





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase=None     ,							lowerCAmelCase=None       ):
       return field(default_factory=lambda: default     ,							metadata=_UpperCAmelCase       )

@dataclass
class 			__A:
     __A							=							field(
         metadata={"help": "The csv file to plot."}    ,      )
     __A							=							field(
         default=__UpperCAmelCase    ,      metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,      )
     __A							=							field(
         default=__UpperCAmelCase    ,      metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,      )
     __A							=							field(
         default=__UpperCAmelCase    ,      metadata={"help": "Disable logarithmic scale when plotting"}    ,      )
     __A							=							field(
         default=__UpperCAmelCase    ,      metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         }    ,      )
     __A							=							field(
         default=__UpperCAmelCase    ,      metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,      )
     __A							=							list_field(
         default=__UpperCAmelCase    ,      metadata={"help": "List of model names that are used instead of the ones in the csv file."}			)






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       try:
              int(_UpperCAmelCase       )
              return True
       except ValueError:
              return False


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       try:
              float(_UpperCAmelCase       )
              return True
       except ValueError:
              return False

class 			__A:

     def __init__(   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    args
            _UpperCamelCase														=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

            with open(self.args.csv_file,							newline=''''''   ) as csv_file:
                   _UpperCamelCase														=    csv.DictReader(__SCREAMING_SNAKE_CASE   )
                   for row in reader:
                          _UpperCamelCase														=    row['''model''']
                          self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']   )   )
                          self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']   )   )
                          if can_convert_to_int(row['''result''']   ):
                                 # value is not None
                                 _UpperCamelCase														=    int(row['''result''']   )
                          elif can_convert_to_float(row['''result''']   ):
                                 # value is not None
                                 _UpperCamelCase														=    float(row['''result''']   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    plt.subplots()
            _UpperCamelCase														=    '''Time usage''' if self.args.is_time else '''Memory usage'''
            _UpperCamelCase														=    title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

            if not self.args.no_log_scale:
                   # set logarithm scales
                   ax.set_xscale('''log'''   )
                   ax.set_yscale('''log'''   )

            for axis in [ax.xaxis, ax.yaxis]:
                   axis.set_major_formatter(ScalarFormatter()   )

            for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
                   _UpperCamelCase														=    sorted(set(self.result_dict[model_name]['''bsz''']   )   )
                   _UpperCamelCase														=    sorted(set(self.result_dict[model_name]['''seq_len''']   )   )
                   _UpperCamelCase														=    self.result_dict[model_name]['''result''']

                   ((_UpperCamelCase)							,		(_UpperCamelCase))														=    (
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   _UpperCamelCase														=    (
                       model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                   )

                   for inner_loop_value in inner_loop_array:
                          if self.args.plot_along_batch:
                                 _UpperCamelCase														=    np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],							dtype=__SCREAMING_SNAKE_CASE,							)
                          else:
                                 _UpperCamelCase														=    np.asarray(
                                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],							dtype=np.floataa,							)

                          ((_UpperCamelCase)							,		(_UpperCamelCase))														=    (
                              ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                          )

                          _UpperCamelCase														=    np.asarray(__SCREAMING_SNAKE_CASE,							__SCREAMING_SNAKE_CASE   )[: len(__SCREAMING_SNAKE_CASE   )]
                          plt.scatter(
                              __SCREAMING_SNAKE_CASE,							__SCREAMING_SNAKE_CASE,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''   )
                          plt.plot(__SCREAMING_SNAKE_CASE,							__SCREAMING_SNAKE_CASE,							'''--'''   )

                   title_str += F''' {label_model_name} vs.'''

            _UpperCamelCase														=    title_str[:-4]
            _UpperCamelCase														=    '''Time in s''' if self.args.is_time else '''Memory in MB'''

            # plot
            plt.title(__SCREAMING_SNAKE_CASE   )
            plt.xlabel(__SCREAMING_SNAKE_CASE   )
            plt.ylabel(__SCREAMING_SNAKE_CASE   )
            plt.legend()

            if self.args.figure_png_file is not None:
                   plt.savefig(self.args.figure_png_file   )
            else:
                   plt.show()






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    HfArgumentParser(_UpperCAmelCase       )
       _UpperCamelCase														=    parser.parse_args_into_dataclasses()[0]
       _UpperCamelCase														=    Plot(args=_UpperCAmelCase       )
       plot.plot()


if __name__ == "__main__":
       main()











from math import floor, sqrt





def 	__snake_case      (     _UpperCAmelCase							):
    __a					=							0.0
    __a					=							1.0
    __a					=							int(sqrt(_UpperCAmelCase							)							)
    __a					=							root
    __a					=							0
    while integer_part != 2 * root:
        __a					=							denominator * integer_part - numerator
        __a					=							(n - numerator**2) / denominator
        __a					=							int((root + numerator) / denominator							)
        period += 1
    return period





def 	__snake_case      (     _UpperCAmelCase = 10000							):
    __a					=							0
    for i in range(2  ,					n + 1							):
        __a					=							sqrt(_UpperCAmelCase							)
        if sr - floor(_UpperCAmelCase							) != 0 and continuous_fraction_period(_UpperCAmelCase							) % 2 == 1:
            count_odd_periods += 1
    return count_odd_periods


if __name__ == "__main__":
 print(f'{solution(int(input().strip()))}')





