import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowercase_  : int								=					datasets.utils.logging.get_logger(__name__)

lowercase_  : Tuple								=					['names', 'prefix']
lowercase_  : str								=					['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowercase_  : Optional[int]								=					['encoding_errors', 'on_bad_lines']
lowercase_  : List[Any]								=					['date_format']






@dataclass
class 		_lowerCamelCase		( datasets.BuilderConfig     ):
    __a						=   ","
    __a						=   None
    __a						=   "infer"
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   True
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   False
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   True
    __a						=   True
    __a						=   False
    __a						=   True
    __a						=   None
    __a						=   "."
    __a						=   None
    __a						=   '"'
    __a						=   0
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   None
    __a						=   True
    __a						=   True
    __a						=   0
    __a						=   True
    __a						=   False
    __a						=   None
    __a						=   10000
    __a						=   None
    __a						=   "strict"
    __a						=   "error"
    __a						=   None
    def     UpperCamelCase_					(	self )							->       Dict:
     if self.delimiter is not None:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   self.delimiter
     if self.column_names is not None:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.column_names



    @property
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: str=   {
         '''sep''': self.sep,
         '''header''': self.header,
         '''names''': self.names,
         '''index_col''': self.index_col,
         '''usecols''': self.usecols,
         '''prefix''': self.prefix,
         '''mangle_dupe_cols''': self.mangle_dupe_cols,
         '''engine''': self.engine,
         '''converters''': self.converters,
         '''true_values''': self.true_values,
         '''false_values''': self.false_values,
         '''skipinitialspace''': self.skipinitialspace,
         '''skiprows''': self.skiprows,
         '''nrows''': self.nrows,
         '''na_values''': self.na_values,
         '''keep_default_na''': self.keep_default_na,
         '''na_filter''': self.na_filter,
         '''verbose''': self.verbose,
         '''skip_blank_lines''': self.skip_blank_lines,
         '''thousands''': self.thousands,
         '''decimal''': self.decimal,
         '''lineterminator''': self.lineterminator,
         '''quotechar''': self.quotechar,
         '''quoting''': self.quoting,
         '''escapechar''': self.escapechar,
         '''comment''': self.comment,
         '''encoding''': self.encoding,
         '''dialect''': self.dialect,
         '''error_bad_lines''': self.error_bad_lines,
         '''warn_bad_lines''': self.warn_bad_lines,
         '''skipfooter''': self.skipfooter,
         '''doublequote''': self.doublequote,
         '''memory_map''': self.memory_map,
         '''float_precision''': self.float_precision,
         '''chunksize''': self.chunksize,
         '''encoding_errors''': self.encoding_errors,
         '''on_bad_lines''': self.on_bad_lines,
         '''date_format''': self.date_format,
     }

     # some kwargs must not be passed if they don't have a default value
     # some others are deprecated and we can also not pass them if they are the default value
     for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
      if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,			lowerCAmelCase ):
       del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
     if not (datasets.config.PANDAS_VERSION.major >= 2):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
       del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
     if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
       del pd_read_csv_kwargs[pd_read_csv_parameter]

     return pd_read_csv_kwargs






class 		_lowerCamelCase		( datasets.ArrowBasedBuilder     ):
    __a						=   CsvConfig
    def     UpperCamelCase_					(	self )							->       Any:
     return datasets.DatasetInfo(features=self.config.features )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     if not self.config.data_files:
      raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
     SCREAMING_SNAKE_CASE__: Optional[int]=   dl_manager.download_and_extract(self.config.data_files )
     if isinstance(lowerCAmelCase			,			(str, list, tuple) ):
      SCREAMING_SNAKE_CASE__: Any=   data_files
      if isinstance(lowerCAmelCase			,			lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Tuple=   [files]
      SCREAMING_SNAKE_CASE__: str=   [dl_manager.iter_files(lowerCAmelCase ) for file in files]
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs={'''files''': files} )]
     SCREAMING_SNAKE_CASE__: List[Any]=   []
     for split_name, files in data_files.items():
      if isinstance(lowerCAmelCase			,			lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Union[str, Any]=   [files]
      SCREAMING_SNAKE_CASE__: Optional[int]=   [dl_manager.iter_files(lowerCAmelCase ) for file in files]
      splits.append(datasets.SplitGenerator(name=lowerCAmelCase			,			gen_kwargs={'''files''': files} ) )
     return splits
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       pa.Table:
     if self.config.features is not None:
      SCREAMING_SNAKE_CASE__: int=   self.config.features.arrow_schema
      if all(not require_storage_cast(lowerCAmelCase ) for feature in self.config.features.values() ):
       # cheaper cast
       SCREAMING_SNAKE_CASE__: Optional[int]=   pa.Table.from_arrays([pa_table[field.name] for field in schema]			,			schema=lowerCAmelCase )
      else:
       # more expensive cast; allows str <-> int/float or str to Audio for example
       SCREAMING_SNAKE_CASE__: Optional[int]=   table_cast(lowerCAmelCase			,			lowerCAmelCase )
     return pa_table



    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: str=   self.config.features.arrow_schema if self.config.features else None
     # dtype allows reading an int column as str
     SCREAMING_SNAKE_CASE__: str=   (
         {
             name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase ) else object
             for name, dtype, feature in zip(schema.names			,			schema.types			,			self.config.features.values() )
         }
         if schema is not None
         else None
     )
     for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase ) ):
      SCREAMING_SNAKE_CASE__: List[Any]=   pd.read_csv(lowerCAmelCase			,			iterator=lowerCAmelCase			,			dtype=lowerCAmelCase			,			**self.config.pd_read_csv_kwargs )
      try:
       for batch_idx, df in enumerate(lowerCAmelCase ):
        SCREAMING_SNAKE_CASE__: int=   pa.Table.from_pandas(lowerCAmelCase )
        # Uncomment for debugging (will print the Arrow table size and elements)
        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
        yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase )
      except ValueError as e:
       logger.error(f'Failed to read file \'{file}\' with error {type(lowerCAmelCase )}: {e}' )
       raise



def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  if n == 1 or not isinstance(lowercase_			,			lowercase_ ):
    return 0
  elif n == 2:
    return 1
  else:
    lowercase__	: List[Any]     		=   [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__	: Optional[Any]     		=   0
  lowercase__	: Dict     		=   2

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

  return index

def 		UpperCamelCase						(   lowercase_ = 10_00 )		->						int:


  '''simple docstring'''






  return fibonacci_digits_index(lowercase_ )


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






