






'''simple docstring'''

from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCamelCase      					=logging.get_logger(__name__)



def 				snake_case       (  a_				:			Any     , a_				:			Any						)							->					int:





      """simple docstring"""

      try:
            with open(a_     , """rb"""						) as flax_state_f:
                  UpperCamelCase_					: Optional[int]            =				from_bytes(a_     , flax_state_f.read()						)
      except UnpicklingError as e:
            try:
                  with open(a_						) as f:
                        if f.read().startswith("""version"""						):
                              raise OSError(
                                  """You seem to have cloned a repository without having git-lfs installed. Please"""
                                  """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                                  """ folder you cloned."""						)
                        else:
                              raise ValueError from e
            except (UnicodeDecodeError, ValueError):
                  raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. "						)

      return load_flax_weights_in_pytorch_model(a_     , a_						)



def 				snake_case       (  a_				:			Optional[int]     , a_				:			Dict						)							->					Dict:





      """simple docstring"""

      try:
            import torch  # noqa: F401
      except ImportError:
            logger.error(
                """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
                """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
                """ instructions."""						)
            raise

      # check if we have bf16 weights
      UpperCamelCase_					: str            =				flatten_dict(jax.tree_util.tree_map(lambda a_						: x.dtype == jnp.bfloataa     , a_						)						).values()
      if any(a_						):
            # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

            # and bf16 is not fully supported in PT yet.
            logger.warning(
                """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
                """before loading those in PyTorch model."""						)
            UpperCamelCase_					: Optional[int]            =				jax.tree_util.tree_map(
                lambda a_						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params     , a_						)

      UpperCamelCase_					: Any            =				""""""

      UpperCamelCase_					: Tuple            =				flatten_dict(a_     , sep="""."""						)
      UpperCamelCase_					: Any            =				pt_model.state_dict()

      # keep track of unexpected & missing keys
      UpperCamelCase_					: Union[str, Any]            =				[]
      UpperCamelCase_					: Any            =				set(pt_model_dict.keys()						)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            UpperCamelCase_					: List[str]            =				flax_key_tuple.split("""."""						)

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  UpperCamelCase_					: Tuple            =				flax_key_tuple_array[:-1] + ["""weight"""]
                  UpperCamelCase_					: Optional[Any]            =				jnp.transpose(a_     , (3, 2, 0, 1)						)
            elif flax_key_tuple_array[-1] == "kernel":
                  UpperCamelCase_					: Any            =				flax_key_tuple_array[:-1] + ["""weight"""]
                  UpperCamelCase_					: Optional[int]            =				flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  UpperCamelCase_					: Optional[int]            =				flax_key_tuple_array[:-1] + ["""weight"""]

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(a_						):
                        UpperCamelCase_					: int            =				(
                            flax_key_tuple_string.replace("""_0"""     , """.0"""						)
                            .replace("""_1"""     , """.1"""						)
                            .replace("""_2"""     , """.2"""						)
                            .replace("""_3"""     , """.3"""						)
                            .replace("""_4"""     , """.4"""						)
                            .replace("""_5"""     , """.5"""						)
                            .replace("""_6"""     , """.6"""						)
                            .replace("""_7"""     , """.7"""						)
                            .replace("""_8"""     , """.8"""						)
                            .replace("""_9"""     , """.9"""						)
                        )

            UpperCamelCase_					: Tuple            =				""".""".join(a_						)

            if flax_key in pt_model_dict:
                  if flax_tensor.shape != pt_model_dict[flax_key].shape:
                        raise ValueError(
                            f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                            f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."						)
                  else:
                        # add weight to pytorch dict
                        UpperCamelCase_					: Optional[Any]            =				np.asarray(a_						) if not isinstance(a_     , np.ndarray						) else flax_tensor
                        UpperCamelCase_					: Optional[int]            =				torch.from_numpy(a_						)
                        # remove from missing keys
                        missing_keys.remove(a_						)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(a_						)

      pt_model.load_state_dict(a_						)

      # re-transform missing_keys to list
      UpperCamelCase_					: str            =				list(a_						)

      if len(a_						) > 0:
            logger.warning(
                """Some weights of the Flax model were not used when initializing the PyTorch model"""
                f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
                f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
                """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
                f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
                """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
                """ FlaxBertForSequenceClassification model)."""						)
      if len(a_						) > 0:
            logger.warning(
                f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
                f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
                """ use it for predictions and inference."""						)

      return pt_model














'''simple docstring'''

def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

      if n == 1 or not isinstance(a_     , a_						):
            return 0
      elif n == 2:
            return 1
      else:
            UpperCamelCase_					: Dict            =				[0, 1]
            for i in range(2     , n + 1						):
                  sequence.append(sequence[i - 1] + sequence[i - 2]						)

            return sequence[n]



def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: Any            =				0
      UpperCamelCase_					: Union[str, Any]            =				2

      while digits < n:
            index += 1
            UpperCamelCase_					: Any            =				len(str(fibonacci(a_						)						)						)

      return index



def 				snake_case       (  a_				:			int = 1_000						)							->					int:





      """simple docstring"""

      return fibonacci_digits_index(a_						)


if __name__ == "__main__":
      print(solution(int(str(input()).strip())))






