



'''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 _lowercase   (  __A=None							,__A=None       ):



 '''simple docstring'''



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







def _lowercase   (  __A       ):



 '''simple docstring'''



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







def _lowercase   (  __A       ):



 '''simple docstring'''



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



 def __init__(  self		,	lowercase			)  ->							Optional[Any]:
  __UpperCamelCase				      =  args
  __UpperCamelCase				      =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

  with open(self.args.csv_file		,	newline=""""""			) as csv_file:
   __UpperCamelCase				      =  csv.DictReader(lowercase			)
   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			)  ->							Tuple:
  __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=lowercase		,	)
    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(lowercase		,	lowercase			)[: len(lowercase			)]
    plt.scatter(
        lowercase		,	lowercase		,	label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"			)
    plt.plot(lowercase		,	lowercase		,	"""--"""			)

   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(lowercase			)
  plt.xlabel(lowercase			)
  plt.ylabel(lowercase			)
  plt.legend()

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







def _lowercase   (  ):



 '''simple docstring'''



 __UpperCamelCase				      =  HfArgumentParser(lowerCAmelCase__       )
 __UpperCamelCase				      =  parser.parse_args_into_dataclasses()[0]
 __UpperCamelCase				      =  Plot(args=lowerCAmelCase__       )
 plot.plot()


if __name__ == "__main__":
    main()










'''simple docstring'''



a__						:			Any										=			{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}







def _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  str(__A       ).strip()

 # Exceptions
 if not binary_str:
  raise ValueError("""Empty string was passed to the function"""       )
 __UpperCamelCase				      =  binary_str[0] == """-"""
 __UpperCamelCase				      =  binary_str[1:] if is_negative else binary_str
 if not all(char in """01""" for char in binary_str       ):
  raise ValueError("""Non-binary value was passed to the function"""       )

 __UpperCamelCase				      =  (
     """0""" * (4 * (divmod(len(__A       )							,4       )[0] + 1) - len(__A       )) + binary_str
 )

 __UpperCamelCase				      =  []
 for x in range(0							,len(__A       )							,4       ):
  hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]       )
 __UpperCamelCase				      =  """0x""" + """""".join(__A       )

 return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
 from doctest import testmod

 testmod()





