#from Data import *
from datasets.Casia import *

'''
Because the size of samples per class had the biggest impact
on training outcome, I decided to check the maximum amount of data 
I can get from a class. 
The highest I can get is 
Rank     | Identity ID  |Count 
-----------------------------------
1        | 3782         | 35          
2        | 2820         | 35          
3        | 3227         | 35          
4        | 3745         | 34          
5        | 3699         | 34          
6        | 8968         | 32          
7        | 9152         | 32          
8        | 9256         | 32          
9        | 2114         | 31          
...      | ...          | ...            
17       | 4126         | 31          
18       | 3185         | 30                  
...      | ...          | ...       
50       | 3186         | 30   

as can be seen, 3 classes have 35, 2 have 34, 3 have 32 and the rest have 30.
'''
def print_top_identity_stats(dataset, top_n=50):
    # we get data
    ids, counts = get_ids_and_counts(dataset)
    # sort in descending order
    sorted_counts, sorted_indices = torch.sort(counts, descending=True)

    # coresponding sorted ids
    sorted_ids = ids[sorted_indices]

    # 4. Slice the first 'top_n' and print
    print(f"{'Rank':<8} | {'Identity ID':<12} | {'Image Count':<12}")
    print("-" * 35)
    
    for i in range(top_n):
        identity_id = sorted_ids[i].item()
        count = sorted_counts[i].item()
        print(f"{i+1:<8} | {identity_id:<12} | {count:<12}")

# Usage:
dataset = get_set()
print_top_identity_stats(dataset, 50)