import sentencepiece as spm


spm.SentencePieceTrainer.Train(
    input = 'all.txt',
    model_prefix = 'all',
    vocab_size = 50304,
    pad_id = 0,                
    unk_id = 1,
    bos_id = 2,
    eos_id = 3,
    pad_piece = '<pad>',
    unk_piece = '<unk>',
    bos_piece = '<bos>',
    eos_piece = '<eos>',
    model_type = 'bpe',
    num_threads = 256,
    add_dummy_prefix = False,
    byte_fallback = True,
    character_coverage = 0.9999,
    user_defined_symbols ='<pad>,<eos>,<bos>,<unk>,<mask>,<unused0>,<unused1>,<unused2>,<unused3>,<unused4>,<unused5>,<unused6>,<unused7>,<unused8>,<unused9>,<unused10>,<unused11>,<unused12>,<unused13>,<unused14>,<unused15>,<unused16>,<unused17>,<unused18>,<unused19>,<unused20>,<unused21>,<unused22>,<unused23>,<unused24>,<unused25>,<unused26>,<unused27>,<unused28>,<unused29>,<unused30>,<unused31>,<unused32>,<unused33>,<unused34>,<unused35>,<unused36>,<unused37>,<unused38>,<unused39>,<unused40>,<unused41>,<unused42>,<unused43>,<unused44>,<unused45>,<unused46>,<unused47>,<unused48>,<unused49>,<unused50>,<unused51>,<unused52>,<unused53>,<unused54>,<unused55>,<unused56>,<unused57>,<unused58>,<unused59>,<unused60>,<unused61>,<unused62>,<unused63>,<unused64>,<unused65>,<unused66>,<unused67>,<unused68>,<unused69>,<unused70>,<unused71>,<unused72>,<unused73>,<unused74>,<unused75>,<unused76>,<unused77>,<unused78>,<unused79>,<unused80>,<unused81>,<unused82>,<unused83>,<unused84>,<unused85>,<unused86>,<unused87>,<unused88>,<unused89>,<unused90>,<unused91>,<unused92>,<unused93>,<unused94>,<unused95>,<unused96>,<unused97>,<unused98>,<start_of_turn>,<end_of_turn>'
    
    
)

exit()

# from datasets import concatenate_datasets, load_dataset, load_from_disk
# from datasets import DatasetDict, Dataset
# import random

# def train_tokenizer(args):

code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']

# indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
# indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
# wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
    
# num_docs = 100000
# hi = random.sample(indic_datasets_hi, num_docs)
# bn = random.sample(indic_datasets_bn, num_docs)
# en = random.sample(wikipedia_en, num_docs)


# # combined_train_set = wikipedia_en

# # combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi + indic_datasets_bn + wikipedia_en
# combined_train_set= en + hi + bn



# data = {
#         "train":{"text": combined_train_set},
#         "validation": {"text": []},
#         "test": {"text": []},
#     }
#     # print(data)
# custom_dataset = DatasetDict()
# for split in data:
#     custom_dataset[split] = Dataset.from_dict(data[split])
# custom_dataset=custom_dataset["train"]
# # lines = []

# with open('all.txt','w') as f:
#     for text in custom_dataset['text']:
#         lines = text.split("\n")
#         for l in lines:
#             f.write(l.strip() + '\n')
#             # break