |
sep="###", | |
) | |
conv_vicuna_v1 = Conversation( | |
system="A chat between a curious user and an artificial intelligence assistant. " | |
"The assistant gives helpful, detailed, and polite answers to the user's questions.", | |
roles=("USER", "ASSISTANT"), | |
version="v1", | |
messages=(), | |
offset=0, | |
sep_style=SeparatorStyle.TWO, | |
sep=" ", | |
sep2="</s>", | |
) | |
conv_mplug_owl2 = Conversation( | |
system="A chat between a curious human and an artificial intelligence assistant. " | |
"The assistant gives helpful, detailed, and polite answers to the human's questions.", | |
roles=("USER", "ASSISTANT"), | |
version="v1", | |
messages=(), | |
offset=0, | |
sep_style=SeparatorStyle.TWO_NO_SYS, | |
sep=" ", | |
sep2="</s>", | |
) | |
# default_conversation = conv_vicuna_v1 | |
default_conversation = conv_mplug_owl2 | |
conv_templates = { | |
"default": conv_vicuna_v0, | |
"v0": conv_vicuna_v0, | |
"v1": conv_vicuna_v1, | |
"vicuna_v1": conv_vicuna_v1, | |
"mplug_owl2": conv_mplug_owl2, | |
} | |
if __name__ == "__main__": | |
print(default_conversation.get_prompt()) |