'''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  __lowerCamelCase       (      _UpperCamelCase  :  Any=None							,    _UpperCamelCase  :  Dict=None					):


      '''simple docstring'''


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







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



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






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


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







class lowerCamelCase							:




  '''simple docstring'''



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

        with open(self.args.csv_file  ,							newline=''''''  ) as csv_file:
              UpperCAmelCase_            =  csv.DictReader(_A  )
              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  lowerCAmelCase__ (      self			:       Optional[int]  )				->Tuple:
        UpperCAmelCase_      ,		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_)      ,		(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=_A  ,							)
                    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_)      ,		(UpperCAmelCase_))            =  (
                        ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                    )

                    UpperCAmelCase_            =  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."""

        UpperCAmelCase_            =  title_str[:-4]
        UpperCAmelCase_            =  '''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  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  HfArgumentParser(UpperCAmelCase__					)
      UpperCAmelCase_            =  parser.parse_args_into_dataclasses()[0]
      UpperCAmelCase_            =  Plot(args=UpperCAmelCase__					)
      plot.plot()


if __name__ == "__main__":
      main()






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

      return [s[i:] + s[:i] for i in range(len(_UpperCamelCase					)					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

      UpperCAmelCase_            =  all_rotations(_UpperCamelCase					)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      UpperCAmelCase_            =  {
          "bwt_string": "".join([word[-1] for word in rotations]					),
          "idx_original_string": rotations.index(_UpperCamelCase					),
      }
      return response



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter bwt_string type must be str.'''					)
      if not bwt_string:
            raise ValueError('''The parameter bwt_string must not be empty.'''					)
      try:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      except ValueError:
            raise TypeError(
                '''The parameter idx_original_string type must be int or passive'''
                ''' of cast to int.'''					)
      if idx_original_string < 0:
            raise ValueError('''The parameter idx_original_string must not be lower than 0.'''					)
      if idx_original_string >= len(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      Dict        =					reverse_bwt(result["bwt_string"], result["idx_original_string"])
      print(
          F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
          F'''we get original string \'{original_string}\''''
      )





