

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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any=None	,     SCREAMING_SNAKE_CASE_		:    List[str]=None			):



     '''simple docstring'''





     return field(default_factory=lambda: default	,     metadata=SCREAMING_SNAKE_CASE_			)


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



def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''simple docstring'''





     try:
          int(SCREAMING_SNAKE_CASE_			)
          return True
     except ValueError:
          return False

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     try:
          float(SCREAMING_SNAKE_CASE_			)
          return True
     except ValueError:
          return False


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a):
         lowercase__    :   List[str]      =       args
         lowercase__    :   Dict      =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

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





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   Optional[Any]      =       plt.subplots()
         lowercase__    :   Optional[int]      =       'Time usage' if self.args.is_time else 'Memory usage'
         lowercase__    :   Optional[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()):
              lowercase__    :   Any      =       sorted(set(self.result_dict[model_name]['bsz']))
              lowercase__    :   Any      =       sorted(set(self.result_dict[model_name]['seq_len']))
              lowercase__    :   Dict      =       self.result_dict[model_name]['result']

              ((lowercase__)      , (lowercase__))    :   Dict      =       (
                  (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
              )

              lowercase__    :   str      =       (
                  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__    :   Tuple      =       np.asarray(
                            [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,   dtype=a						,   )
                   else:
                        lowercase__    :   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						,   )

                   ((lowercase__)      , (lowercase__))    :   List[Any]      =       (
                       ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                   )

                   lowercase__    :   List[str]      =       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."""

         lowercase__    :   List[Any]      =       title_str[:-4]
         lowercase__    :   Union[str, Any]      =       '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 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   str      =       HfArgumentParser(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Dict      =       parser.parse_args_into_dataclasses()[0]
     lowercase__    :   Optional[int]      =       Plot(args=SCREAMING_SNAKE_CASE_			)
     plot.plot()


if __name__ == "__main__":
       main()




import math

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       []
     lowercase__    :   str      =       2
     lowercase__    :   Optional[Any]      =       int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)  # Size of every segment
     lowercase__    :   Dict      =       [True] * (end + 1)
     lowercase__    :   Union[str, Any]      =       []

     while start <= end:
          if temp[start] is True:
               in_prime.append(SCREAMING_SNAKE_CASE_			)
               for i in range(start * start	,     end + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   int      =       False
          start += 1
     prime += in_prime

     lowercase__    :   Optional[int]      =       end + 1
     lowercase__    :   List[str]      =       min(2 * end	,     SCREAMING_SNAKE_CASE_			)

     while low <= n:
          lowercase__    :   str      =       [True] * (high - low + 1)
          for each in in_prime:
               lowercase__    :   str      =       math.floor(low / each			) * each
               if t < low:
                    t += each

               for j in range(SCREAMING_SNAKE_CASE_	,     high + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[Any]      =       False

          for j in range(len(SCREAMING_SNAKE_CASE_			)			):
               if temp[j] is True:
                    prime.append(j + low			)

          lowercase__    :   Optional[Any]      =       high + 1
          lowercase__    :   Optional[int]      =       min(high + end	,     SCREAMING_SNAKE_CASE_			)

     return prime

print(sieve(10**6))

