

lowercase_   							=  range(2, 2_0 + 1)
lowercase_   							=  [1_0**k for k in range(ks[-1] + 1)]
lowercase_   							=  {}
def 	a (     A__      :  Any ,  A__      :  Any ,  A__      :  Optional[int] ,  A__      :  Any	)       -> int:





      """simple docstring"""



      _lowercase      			=sum(a_i[j] for j in range(A__ ,  len(A__	)	)	)
      _lowercase      			=sum(a_i[j] * base[j] for j in range(min(len(A__	) ,  A__	)	)	)

      _lowercase			,			_lowercase      			=0, 0
      _lowercase      			=n - i

      _lowercase      			=memo.get(A__	)

      if sub_memo is not None:
            _lowercase      			=sub_memo.get(A__	)

            if jumps is not None and len(A__	) > 0:
                  # find and make the largest jump without going over
                  _lowercase      			=-1
                  for _k in range(len(A__	) - 1 ,  -1 ,  -1	):
                        if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                              _lowercase      			=_k
                              break

                  if max_jump >= 0:
                        _lowercase			,			_lowercase			,			_lowercase      			=jumps[max_jump]
                        # since the difference between jumps is cached, add c
                        _lowercase      			=diff + c
                        for j in range(min(A__ ,  len(A__	)	)	):
                              _lowercase			,			_lowercase      			=divmod(A__ ,  10	)
                        if new_c > 0:
                              add(A__ ,  A__ ,  A__	)

            else:
                  _lowercase      			=[]
      else:
            _lowercase      			={c: []}
            _lowercase      			=sub_memo

      if dn >= max_dn or c + diff >= base[k]:
            return diff, dn

      if k > ks[0]:
            while True:
                  # keep doing smaller jumps
                  _lowercase			,			_lowercase      			=next_term(A__ ,  k - 1 ,  i + dn ,  A__	)
                  diff += _diff
                  dn += terms_jumped

                  if dn >= max_dn or c + diff >= base[k]:
                        break
      else:
            # would be too small a jump, just compute sequential terms instead
            _lowercase			,			_lowercase      			=compute(A__ ,  A__ ,  i + dn ,  A__	)
            diff += _diff
            dn += terms_jumped

      _lowercase      			=sub_memo[c]

      # keep jumps sorted by # of terms skipped
      _lowercase      			=0
      while j < len(A__	):
            if jumps[j][1] > dn:
                  break
            j += 1

      # cache the jump for this value digitsum(b) and c
      sub_memo[c].insert(A__ ,  (diff, dn, k)	)
      return (diff, dn)
def 	a (     A__      :  Union[str, Any] ,  A__      :  Dict ,  A__      :  Any ,  A__      :  Dict	)       -> str:





      """simple docstring"""



      if i >= n:
            return 0, i
      if k > len(A__	):
            a_i.extend([0 for _ in range(k - len(A__	)	)]	)

      # note: a_i -> b * 10^k + c
      # ds_b -> digitsum(b)
      # ds_c -> digitsum(c)
      _lowercase      			=i
      _lowercase			,			_lowercase			,			_lowercase      			=0, 0, 0
      for j in range(len(A__	)	):
            if j >= k:
                  ds_b += a_i[j]
            else:
                  ds_c += a_i[j]

      while i < n:
            i += 1
            _lowercase      			=ds_c + ds_b
            diff += addend
            _lowercase      			=0
            for j in range(A__	):
                  _lowercase      			=a_i[j] + addend
                  _lowercase			,			_lowercase      			=divmod(A__ ,  10	)

                  ds_c += a_i[j]

            if addend > 0:
                  break

      if addend > 0:
            add(A__ ,  A__ ,  A__	)
      return diff, i - start_i
def 	a (     A__      :  Optional[Any] ,  A__      :  Tuple ,  A__      :  Optional[int]	)       -> List[str]:





      """simple docstring"""



      for j in range(A__ ,  len(A__	)	):
            _lowercase      			=digits[j] + addend
            if s >= 10:
                  _lowercase			,			_lowercase      			=divmod(A__ ,  10	)
                  _lowercase      			=addend // 10 + quotient
            else:
                  _lowercase      			=s
                  _lowercase      			=addend // 10

            if addend == 0:
                  break

      while addend > 0:
            _lowercase			,			_lowercase      			=divmod(A__ ,  10	)
            digits.append(A__	)
def 	a (     A__      :  int = 10**15	)       -> int:





      """simple docstring"""



      _lowercase      			=[1]
      _lowercase      			=1
      _lowercase      			=0
      while True:
            _lowercase			,			_lowercase      			=next_term(A__ ,  20 ,  i + dn ,  A__	)
            dn += terms_jumped
            if dn == n - i:
                  break

      _lowercase      			=0
      for j in range(len(A__	)	):
            a_n += digits[j] * 10**j
      return a_n


if __name__ == "__main__":
  print(f"{solution() = }")





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


