
"""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  _UpperCamelCase	(						_A=None					,      _A=None      )    ->  Dict:





    """simple docstring"""

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




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







def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

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




class 		a_						:







    def __init__(			self			:       Dict     ,					__UpperCamelCase			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






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

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







    def      _snake_case					(			self			:       int     )		->Dict:






        '''simple docstring'''






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

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

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

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

                _UpperCAmelCase          =   np.asarray(__UpperCamelCase     ,					__UpperCamelCase     )[: len(__UpperCamelCase     )]
                plt.scatter(
                    __UpperCamelCase     ,					__UpperCamelCase     ,					label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""     )
                plt.plot(__UpperCamelCase     ,					__UpperCamelCase     ,					"""--"""     )

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

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

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

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







def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   HfArgumentParser(__lowercase      )
    _UpperCAmelCase          =   parser.parse_args_into_dataclasses()[0]
    _UpperCAmelCase          =   Plot(args=__lowercase      )
    plot.plot()


if __name__ == "__main__":
 main()

"""simple docstring"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

    _UpperCAmelCase          =   x
    _UpperCAmelCase          =   y
    for step in range(_A      ):  # noqa: B007
        _UpperCAmelCase          =   a * a - b * b + x
        _UpperCAmelCase          =   2 * a * b + y
        _UpperCAmelCase          =   a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(_A					,      1					,      1      )      )


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

    _UpperCAmelCase          =   Image.new("""RGB"""					,      (image_width, image_height)      )
    _UpperCAmelCase          =   img.load()

    # loop through the image-coordinates
    for image_x in range(_A      ):
        for image_y in range(_A      ):
            # determine the figure-coordinates based on the image-coordinates
            _UpperCAmelCase          =   figure_width / image_width * image_height
            _UpperCAmelCase          =   figure_center_x + (image_x / image_width - 0.5) * figure_width
            _UpperCAmelCase          =   figure_center_y + (image_y / image_height - 0.5) * figure_height

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                _UpperCAmelCase          =   get_color_coded_rgb(_A      )
            else:
                _UpperCAmelCase          =   get_black_and_white_rgb(_A      )

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a					:	List[str]							=		get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()