
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


SCREAMING_SNAKE_CASE				:Tuple									=							logging.get_logger(__name__)


def 		UpperCAmelCase     (      a_						,  a_		)						->      List[str]:







				"""simple docstring"""



				try:
								with open(a_						,  "rb"		) as flax_state_f:
												__A       =					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 		UpperCAmelCase     (      a_						,  a_		)						->      List[Any]:







				"""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
				__A       =					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."		)
								__A       =					jax.tree_util.tree_map(
								    lambda a_		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params						,  a_		)

				__A       =					""

				__A       =					flatten_dict(a_						,  sep="."		)
				__A       =					pt_model.state_dict()

				# keep track of unexpected & missing keys
				__A       =					[]
				__A       =					set(pt_model_dict.keys()		)

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								__A       =					flax_key_tuple.split("."		)

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

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(a_		):
																__A       =					(
																    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"		)
																)

								__A       =					".".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
																__A       =					np.asarray(a_		) if not isinstance(a_						,  np.ndarray		) else flax_tensor
																__A       =					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
				__A       =					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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      if n == 1 or not isinstance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
            return 0
      elif n == 2:
            return 1
      else:
            snake_case__       :   int	      =    [0, 1]
            for i in range(2				,      n + 1		):
                  sequence.append(sequence[i - 1] + sequence[i - 2]		)

            return sequence[n]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Optional[int]	      =    0
      snake_case__       :   Dict	      =    2

      while digits < n:
            index += 1
            snake_case__       :   Optional[int]	      =    len(str(fibonacci(__SCREAMING_SNAKE_CASE		)		)		)

      return index






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE = 1000		)    ->   int:
      return fibonacci_digits_index(__SCREAMING_SNAKE_CASE		)


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