






'''simple docstring'''






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  (				A  :       int=None  ,		A  :       Union[str, Any]=None  )    -> List[Any]:







   """simple docstring"""
   return field(default_factory=lambda: default  ,		metadata=A  )




@dataclass
class a_    :
   _snake_case        =							field(
       metadata={"""help""": """The csv file to plot."""}		,      )
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,      )
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,      )
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Disable logarithmic scale when plotting"""}		,      )
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={
           """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
       }		,      )
   _snake_case        =							field(
       default=UpperCamelCase_		,      metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,      )
   _snake_case        =							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  (				A  :       Any  )    -> Any:







   """simple docstring"""
   try:
      int(A  )
      return True
   except ValueError:
      return False




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







   """simple docstring"""
   try:
      float(A  )
      return True
   except ValueError:
      return False




class a_    :







   def __init__(self					,   __a)			->      List[str]:
      """simple docstring"""






      __snake_case				:      str     							= args
      __snake_case				:      Optional[int]     							= defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

      with open(self.args.csv_file					,   newline='') as csv_file:
         __snake_case				:      Any     							= csv.DictReader(__a)
         for row in reader:
            __snake_case				:      List[Any]     							= 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
               __snake_case				:      str     							= int(row['result'])
            elif can_convert_to_float(row['result']):
               # value is not None
               __snake_case				:      Tuple     							= float(row['result'])







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case							,__snake_case				:      str     							= plt.subplots()
      __snake_case				:      Union[str, Any]     							= 'Time usage' if self.args.is_time else 'Memory usage'
      __snake_case				:      Union[str, Any]     							= 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()):
         __snake_case				:      Dict     							= sorted(set(self.result_dict[model_name]['bsz']))
         __snake_case				:      Union[str, Any]     							= sorted(set(self.result_dict[model_name]['seq_len']))
         __snake_case				:      str     							= self.result_dict[model_name]['result']

         ((__snake_case)							,(__snake_case))				:      int     							= (
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         __snake_case				:      Optional[int]     							= (
             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:
               __snake_case				:      str     							= np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,   dtype=__a					,   )
            else:
               __snake_case				:      Optional[Any]     							= np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,   dtype=np.floataa					,   )

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

            __snake_case				:      Tuple     							= np.asarray(__a					,   __a)[: len(__a)]
            plt.scatter(
                __a					,   __a					,   label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}""")
            plt.plot(__a					,   __a					,   '--')

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

      __snake_case				:      List[Any]     							= title_str[:-4]
      __snake_case				:      List[str]     							= 'Time in s' if self.args.is_time else 'Memory in MB'

      # plot
      plt.title(__a)
      plt.xlabel(__a)
      plt.ylabel(__a)
      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  (				)    -> List[str]:







   """simple docstring"""
   __snake_case				:      int     							= HfArgumentParser(A  )
   __snake_case				:      Optional[Any]     							= parser.parse_args_into_dataclasses()[0]
   __snake_case				:      Optional[Any]     							= Plot(args=A  )
   plot.plot()


if __name__ == "__main__":
      main()







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   __snake_case				:      str     							= 1
   for i in range(1  ,		num + 1  ):
      fact *= i
   return fact




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= 0
   while number > 0:
      __snake_case				:      Dict     							= number % 10
      sum_of_digits += last_digit
      __snake_case				:      Union[str, Any]     							= number // 10  # Removing the last_digit from the given number
   return sum_of_digits




def        _SCREAMING_SNAKE_CASE  (				A  :       int = 1_00  )    -> int:







   """simple docstring"""
   __snake_case				:      List[Any]     							= factorial(A  )
   __snake_case				:      Dict     							= split_and_add(A  )
   return result


if __name__ == "__main__":
      print(solution(int(input('''Enter the Number: ''').strip())))