

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__			:	Dict=None    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=None      )							->      Optional[int]:





    '''simple docstring'''






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


@dataclass
class  A						:







    """simple docstring"""





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




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      Tuple:





    '''simple docstring'''






    try:
        int(SCREAMING_SNAKE_CASE__      )
        return True
    except ValueError:
        return False
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      str:





    '''simple docstring'''






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


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       Tuple		)->    Union[str, Any]:





        '''simple docstring'''







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

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


    def 						snake_case__     (      self :       List[str]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   plt.subplots()
        A__          =   "Time usage" if self.args.is_time else "Memory usage"
        A__          =   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()		):
            A__          =   sorted(set(self.result_dict[model_name]['bsz']		)		)
            A__          =   sorted(set(self.result_dict[model_name]['seq_len']		)		)
            A__          =   self.result_dict[model_name]["result"]

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

            A__          =   (
                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:
                    A__          =   np.asarray(
                        [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],dtype=snake_case__,)
                else:
                    A__          =   np.asarray(
                        [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],dtype=np.floataa,)

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

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

            title_str += F' {label_model_name} vs.'

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

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

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




def 						_snake_case(    )							->      Optional[int]:





    '''simple docstring'''






    A__          =   HfArgumentParser(SCREAMING_SNAKE_CASE__      )
    A__          =   parser.parse_args_into_dataclasses()[0]
    A__          =   Plot(args=SCREAMING_SNAKE_CASE__      )
    plot.plot()


if __name__ == "__main__":
     main()





"""simple docstring"""







def 					a__			(				SCREAMING_SNAKE_CASE    :    float				,      SCREAMING_SNAKE_CASE    :    float				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  if principal <= 0:
    raise Exception("Principal borrowed must be > 0"				)
  if rate_per_annum < 0:
    raise Exception("Rate of interest must be >= 0"				)
  if years_to_repay <= 0 or not isinstance(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				):
    raise Exception("Years to repay must be an integer > 0"				)

  # Yearly rate is divided by 12 to get monthly rate
  lowerCAmelCase     :     Tuple								=							rate_per_annum / 1_2

  # Years to repay is multiplied by 12 to get number of payments as payment is monthly
  lowerCAmelCase     :     List[Any]								=							years_to_repay * 1_2

  return (
      principal
      * rate_per_month
      * (1 + rate_per_month) ** number_of_payments
      / ((1 + rate_per_month) ** number_of_payments - 1)
  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





