
import sys

from transformers import AutoTokenizer


_lowerCamelCase        =sys.argv[1]
_lowerCamelCase        =sys.argv[2]
_lowerCamelCase        =int(sys.argv[3])

_lowerCamelCase        =0

_lowerCamelCase        =AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
      for line in f_p:
            _lowerCamelCase        =line.rstrip()

            if not line:
                  print(line)
                  _lowerCamelCase        =0
                  continue

            _lowerCamelCase        =line.split()[0]

            _lowerCamelCase        =len(tokenizer.tokenize(token))

            # Token contains strange control characters like \x96 or \x95
            # Just filter out the complete line
            if current_subwords_len == 0:
                  continue

            if (subword_len_counter + current_subwords_len) > max_len:
                  print("""""")
                  print(line)
                  _lowerCamelCase        =current_subwords_len
                  continue

            subword_len_counter += current_subwords_len

            print(line)






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # Creating a dictionary containing count of each word
 for word in sentence.split():
  occurrence[word] += 1
 return occurrence


if __name__ == "__main__":
      for word, count in word_occurrence("""INPUT STRING""").items():
            print(f'''{word}: {count}''')




