code: 
  
  
  
  
  
  
  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 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase=None      ,					__lowerCAmelCase=None    )				->			Any:
       return field(default_factory=lambda: default      ,					metadata=__lowerCAmelCase    )
  
  
  
  
  
  @dataclass
  class     __a						:
   _lowerCAmelCase : str 				=				field(
       metadata={'''help''': '''The csv file to plot.'''} ,     )
   _lowerCAmelCase : bool 				=				field(
       default=A__ ,     metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} ,     )
   _lowerCAmelCase : bool 				=				field(
       default=A__ ,     metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} ,     )
   _lowerCAmelCase : bool 				=				field(
       default=A__ ,     metadata={'''help''': '''Disable logarithmic scale when plotting'''} ,     )
   _lowerCAmelCase : bool 				=				field(
       default=A__ ,     metadata={
           '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
       } ,     )
   _lowerCAmelCase : Optional[str] 				=				field(
       default=A__ ,     metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} ,     )
   _lowerCAmelCase : Optional[List[str]] 				=				list_field(
       default=A__ ,     metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			List[str]:
       try:
            int(__lowerCAmelCase    )
            return True
       except ValueError:
            return False
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Tuple:
       try:
            float(__lowerCAmelCase    )
            return True
       except ValueError:
            return False
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						args
        UpperCamelCase__    :     Dict        =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)
  
        with open(self.args.csv_file							,     newline=""		) as csv_file:
             UpperCamelCase__    :     Union[str, Any]        =						csv.DictReader(SCREAMING_SNAKE_CASE		)
             for row in reader:
                  UpperCamelCase__    :     str        =						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__    :     List[str]        =						int(row["result"]		)
                  elif can_convert_to_float(row["result"]		):
                       # value is not None
                       UpperCamelCase__    :     Optional[int]        =						float(row["result"]		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Optional[Any]        =						plt.subplots()
        UpperCamelCase__    :     Optional[Any]        =						"Time usage" if self.args.is_time else "Memory usage"
        UpperCamelCase__    :     Dict        =						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__    :     Dict        =						sorted(set(self.result_dict[model_name]["bsz"]		)		)
             UpperCamelCase__    :     str        =						sorted(set(self.result_dict[model_name]["seq_len"]		)		)
             UpperCamelCase__    :     List[Any]        =						self.result_dict[model_name]["result"]
  
             ((UpperCamelCase__)       ,  (UpperCamelCase__))    :     str        =						(
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )
  
             UpperCamelCase__    :     Optional[Any]        =						(
                 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__    :     List[str]        =						np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,     dtype=SCREAMING_SNAKE_CASE							,     )
                  else:
                       UpperCamelCase__    :     Dict        =						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__))    :     Union[str, Any]        =						(
                      ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                  )
  
                  UpperCamelCase__    :     Union[str, Any]        =						np.asarray(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)[: len(SCREAMING_SNAKE_CASE		)]
                  plt.scatter(
                      SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'		)
                  plt.plot(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     "--"		)
  
             title_str += F' {label_model_name} vs.'
  
        UpperCamelCase__    :     Optional[Any]        =						title_str[:-4]
        UpperCamelCase__    :     int        =						"Time in s" if self.args.is_time else "Memory in MB"
  
        # plot
        plt.title(SCREAMING_SNAKE_CASE		)
        plt.xlabel(SCREAMING_SNAKE_CASE		)
        plt.ylabel(SCREAMING_SNAKE_CASE		)
        plt.legend()
  
        if self.args.figure_png_file is not None:
             plt.savefig(self.args.figure_png_file		)
        else:
             plt.show()
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      )				->			int:
       UpperCamelCase__    :     List[str]        =						HfArgumentParser(__lowerCAmelCase    )
       UpperCamelCase__    :     Union[str, Any]        =						parser.parse_args_into_dataclasses()[0]
       UpperCamelCase__    :     Union[str, Any]        =						Plot(args=__lowerCAmelCase    )
       plot.plot()
  
  
  if __name__ == "__main__":
   main()
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     str							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Any        =						data
        UpperCamelCase__    :     Node | None        =						None
  
  
  
  
  
  
  
   def __str__(	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return F'{self.data}'
  
  
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Node | None        =						None
        UpperCamelCase__    :     Node | None        =						None
  
  
  
  
  
  
  
   def __iter__(	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						self.front
        while node:
             yield node.data
             UpperCamelCase__    :     Any        =						node.next
  
  
  
  
  
  
  
   def __len__(	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(tuple(iter(self		)		)		)
  
  
  
  
  
  
  
   def __str__(	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return " <- ".join(str(SCREAMING_SNAKE_CASE		) for item in self		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(self		) == 0
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						Node(SCREAMING_SNAKE_CASE		)
        if self.is_empty():
             UpperCamelCase__    :     int        =						node
        else:
             assert isinstance(self.rear							,     SCREAMING_SNAKE_CASE		)
             UpperCamelCase__    :     Dict        =						node
             UpperCamelCase__    :     str        =						node
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if self.is_empty():
             raise IndexError("dequeue from empty queue"		)
        assert isinstance(self.front							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						self.front
        UpperCamelCase__    :     List[str]        =						self.front.next
        if self.front is None:
             UpperCamelCase__    :     List[Any]        =						None
        return node.data
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						None
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()