



'''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 	a_					(							_lowerCAmelCase=None	,_lowerCAmelCase=None	)				->				Dict:
  return field(default_factory=lambda: default	,metadata=_lowerCAmelCase	)






@dataclass
class  lowerCamelCase_   :
  """simple docstring"""




  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 	a_					(							_lowerCAmelCase	)				->				Dict:



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

def 	a_					(							_lowerCAmelCase	)				->				Tuple:



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






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     str      ,     _a				:     List[str]       ) -> str:
    __lowerCamelCase	:	Optional[int]   	=			args
    __lowerCamelCase	:	int   	=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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




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

      ((__lowerCamelCase)	,(__lowerCamelCase))	:	Optional[int]   	=			(
          (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
      )

      __lowerCamelCase	:	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:
          __lowerCamelCase	:	List[str]   	=			np.asarray(
              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,     dtype=lowerCAmelCase__      ,     )
        else:
          __lowerCamelCase	:	Dict   	=			np.asarray(
              [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,     dtype=np.floataa      ,     )

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

        __lowerCamelCase	:	Any   	=			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.'

    __lowerCamelCase	:	Optional[Any]   	=			title_str[:-4]
    __lowerCamelCase	:	List[Any]   	=			'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 	a_					(							)				->				str:
  __lowerCamelCase	:	Any   	=			HfArgumentParser(_lowerCAmelCase	)
  __lowerCamelCase	:	Optional[Any]   	=			parser.parse_args_into_dataclasses()[0]
  __lowerCamelCase	:	Union[str, Any]   	=			Plot(args=_lowerCAmelCase	)
  plot.plot()


if __name__ == "__main__":
   main()










'''simple docstring'''





import random






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     float      ,     _a				:     float       ) -> None:
    __lowerCamelCase	:	Optional[int]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y




  def 						_lowercase					(			self				:     Tuple       ) -> bool:
    return (self.x**2 + self.y**2) <= 1




  @classmethod
  def 						_lowercase					(			cls				:     List[Any]       ) -> Optional[int]:
    return cls(x=random.random()      ,     y=random.random()       )




def 	a_					(							_lowerCAmelCase	)				->				float:
  if number_of_simulations < 1:
    raise ValueError('At least one simulation is necessary to estimate PI.'	)

  __lowerCamelCase	:	Optional[int]   	=			0
  for _ in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			Point.random_unit_square()

    if random_point.is_in_unit_circle():
      number_in_unit_circle += 1

  return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
   # import doctest

   # doctest.testmod()
   from math import pi

   _UpperCamelCase							    =						'Please enter the desired number of Monte Carlo simulations: '
   _UpperCamelCase							    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')






