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Running
on
Zero
File size: 9,527 Bytes
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import dataclasses
from enum import auto, Enum
from typing import List, Tuple
import base64
from io import BytesIO
from PIL import Image
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
MPT = auto()
PLAIN = auto()
LLAMA_3 = auto()
@dataclasses.dataclass
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)
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.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" if len(msg) > 0 else msg
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.CHATML:
ret = "" if self.system == "" else self.system + self.sep + "\n"
for role, message in messages:
if message:
if type(message) is tuple:
message, images, _ = message
message = "<image>" * len(images) + message
ret += role + "\n" + message + self.sep + "\n"
else:
ret += role + "\n"
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
return ret
def append_message(self, role, message):
if isinstance(self.messages, tuple):
self.messages = list(self.messages)
self.messages.append([role, message])
def process_image(self, image, image_process_mode, return_pil=False, image_format='PNG', max_len=1344, min_len=672):
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}")
if max(image.size) > max_len:
max_hw, min_hw = max(image.size), min(image.size)
aspect_ratio = max_hw / min_hw
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))
if return_pil:
return image
else:
buffered = BytesIO()
image.save(buffered, format=image_format)
img_b64_str = base64.b64encode(buffered.getvalue()).decode()
return img_b64_str
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:
msg, image, image_process_mode = msg
image = self.process_image(image, image_process_mode, return_pil=return_pil)
images.append(image)
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:
msg, image, image_process_mode = msg
img_b64_str = self.process_image(
image, "Default", return_pil=False,
image_format='JPEG')
img_str = f'<img src="data:image/jpeg;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_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_llava_llama_3 = Conversation(
system="""<|start_header_id|>system<|end_header_id|>\n\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""",
roles=("<|start_header_id|>user<|end_header_id|>\n\n", "<|start_header_id|>assistant<|end_header_id|>\n\n"),
version="llama3",
messages=(),
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|eot_id|>",
)
conv_llava_phi_3 = Conversation(
system="""<|system|>\nYou are a helpful AI assistant.""",
roles=("\n<|user|>\n", "\n<|assistant|>\n"),
version="phi3",
messages=(),
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|end|>",
)
default_conversation = conv_llava_phi_3
conv_templates = {
"v1": conv_vicuna_v1,
"vicuna_v1": conv_vicuna_v1,
"llava_phi_3": conv_llava_phi_3,
"llava_llama_3": conv_llava_llama_3,
}
if __name__ == "__main__":
print(default_conversation.get_prompt())
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