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   _A (	SCREAMING_SNAKE_CASE  :     Dict=None , SCREAMING_SNAKE_CASE  :     str=None		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
  @dataclass
  class        __lowerCAmelCase :
        _lowercase       :      str        =    field(
            metadata={"""help""": """The csv file to plot."""}		,       )
        _lowercase       :      bool        =    field(
            default=lowerCAmelCase__		,       metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,       )
        _lowercase       :      bool        =    field(
            default=lowerCAmelCase__		,       metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,       )
        _lowercase       :      bool        =    field(
            default=lowerCAmelCase__		,       metadata={"""help""": """Disable logarithmic scale when plotting"""}		,       )
        _lowercase       :      bool        =    field(
            default=lowerCAmelCase__		,       metadata={
                """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
            }		,       )
        _lowercase       :      Optional[str]        =    field(
            default=lowerCAmelCase__		,       metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,       )
        _lowercase       :      Optional[List[str]]        =    list_field(
            default=lowerCAmelCase__		,       metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""})
  def   _A (	SCREAMING_SNAKE_CASE  :     Dict		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    try:
      int(SCREAMING_SNAKE_CASE		)
      return True
    except ValueError:
      return False
  def   _A (	SCREAMING_SNAKE_CASE  :     Any		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    try:
      float(SCREAMING_SNAKE_CASE		)
      return True
    except ValueError:
      return False
  
  
  
  
  
  
  
  class        __lowerCAmelCase :
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Union[str, Any]            =args
          a__			:      List[Any]            =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)
  
          with open(self.args.csv_file	,   newline=""							) as csv_file:
            a__			:      Union[str, Any]            =csv.DictReader(lowerCAmelCase_							)
            for row in reader:
              a__			:      Union[str, Any]            =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
                a__			:      int            =int(row["result"]							)
              elif can_convert_to_float(row["result"]							):
                # value is not None
                a__			:      Union[str, Any]            =float(row["result"]							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__      ,							a__			:      str            =plt.subplots()
          a__			:      List[Any]            ="Time usage" if self.args.is_time else "Memory usage"
          a__			:      Any            =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()							):
            a__			:      Tuple            =sorted(set(self.result_dict[model_name]["bsz"]							)							)
            a__			:      Tuple            =sorted(set(self.result_dict[model_name]["seq_len"]							)							)
            a__			:      int            =self.result_dict[model_name]["result"]
  
            ((a__)      ,							(a__))			:      Any            =(
                (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
            )
  
            a__			:      List[str]            =(
                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:
                a__			:      List[str]            =np.asarray(
                    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,   dtype=lowerCAmelCase_	,   )
              else:
                a__			:      int            =np.asarray(
                    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,   dtype=np.floataa	,   )
  
              ((a__)      ,							(a__))			:      Optional[Any]            =(
                  ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
              )
  
              a__			:      Optional[Any]            =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.'''
  
          a__			:      List[Any]            =title_str[:-4]
          a__			:      Dict            ="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   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      int            =HfArgumentParser(SCREAMING_SNAKE_CASE		)
    a__			:      List[Any]            =parser.parse_args_into_dataclasses()[0]
    a__			:      Any            =Plot(args=SCREAMING_SNAKE_CASE		)
    plot.plot()
  
  
  if __name__ == "__main__":
       main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  UpperCAmelCase			:			int	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class        __lowerCAmelCase :
        _lowercase       :      str
        _lowercase       :      str
        _lowercase       :      Optional[str]        =    None
        _lowercase       :      Optional[str]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)	,   indent=2							) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        __lowerCAmelCase :
        _lowercase       :      List[int]
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[Union[int, float]]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)							) + "\n"
  
  
  
  
  
  
  
  class        __lowerCAmelCase :
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          if len(self.get_labels()							) > 1:
            a__			:      Dict            =self.get_labels()[int(example.label							)]
          return example
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          with open(lowerCAmelCase__	,   "r"	,   encoding="utf-8-sig"							) as f:
            return list(csv.reader(lowerCAmelCase__	,   delimiter="\t"	,   quotechar=lowerCAmelCase__							)							)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__="classification"	,   lowerCAmelCase__=False							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =[] if labels is None else labels
          a__			:      List[Any]            =[] if examples is None else examples
          a__			:      List[str]            =mode
          a__			:      Tuple            =verbose
  
  
  
  
  
  
  
        def __len__(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return len(self.examples							)
  
  
  
  
  
  
  
        def __getitem__(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							):
            return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]							)
          return self.examples[idx]
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =cls(**lowerCAmelCase__							)
          processor.add_examples_from_csv(
              lowerCAmelCase__	,   split_name=lowerCAmelCase__	,   column_label=lowerCAmelCase__	,   column_text=lowerCAmelCase__	,   column_id=lowerCAmelCase__	,   skip_first_row=lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__	,   )
          return processor
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   **lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =cls(**lowerCAmelCase__							)
          processor.add_examples(lowerCAmelCase__	,   labels=lowerCAmelCase__							)
          return processor
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   )     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =self._read_tsv(lowerCAmelCase__							)
          if skip_first_row:
            a__			:      Tuple            =lines[1:]
          a__			:      List[Any]            =[]
          a__			:      int            =[]
          a__			:      str            =[]
          for i, line in enumerate(lowerCAmelCase__							):
            texts.append(line[column_text]							)
            labels.append(line[column_label]							)
            if column_id is not None:
              ids.append(line[column_id]							)
            else:
              a__			:      List[str]            =F'''{split_name}-{i}''' if split_name else str(lowerCAmelCase__							)
              ids.append(lowerCAmelCase__							)
  
          return self.add_examples(
              lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if labels is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(
                F'''Text and labels have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(F'''Text and ids have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is None:
            a__			:      Dict            =[None] * len(lowerCAmelCase__							)
          if labels is None:
            a__			:      str            =[None] * len(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =[]
          a__			:      Optional[Any]            =set()
          for text_or_text_and_label, label, guid in zip(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							):
            if isinstance(lowerCAmelCase__	,   (tuple, list)							) and label is None:
              a__      ,							a__			:      int            =text_or_text_and_label
            else:
              a__			:      List[str]            =text_or_text_and_label
            added_labels.add(lowerCAmelCase__							)
            examples.append(InputExample(guid=lowerCAmelCase__	,   text_a=lowerCAmelCase__	,   text_b=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          # Update examples
          if overwrite_examples:
            a__			:      Dict            =examples
          else:
            self.examples.extend(lowerCAmelCase__							)
  
          # Update labels
          if overwrite_labels:
            a__			:      Any            =list(lowerCAmelCase__							)
          else:
            a__			:      List[Any]            =list(set(self.labels							).union(lowerCAmelCase__							)							)
  
          return self.examples
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if max_length is None:
            a__			:      int            =tokenizer.max_len
  
          a__			:      int            ={label: i for i, label in enumerate(self.labels							)}
  
          a__			:      Union[str, Any]            =[]
          for ex_index, example in enumerate(self.examples							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Tokenizing example {ex_index}'''							)
  
            a__			:      Dict            =tokenizer.encode(
                example.text_a	,   add_special_tokens=lowerCAmelCase__	,   max_length=min(lowerCAmelCase__	,   tokenizer.max_len							)	,   )
            all_input_ids.append(lowerCAmelCase__							)
  
          a__			:      List[Any]            =max(len(lowerCAmelCase__							) for input_ids in all_input_ids							)
  
          a__			:      Dict            =[]
          for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__	,   self.examples							)							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Writing example {ex_index}/{len(self.examples							)}'''							)
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            a__			:      Optional[Any]            =[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__							)
  
            # Zero-pad up to the sequence length.
            a__			:      List[str]            =batch_length - len(lowerCAmelCase__							)
            if pad_on_left:
              a__			:      Tuple            =([pad_token] * padding_length) + input_ids
              a__			:      Dict            =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
              a__			:      List[str]            =input_ids + ([pad_token] * padding_length)
              a__			:      Any            =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
  
            if self.mode == "classification":
              a__			:      Dict            =label_map[example.label]
            elif self.mode == "regression":
              a__			:      Any            =float(example.label							)
            else:
              raise ValueError(self.mode							)
  
            if ex_index < 5 and self.verbose:
              logger.info("*** Example ***"							)
              logger.info(F'''guid: {example.guid}'''							)
              logger.info(F'''input_ids: {" ".join([str(lowerCAmelCase__							) for x in input_ids]							)}'''							)
              logger.info(F'''attention_mask: {" ".join([str(lowerCAmelCase__							) for x in attention_mask]							)}'''							)
              logger.info(F'''label: {example.label} (id = {label})'''							)
  
            features.append(InputFeatures(input_ids=lowerCAmelCase__	,   attention_mask=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          if return_tensors is None:
            return features
          elif return_tensors == "tf":
            if not is_tf_available():
              raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"							)
            import tensorflow as tf
  
            def gen():
              for ex in features:
                yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
            a__			:      Any            =tf.data.Dataset.from_generator(
                lowerCAmelCase__	,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,   ({"input_ids": tf.TensorShape([None]							), "attention_mask": tf.TensorShape([None]							)}, tf.TensorShape([]							))	,   )
            return dataset
          elif return_tensors == "pt":
            if not is_torch_available():
              raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"							)
            import torch
            from torch.utils.data import TensorDataset
  
            a__			:      str            =torch.tensor([f.input_ids for f in features]	,   dtype=torch.long							)
            a__			:      str            =torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long							)
            if self.mode == "classification":
              a__			:      Dict            =torch.tensor([f.label for f in features]	,   dtype=torch.long							)
            elif self.mode == "regression":
              a__			:      Tuple            =torch.tensor([f.label for f in features]	,   dtype=torch.float							)
  
            a__			:      List[str]            =TensorDataset(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
            return dataset
          else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)
  
  
  
  
  
  
  