

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       ):





     '''simple docstring'''




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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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



def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     try:
          int(_A       )
          return True
     except ValueError:
          return False
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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






class  UpperCAmelCase__	:






  """simple docstring"""


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

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







  def    lowercase_	(							self		:	Optional[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					plt.subplots()
       SCREAMING_SNAKE_CASE__					=					'''Time usage''' if self.args.is_time else '''Memory usage'''
       SCREAMING_SNAKE_CASE__					=					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()      ):
            SCREAMING_SNAKE_CASE__					=					sorted(set(self.result_dict[model_name]['''bsz''']      )      )
            SCREAMING_SNAKE_CASE__					=					sorted(set(self.result_dict[model_name]['''seq_len''']      )      )
            SCREAMING_SNAKE_CASE__					=					self.result_dict[model_name]['''result''']

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

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

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

                 SCREAMING_SNAKE_CASE__					=					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.'''

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





     '''simple docstring'''




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


if __name__ == "__main__":
       main()





import random
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					a[left_index]
     SCREAMING_SNAKE_CASE__					=					left_index + 1
     for j in range(left_index + 1   ,	_A       ):
          if a[j] < pivot:
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i], a[j]
               i += 1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i - 1], a[left_index]
     return i - 1
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     if left < right:
          SCREAMING_SNAKE_CASE__					=					random.randint(_A   ,	right - 1       )
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
              a[left],
              a[pivot],
          )  # switches the pivot with the left most bound
          SCREAMING_SNAKE_CASE__					=					partition(_A   ,	_A   ,	_A       )
          quick_sort_random(
              _A   ,	_A   ,	_A       )  # recursive quicksort to the left of the pivot point
          quick_sort_random(
              _A   ,	pivot_index + 1   ,	_A       )  # recursive quicksort to the right of the pivot point
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					input('''Enter numbers separated by a comma:\n'''       ).strip()
     SCREAMING_SNAKE_CASE__					=					[int(_A       ) for item in user_input.split(''','''       )]

     quick_sort_random(_A   ,	0   ,	len(_A       )       )

     print(_A       )


if __name__ == "__main__":
       main()


