/
utils_prompt.py
207 lines (174 loc) · 7.78 KB
/
utils_prompt.py
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from dataclasses import dataclass
from typing import List, Optional
def get_question_text(problem):
question = problem['question']
return question
def get_context_text(problem, use_caption):
txt_context = problem['hint']
img_context = problem['caption'] if use_caption else ""
context = " ".join([txt_context, img_context]).strip()
if context == "":
context = "N/A"
return context
def get_choice_text(probelm, options):
choices = probelm['choices']
choice_list = []
for i, c in enumerate(choices):
choice_list.append("({}) {}".format(options[i], c))
choice_txt = " ".join(choice_list)
return choice_txt
def get_origin_answer(problem, options):
return problem['choices'][problem['answer']]
def get_answer(problem, options):
return options[problem['answer']]
def get_lecture_text(problem):
lecture = problem['lecture'].replace("\n", "\\n")
return lecture
def get_solution_text(problem):
# \\n: GPT-3 can generate the solution with more tokens
solution = problem['solution'].replace("\n", "\\n")
return solution
def create_one_example_coco(format, question, context, choice, answer, lecture, solution, test_example=True, WithOutput = False, curr_le_data=None):
input_format, output_format = format.split("-")
## Inputs
if input_format == "CQM":
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
elif input_format == "Q":
input = f"Question: {question}\n"
elif input_format == "QG":
if curr_le_data is not None:
input = f"Question: {question}\n Solution: {curr_le_data}\n"
else:
input = f"Question: {question}\n Solution: {solution}\n"
elif input_format == "QCM":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
elif input_format == "QM":
input = f"Question: {question}\nOptions: {choice}\n"
elif input_format == "QC":
input = f"Question: {question}\nContext: {context}\n"
elif input_format == "QCMG":
if curr_le_data is not None:
input = f"Question: {question}\n{curr_le_data}\n"
else:
input = f"Question: {question}\n{solution}\n"
elif input_format == "CQMG":
if curr_le_data is not None:
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n{curr_le_data}\n"
else:
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\nSolution: {lecture} {solution}\n"
# upper bound experiment
elif input_format == "QCML":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
elif input_format == "QCME":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
elif input_format == "QCMLE":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
elif input_format == "QCLM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
elif input_format == "QCEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
elif input_format == "QCLEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
elif input_format == "QCMA":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nAnswer: The answer is {answer}.\n"
elif input_format == "QCA":
input = f"Question: {question}\nContext: {context}\nAnswer: The answer is {answer}. \nBECAUSE:"
# Outputs
if test_example:
if output_format == 'A':
output = "Answer:"
elif output_format == 'E':
output = "Solution:"
else:
output = "Solution:"
elif output_format == 'A':
# output = f"Answer: The answer is {answer}."
output = f"Answer: {answer}"
elif output_format == 'AL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
elif output_format == 'AE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
elif output_format == 'ALE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
elif output_format == 'AEL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
elif output_format == 'LA':
output = f"Answer: {lecture} The answer is {answer}."
elif output_format == 'EA':
output = f"Answer: {solution} The answer is {answer}."
elif output_format == 'LEA':
output = f"Answer: {lecture} {solution} The answer is {answer}."
elif output_format == 'ELA':
output = f"Answer: {solution} {lecture} The answer is {answer}."
elif output_format == 'LE':
output = f"Solution: {lecture} {solution}."
elif output_format == 'E':
output = f"Solution: {solution}"
if WithOutput:
if output.endswith("BECAUSE:"):
output = output.replace("BECAUSE:", "").strip()
if output_format == 'E':
text = input + f'Solution:'
elif output_format == 'A':
text = input + f'Answer:'
else:
text = input + f'Solution:'
text = text.replace(" ", " ").strip()
output = output.replace(" ", " ").strip()
return text, output
text = input + output
text = text.replace(" ", " ").strip()
if text.endswith("BECAUSE:"):
text = text.replace("BECAUSE:", "").strip()
return text
def build_train_pair_coco(coco_data, image_id, args, curr_le_data=None):
def get_image_description(coco_data, image_id):
if "final_answer" in coco_data[image_id]:
return coco_data[image_id]["final_answer"]
else:
return ""
def get_image_question(coco_data, image_id):
if "question" in coco_data[image_id]:
return coco_data[image_id]["question"]
else:
return ""
def get_image_Context(coco_data, image_id):
if "Context" in coco_data[image_id]:
return coco_data[image_id]["Context"]
else:
return ""
def get_image_Context(coco_data, image_id):
return coco_data[image_id]["Context"]
examples = []
question = get_image_question(coco_data, image_id) #问题
solution = get_image_Context(coco_data, image_id) #推理
answer = get_image_description(coco_data, image_id) #最终答案
context=""
choice=""
lecture=""
test_example, target = create_one_example_coco(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=False,WithOutput = True, curr_le_data=curr_le_data)
examples.append(test_example)
# target = target.replace("Answer:", "").strip()
# create the prompt input
prompt_input = '\n\n'.join(examples)
return prompt_input, target
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of data.
Property names are the same names as the corresponding inputs to a model.
"""
input_ids: List[List[int]]
attention_mask: Optional[List[List[int]]]
token_type_ids: Optional[List[List[int]]]
le_input_ids: List[List[int]]
le_attention_mask: Optional[List[List[int]]]
le_token_type_ids: Optional[List[List[int]]]
label: Optional[int]