




"""simple docstring"""



import math as m







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=0.0_0_1		)		->						Union[str, Any]:
 return (f(a + h		) - f(a - h		)) / (2 * h)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=0     ,					lowercase_=1_00     ,					lowercase_=0.0_0_0_1     ,					lowercase_=1E-6     ,					lowercase_=False		)		->						Optional[int]:
 A__			=							xa  # set the initial guess
 A__			=							[a]
 A__			=							abs(f(SCREAMING_SNAKE_CASE_		)		)
 A__			=							lambda lowercase_		: calc_derivative(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					h=SCREAMING_SNAKE_CASE_		)  # noqa: E731  Derivative of f(x)
 for _ in range(SCREAMING_SNAKE_CASE_		):
  if fa(SCREAMING_SNAKE_CASE_		) == 0:
   raise ValueError("No converging solution found"		)
  A__			=							a - f(SCREAMING_SNAKE_CASE_		) / fa(SCREAMING_SNAKE_CASE_		)  # Calculate the next estimate
  if logsteps:
   steps.append(SCREAMING_SNAKE_CASE_		)
  if error < maxerror:
   break
 else:
  raise ValueError("Iteration limit reached, no converging solution found"		)
 if logsteps:
  # If logstep is true, then log intermediate steps
  return a, error, steps
 return a, error


if __name__ == "__main__":
      from matplotlib import pyplot as plt

      SCREAMING_SNAKE_CASE							=			lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
      SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE							=			newton_raphson(
          f, xa=10, maxiter=1000, step=1E-6, logsteps=True
      )
      plt.plot([abs(f(x)) for x in steps])
      plt.xlabel("step")
      plt.ylabel("error")
      plt.show()
      print(f'solution = {{{solution:f}}}, error = {{{error:f}}}')












import sys

from transformers import AutoTokenizer


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

snake_case_				=      0

snake_case_				=      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:
								snake_case_				=      line.rstrip()

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

								snake_case_				=      line.split()[0]

								snake_case_				=      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)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

