





'''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(SCREAMING_SNAKE_CASE_					:   str=None						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]=None      ):



    '''simple docstring'''





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

@dataclass
class 				lowerCAmelCase_						:
 __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 		__a(SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





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

class 				lowerCAmelCase_						:




 def __init__(		self     ,       _lowerCAmelCase						)      ->							str:
     _lowerCAmelCase		     = args
     _lowerCAmelCase		     = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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





 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = plt.subplots()
     _lowerCAmelCase		     = "Time usage" if self.args.is_time else "Memory usage"
     _lowerCAmelCase		     = 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		     = sorted(set(self.result_dict[model_name]["bsz"]						)						)
         _lowerCAmelCase		     = sorted(set(self.result_dict[model_name]["seq_len"]						)						)
         _lowerCAmelCase		     = self.result_dict[model_name]["result"]

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

         _lowerCAmelCase		     = (
             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		     = np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,       dtype=lowerCAmelCase__     ,       )
             else:
                 _lowerCAmelCase		     = 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))		     = (
                 ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
             )

             _lowerCAmelCase		     = 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		     = title_str[:-4]
     _lowerCAmelCase		     = "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():



    '''simple docstring'''





    _lowerCAmelCase		     = HfArgumentParser(SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = parser.parse_args_into_dataclasses()[0]
    _lowerCAmelCase		     = Plot(args=SCREAMING_SNAKE_CASE_      )
    plot.plot()


if __name__ == "__main__":
       main()






'''simple docstring'''


_SCREAMING_SNAKE_CASE			=							{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}


def 		__a(SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return [alphabet[char] for char in key.upper()]


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = ""
    _lowerCAmelCase		     = 0
    _lowerCAmelCase		     = generate_table(SCREAMING_SNAKE_CASE_      )
    for char in words.upper():
        cipher += get_opponent(table[count]						,    SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = (count + 1) % len(SCREAMING_SNAKE_CASE_      )
    return cipher


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return encrypt(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = 0 if char in table[0] else 1
    _lowerCAmelCase		     = table[row].index(SCREAMING_SNAKE_CASE_      )
    return row, col


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase     ,     _lowerCAmelCase		     = get_position(SCREAMING_SNAKE_CASE_						,    char.upper()      )
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
       import doctest

       doctest.testmod()  # Fist ensure that all our tests are passing...
       _SCREAMING_SNAKE_CASE			=							input("Enter key: ").strip()
       _SCREAMING_SNAKE_CASE			=							input("Enter text to encrypt: ").strip()
       _SCREAMING_SNAKE_CASE			=							encrypt(key, text)

       print(f'''Encrypted: {cipher_text}''')
       print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')


