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import copy as cp
import json
from collections import defaultdict
from urllib.request import urlopen
import gradio as gr
import numpy as np
import pandas as pd
from meta_data import META_FIELDS, URL, DATASETS_ALL, DATASETS_ESS
def listinstr(lst, s):
assert isinstance(lst, list)
for item in lst:
if item in s:
return True
return False
def upper_key(k):
if k == 'ocr':
return 'OCR'
elif '_' in k:
k = k.split('_')
k = [x[0].upper() + x[1:] for x in k]
k = ' '.join(k)
return k
else:
return k
def load_results():
data = json.loads(urlopen(URL).read())
return data
def nth_large(val, vals):
return sum([1 for v in vals if v > val]) + 1
def model_size_flag(sz, FIELDS):
if pd.isna(sz) and 'Unknown' in FIELDS:
return True
if pd.isna(sz):
return False
sz = int(sz)
if '<4B' in FIELDS and sz < 4:
return True
if '4B-10B' in FIELDS and sz >= 4 and sz < 10:
return True
if '10B-20B' in FIELDS and sz >= 10 and sz < 20:
return True
if '20B-40B' in FIELDS and sz >= 20 and sz < 40:
return True
if '>40B' in FIELDS and sz >= 40:
return True
return False
def model_type_flag(line, FIELDS):
if 'OpenSource' in FIELDS and line['OpenSource'] == 'Yes':
return True
if 'API' in FIELDS and line['OpenSource'] == 'No':
return True
return False
def BUILD_L1_DF(results):
check_box = {}
check_box['essential'] = ['Method', 'Org', 'Param (B)', 'Language Model', 'Vision Model']
# revise there to set default dataset
check_box['required'] = ['Overall'] + DATASETS_ESS
check_box['all'] = ['Overall'] + DATASETS_ALL
type_map = defaultdict(lambda: 'number')
type_map['Method'] = 'html'
type_map['Language Model'] = type_map['Vision Model'] = type_map['Org'] = 'html'
type_map['OpenSource'] = type_map['Verified'] = 'str'
check_box['type_map'] = type_map
df = generate_table(results)
return df, check_box
def BUILD_L2_DF(results, dataset):
res = defaultdict(list)
sub = [v for v in results.values() if dataset in v]
assert len(sub), dataset
fields = list(sub[0][dataset].keys())
non_overall_fields = [x for x in fields if 'Overall' not in x]
overall_fields = [x for x in fields if 'Overall' in x]
for m in results:
item = results[m]
if dataset not in item:
continue
for k in META_FIELDS:
if k == 'Param (B)':
param = item['META']['Parameters']
res[k].append(float(param.replace('B', '')) if param != '' else None)
elif k == 'Method':
name, url = item['META']['Method']
res[k].append(f'<a href="{url}">{name}</a>')
else:
s = item['META'][k].replace('\n', '<br>')
s = s.replace(' & ', '<br>')
res[k].append(s)
for d in overall_fields:
res[d].append(float(item[dataset][d]))
for d in non_overall_fields:
res[d].append(float(item[dataset][d]))
df = pd.DataFrame(res)
all_fields = overall_fields + non_overall_fields
# Use the first 5 non-overall fields as required fields
required_fields = overall_fields if len(overall_fields) else non_overall_fields[:5]
df = df.sort_values('Overall')
df = df.iloc[::-1]
check_box = {}
check_box['essential'] = ['Method', 'Org', 'Param (B)', 'Language Model', 'Vision Model']
check_box['required'] = required_fields
check_box['all'] = all_fields
type_map = defaultdict(lambda: 'number')
type_map['Method'] = 'html'
type_map['Language Model'] = type_map['Vision Model'] = type_map['Org'] = 'html'
type_map['OpenSource'] = type_map['Verified'] = 'str'
check_box['type_map'] = type_map
return df, check_box
def generate_table(results):
res = defaultdict(list)
for i, m in enumerate(results):
item = results[m]
avg = 0
for k in META_FIELDS:
if k == 'Param (B)':
param = item['META']['Parameters']
res[k].append(float(param.replace('B', '')) if param != '' else None)
elif k == 'Method':
name, url = item['META']['Method']
res[k].append(f'<a href="{url}">{name}</a>')
else:
s = item['META'][k].replace('\n', '<br>')
s = s.replace(' & ', '<br>')
res[k].append(s)
for d in DATASETS_ALL:
key_name = 'Overall'
if d in item:
val = float(item[d][key_name])
val = float(f'{val:.1f}')
res[d].append(val)
else:
res[d].append(None)
if d in DATASETS_ESS:
if d in item and avg is not None:
avg += res[d][-1]
else:
avg = None
if avg is not None:
avg = float(f'{avg / len(DATASETS_ESS):.1f}')
res['Overall'].append(avg)
df = pd.DataFrame(res)
overall_isna = df[pd.isna(df['Overall'])]
overall_notna = df[~pd.isna(df['Overall'])]
overall_notna = overall_notna.sort_values('Overall')
overall_notna = overall_notna.iloc[::-1]
overall_isna = overall_isna.sort_values('MathVista')
overall_isna = overall_isna.iloc[::-1]
df = pd.concat([overall_notna, overall_isna])
return df
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