code: 
  
  
  '''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=None						,				A=None ) -> List[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return field(default_factory=lambda: default						,				metadata=lowercase__ )
  @dataclass
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  str						      =							field(
            metadata={"""help""": """The csv file to plot."""}						,  )
        lowerCAmelCase__      :  bool						      =							field(
            default=__UpperCAmelCase						,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}						,  )
        lowerCAmelCase__      :  bool						      =							field(
            default=__UpperCAmelCase						,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}						,  )
        lowerCAmelCase__      :  bool						      =							field(
            default=__UpperCAmelCase						,  metadata={"""help""": """Disable logarithmic scale when plotting"""}						,  )
        lowerCAmelCase__      :  bool						      =							field(
            default=__UpperCAmelCase						,  metadata={
                """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
            }						,  )
        lowerCAmelCase__      :  Optional[str]						      =							field(
            default=__UpperCAmelCase						,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}						,  )
        lowerCAmelCase__      :  Optional[List[str]]						      =							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 ) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         try:
                int(lowercase__ )
                return True
         except ValueError:
                return False
  
  def _SCREAMING_SNAKE_CASE   (A ) -> str:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         try:
                float(lowercase__ )
                return True
         except ValueError:
                return False
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					List[Any]     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				args
               lowercase__       =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)
  
               with open(self.args.csv_file     ,		newline=''''''				) as csv_file:
                      lowercase__       =				csv.DictReader(lowerCamelCase__				)
                      for row in reader:
                             lowercase__       =				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
                                    lowercase__       =				int(row['''result''']				)
                             elif can_convert_to_float(row['''result''']				):
                                    # value is not None
                                    lowercase__       =				float(row['''result''']				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				plt.subplots()
               lowercase__       =				'''Time usage''' if self.args.is_time else '''Memory usage'''
               lowercase__       =				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()				):
                      lowercase__       =				sorted(set(self.result_dict[model_name]['''bsz''']				)				)
                      lowercase__       =				sorted(set(self.result_dict[model_name]['''seq_len''']				)				)
                      lowercase__       =				self.result_dict[model_name]['''result''']
  
                      ((lowercase__)    ,(lowercase__))       =				(
                          (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                      )
  
                      lowercase__       =				(
                          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:
                                    lowercase__       =				np.asarray(
                                        [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,		dtype=lowerCamelCase__     ,		)
                             else:
                                    lowercase__       =				np.asarray(
                                        [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,		dtype=np.floataa     ,		)
  
                             ((lowercase__)    ,(lowercase__))       =				(
                                 ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                             )
  
                             lowercase__       =				np.asarray(lowerCamelCase__     ,		lowerCamelCase__				)[: len(lowerCamelCase__				)]
                             plt.scatter(
                                 lowerCamelCase__     ,		lowerCamelCase__     ,		label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"				)
                             plt.plot(lowerCamelCase__     ,		lowerCamelCase__     ,		'''--'''				)
  
                      title_str += f" {label_model_name} vs."
  
               lowercase__       =				title_str[:-4]
               lowercase__       =				'''Time in s''' if self.args.is_time else '''Memory in MB'''
  
               # plot
               plt.title(lowerCamelCase__				)
               plt.xlabel(lowerCamelCase__				)
               plt.ylabel(lowerCamelCase__				)
               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   () -> int:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				HfArgumentParser(lowercase__ )
         lowercase__       =				parser.parse_args_into_dataclasses()[0]
         lowercase__       =				Plot(args=lowercase__ )
         plot.plot()
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  from collections import Counter
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				[True] * n
         lowercase__       =				False
         lowercase__       =				False
         lowercase__       =				True
  
         for i in range(3						,				int(n**0.5 + 1 )						,				2 ):
                lowercase__       =				i * 2
                while index < n:
                       lowercase__       =				False
                       lowercase__       =				index + i
  
         lowercase__       =				[2]
  
         for i in range(3						,				A						,				2 ):
                if is_prime[i]:
                       primes.append(A )
  
         return primes
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list[list[int]]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				str(A )
         lowercase__       =				[]
         lowercase__       =				['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']
  
         for duplicate in Counter(A ) - Counter(set(A ) ):
                lowercase__       =				[int(number_str.replace(A						,				A ) ) for digit in digits]
                replacements.append(A )
  
         return replacements
  
  def _SCREAMING_SNAKE_CASE   (A = 8 ) -> int:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				set()
  
         # Filter primes with less than 3 replaceable digits
         lowercase__       =				{
             x for x in set(prime_sieve(1_000_000 ) ) if len(str(A ) ) - len(set(str(A ) ) ) >= 3
         }
  
         for prime in primes:
                if prime in numbers_checked:
                       continue
  
                lowercase__       =				digit_replacements(A )
  
                for family in replacements:
                       numbers_checked.update(A )
                       lowercase__       =				primes.intersection(A )
  
                       if len(A ) != family_length:
                              continue
  
                       return min(A )
  
         return -1
  
  
  if __name__ == "__main__":
   print(solution())
  
  
  
  
  