





import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
					import bitsandbytes as bnb
					import torch
					import torch.nn as nn

					from ..pytorch_utils import ConvaD

if is_accelerate_available():
					from accelerate import init_empty_weights
					from accelerate.utils import find_tied_parameters

__SCREAMING_SNAKE_CASE							:    List[Any]          =logging.get_logger(__name__)


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None ):





				if "." in tensor_name:
								lowercase							=     tensor_name.split(""".""" )
								for split in splits[:-1]:
												lowercase							=     getattr(lowercase__       ,lowercase__ )
												if new_module is None:
																raise ValueError(f"""{module} has no attribute {split}.""" )
												lowercase							=     new_module
								lowercase							=     splits[-1]

				if tensor_name not in module._parameters and tensor_name not in module._buffers:
								raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}.""" )
				lowercase							=     tensor_name in module._buffers
				lowercase							=     getattr(lowercase__       ,lowercase__ )

				if old_value.device == torch.device("""meta""" ) and device not in ["meta", torch.device("""meta""" )] and value is None:
								raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}.""" )

				lowercase							=     False
				lowercase							=     False
				if is_buffer or not is_bitsandbytes_available():
								lowercase							=     False
								lowercase							=     False
				else:
								lowercase							=     hasattr(bnb.nn       ,"""Params4bit""" ) and isinstance(module._parameters[tensor_name]       ,bnb.nn.Paramsabit )
								lowercase							=     isinstance(module._parameters[tensor_name]       ,bnb.nn.IntaParams )

				if is_abit or is_abit:
								lowercase							=     module._parameters[tensor_name]
								if param.device.type != "cuda":
												if value is None:
																lowercase							=     old_value.to(lowercase__ )
												elif isinstance(lowercase__       ,torch.Tensor ):
																lowercase							=     value.to("""cpu""" )
																if value.dtype == torch.inta:
																				lowercase							=     version.parse(importlib.metadata.version("""bitsandbytes""" ) ) > version.parse(
																				    """0.37.2""" )
																				if not is_abit_serializable:
																								raise ValueError(
																								    """Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
																								    """Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.""" )
												else:
																lowercase							=     torch.tensor(lowercase__       ,device="""cpu""" )

												# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
												# Since weights are saved in the correct "orientation", we skip transposing when loading.
												if issubclass(module.source_cls       ,lowercase__ ) and fpaa_statistics is None:
																lowercase							=     new_value.T

												lowercase							=     old_value.__dict__
												if is_abit:
																lowercase							=     bnb.nn.IntaParams(lowercase__       ,requires_grad=lowercase__       ,**lowercase__ ).to(lowercase__ )
												elif is_abit:
																lowercase							=     bnb.nn.Paramsabit(lowercase__       ,requires_grad=lowercase__       ,**lowercase__ ).to(lowercase__ )

												lowercase							=     new_value
												if fpaa_statistics is not None:
																setattr(module.weight       ,"""SCB"""       ,fpaa_statistics.to(lowercase__ ) )

				else:
								if value is None:
												lowercase							=     old_value.to(lowercase__ )
								elif isinstance(lowercase__       ,torch.Tensor ):
												lowercase							=     value.to(lowercase__ )
								else:
												lowercase							=     torch.tensor(lowercase__       ,device=lowercase__ )

								if is_buffer:
												lowercase							=     new_value
								else:
												lowercase							=     nn.Parameter(lowercase__       ,requires_grad=old_value.requires_grad )
												lowercase							=     new_value


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=False ):





				for name, module in model.named_children():
								if current_key_name is None:
												lowercase							=     []
								current_key_name.append(lowercase__ )

								if (isinstance(lowercase__       ,nn.Linear ) or isinstance(lowercase__       ,lowercase__ )) and name not in modules_to_not_convert:
												# Check if the current key is not in the `modules_to_not_convert`
												if not any(key in """.""".join(lowercase__ ) for key in modules_to_not_convert ):
																with init_empty_weights():
																				if isinstance(lowercase__       ,lowercase__ ):
																								lowercase					,    lowercase							=     module.weight.shape
																				else:
																								lowercase							=     module.in_features
																								lowercase							=     module.out_features

																				if quantization_config.quantization_method() == "llm_int8":
																								lowercase							=     bnb.nn.LinearabitLt(
																								    lowercase__       ,lowercase__       ,module.bias is not None       ,has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight       ,threshold=quantization_config.llm_inta_threshold       ,)
																								lowercase							=     True
																				else:
																								if (
																								    quantization_config.llm_inta_skip_modules is not None
																								    and name in quantization_config.llm_inta_skip_modules
																								):
																												pass
																								else:
																												lowercase							=     bnb.nn.Linearabit(
																												    lowercase__       ,lowercase__       ,module.bias is not None       ,quantization_config.bnb_abit_compute_dtype       ,compress_statistics=quantization_config.bnb_abit_use_double_quant       ,quant_type=quantization_config.bnb_abit_quant_type       ,)
																												lowercase							=     True
                    # Store the module class in case we need to transpose the weight later
																				lowercase							=     type(lowercase__ )
																				# Force requires grad to False to avoid unexpected errors
																				model._modules[name].requires_grad_(lowercase__ )
								if len(list(module.children() ) ) > 0:
												lowercase					,    lowercase							=     _replace_with_bnb_linear(
												    lowercase__       ,lowercase__       ,lowercase__       ,lowercase__       ,has_been_replaced=lowercase__       ,)
								# Remove the last key for recursion
								current_key_name.pop(-1 )
				return model, has_been_replaced


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None ):
				lowercase							=     ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
				lowercase					,    lowercase							=     _replace_with_bnb_linear(
				    lowercase__       ,lowercase__       ,lowercase__       ,lowercase__ )

				if not has_been_replaced:
								logger.warning(
								    """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
								    """ Please double check your model architecture, or submit an issue on github if you think this is"""
								    """ a bug.""" )

				return model


def       UpperCamelCase__    (		*lowerCAmelCase__       ,**lowerCAmelCase__ ):
				warnings.warn(
				    """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""       ,lowercase__       ,)
				return replace_with_bnb_linear(*lowercase__       ,**lowercase__ )


def       UpperCamelCase__    (		*lowerCAmelCase__       ,**lowerCAmelCase__ ):
				warnings.warn(
				    """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"""       ,lowercase__       ,)
				return set_module_quantized_tensor_to_device(*lowercase__       ,**lowercase__ )







def       UpperCamelCase__    (		lowerCAmelCase__ ):
				lowercase							=     deepcopy(lowercase__ )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
				tied_model.tie_weights()

				lowercase							=     find_tied_parameters(lowercase__ )
				# For compatibility with Accelerate < 0.18
				if isinstance(lowercase__       ,lowercase__ ):
								lowercase							=     sum(list(tied_params.values() )       ,[] ) + list(tied_params.keys() )
				else:
								lowercase							=     sum(lowercase__       ,[] )
				lowercase							=     len(lowercase__ ) > 0

				# Check if it is a base model
				lowercase							=     not hasattr(lowercase__       ,model.base_model_prefix )

				# Ignore this for base models (BertModel, GPT2Model, etc.)
				if (not has_tied_params) and is_base_model:
								return []

				# otherwise they have an attached head
				lowercase							=     list(model.named_children() )
				lowercase							=     [list_modules[-1][0]]

				# add last module together with tied weights
				lowercase							=     set(lowercase__ ) - set(lowercase__ )
				lowercase							=     list(set(lowercase__ ) ) + list(lowercase__ )

				# remove ".weight" from the keys
				lowercase							=     [""".weight""", """.bias"""]
				lowercase							=     []
				for name in list_untouched:
								for name_to_remove in names_to_remove:
												if name_to_remove in name:
																lowercase							=     name.replace(lowercase__       ,"""""" )
								filtered_module_names.append(lowercase__ )

				return filtered_module_names





'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





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

          return sequence[n]

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=0
     __lowercase      		=2

     while digits < n:
          index += 1
          __lowercase      		=len(str(fibonacci(lowercase__			)			)			)

     return index

def 					__UpperCamelCase				(						lowercase__    :	int = 10_00			):






     '''simple docstring'''





     return fibonacci_digits_index(lowercase__			)


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


