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import PIL
import torch
from .modelling_gecko import GeckoForConditionalGeneration
from .processing_gecko import GeckoProcessor
from .conversation import conv_llama_3 as default_conv, conv_templates
import transformers
from typing import List, Tuple, Union
from io import StringIO
import sys
class Capturing(list):
def __enter__(self):
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
return self
def __exit__(self, *args):
self.extend(self._stringio.getvalue().splitlines())
del self._stringio # free up some memory
sys.stdout = self._stdout
def chat_gecko(
text:str,
images: List[Union[PIL.Image.Image, str]],
model:GeckoForConditionalGeneration,
processor:GeckoProcessor,
max_input_length:int=None,
history:List[dict]=None,
**kwargs) -> Tuple[str, List[dict]]:
if "llama-3" in model.language_model.name_or_path.lower():
conv = conv_templates['llama_3']
terminators = [
processor.tokenizer.eos_token_id,
processor.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
else:
conv = default_conv
terminators = None
kwargs["eos_token_id"] = terminators
conv = conv.copy()
conv.messages = []
if history is not None:
for message in history:
assert message["role"] in conv.roles
conv.append_message(message["role"], message["text"])
if text:
assert conv.messages[-1][0] == conv.roles[1], "The last message in the history should be the assistant, if the given text is not empty"
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], "")
history.append({"role": conv.roles[0], "text": text})
history.append({"role": conv.roles[1], "text": ""})
else:
if conv.messages[-1][0] == conv.roles[1]:
assert conv.messages[-1][1] == "", "No user message should be provided"
else:
assert conv.messages[-1][0] == conv.roles[0], "The last message in the history should be the user, if the given text is empty"
conv.append_message(conv.roles[0], "")
history.append({"role": conv.roles[0], "text": ""})
else:
history = []
history.append({"role": conv.roles[0], "text": text})
history.append({"role": conv.roles[1], "text": ""})
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], "")
assert conv.messages[-1][0] == conv.roles[1] and conv.messages[-1][1] == "", "Format check"
assert history[-1]["role"] == conv.roles[1] and history[-1]["text"] == "", "Format check"
keyword_prompt = conv.generate_keyword_prompt(text.split("\n")[len(images)])
prompt = conv.get_prompt()
if images:
for i in range(len(images)):
if isinstance(images[i], str):
images[i] = PIL.Image.open(images[i]).convert("RGB")
inputs = processor(images=images, text=prompt, keywords_text=keyword_prompt, return_tensors="pt", truncation=True, max_length=max_input_length)
for k, v in inputs.items():
if v is not None:
if isinstance(v, torch.Tensor):
inputs[k] = v.to(model.device)
elif isinstance(v, list):
if k == 'coords':
continue
inputs[k] = [x.to(model.device) for x in v]
elif isinstance(v, transformers.tokenization_utils_base.BatchEncoding) or isinstance(v, dict):
for key, value in v.items():
if value is not None:
if isinstance(value, list):
inputs[k][key] = [x.to(model.device) for x in value]
else:
inputs[k][key] = value.to(model.device)
else:
raise ValueError(f"Invalid input type: {type(v)}")
with torch.inference_mode():
output_ids = model.generate(**inputs, **kwargs)[0]
# remove the input tokens
generated_ids = output_ids[inputs["input_ids"].shape[-1]:]
generated_text = processor.decode(generated_ids, skip_special_tokens=True)
history[-1]["text"] = generated_text
return generated_text, history
def chat_gecko_stream(
text:str,
images: List[Union[PIL.Image.Image, str]],
model:GeckoForConditionalGeneration,
processor:GeckoProcessor,
max_input_length:int=None,
history:List[dict]=None,
**kwargs) -> Tuple[str, List[dict]]:
if "llama-3" in model.language_model.name_or_path.lower():
conv = conv_templates['llama_3']
terminators = [
processor.tokenizer.eos_token_id,
processor.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
else:
conv = default_conv
terminators = None
kwargs["eos_token_id"] = terminators
conv = conv.copy()
conv.messages = []
if history is not None:
for message in history:
assert message["role"] in conv.roles
conv.append_message(message["role"], message["text"])
if text:
assert conv.messages[-1][0] == conv.roles[1], "The last message in the history should be the assistant, if the given text is not empty"
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], "")
history.append({"role": conv.roles[0], "text": text})
history.append({"role": conv.roles[1], "text": ""})
else:
if conv.messages[-1][0] == conv.roles[1]:
assert conv.messages[-1][1] == "", "No user message should be provided"
else:
assert conv.messages[-1][0] == conv.roles[0], "The last message in the history should be the user, if the given text is empty"
conv.append_message(conv.roles[0], "")
history.append({"role": conv.roles[0], "text": ""})
else:
history = []
history.append({"role": conv.roles[0], "text": text})
history.append({"role": conv.roles[1], "text": ""})
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], "")
assert conv.messages[-1][0] == conv.roles[1] and conv.messages[-1][1] == "", "Format check"
assert history[-1]["role"] == conv.roles[1] and history[-1]["text"] == "", "Format check"
if images:
for i in range(len(images)):
if isinstance(images[i], str):
images[i] = PIL.Image.open(images[i])
last_prompt = history[-2]['text'].split("?")[0]
last_prompt = last_prompt.replace('<image>', '').strip() if '<image>' in last_prompt else last_prompt.strip()
keyword_prompt = conv.generate_keyword_prompt(last_prompt.replace('<image>', '').strip()) if '<image>' in last_prompt else conv.generate_keyword_prompt(last_prompt.strip())
else:
keyword_prompt = None
prompt = conv.get_prompt()
inputs = processor(images=images, text=prompt, keywords_text=keyword_prompt, return_tensors="pt", truncation=True, max_length=max_input_length)
for k, v in inputs.items():
if v is not None:
if isinstance(v, torch.Tensor):
inputs[k] = v.to(model.device)
elif isinstance(v, list):
if k == 'coords':
continue
inputs[k] = [x.to(model.device) for x in v]
elif isinstance(v, transformers.tokenization_utils_base.BatchEncoding) or isinstance(v, dict):
for key, value in v.items():
if value is not None:
if isinstance(value, list):
inputs[k][key] = [x.to(model.device) for x in value]
else:
inputs[k][key] = value.to(model.device)
else:
raise ValueError(f"Invalid input type: {type(v)}")
from transformers import TextIteratorStreamer
from threading import Thread
streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
kwargs["streamer"] = streamer
inputs.update(kwargs)
thread = Thread(target=model.generate, kwargs=inputs)
thread.start()
generator = []
with Capturing() as print_kw:
for _output in streamer:
history[-1]["text"] += _output
generator.append((history[-1]["text"], history))
# yield history[-1]["text"], history
return generator, print_kw, inputs
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