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__    :     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__)    :     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__)    :     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: 
  
  
  
  
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class     __a						(					A__ ,     unittest.TestCase ):
   _lowerCAmelCase : str 				=				MPNetTokenizer
   _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
   _lowerCAmelCase : List[Any] 				=				True
   _lowerCAmelCase : Union[str, Any] 				=				True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().setUp()
  
        UpperCamelCase__    :     List[Any]        =						[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
        with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
        UpperCamelCase__    :     List[str]        =						"unwanted, running"
        return input_text, output_text
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)
  
        UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
        self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)
  
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]