code: 
  '''simple docstring'''
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  
  import numpy as np
  import torch
  from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  __A          =    logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,						)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()				)}				)
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
  			lowerCamelCase							:							int       		=  field(
  			    default=128		,						metadata={
  			        """help""": (
  			            """The maximum total input sequence length after tokenization. Sequences longer """
  			            """than this will be truncated, sequences shorter will be padded."""
  			        )
  			    }		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)
  
  
  
  
  
  def  _A		(							lowercase__	):
  
  						'''simple docstring'''
  
  
  
  
  
  						lowercase__							 =       default_data_collator(lowercase__	)
  						lowercase__							 =       batch.pop("""pairID"""		,		lowercase__	)
  						return batch
  
  
  
  
  
  def  _A		(							):
  
  						'''simple docstring'''
  
  
  
  
  
  						lowercase__							 =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
  						lowercase__ ,						lowercase__ ,						lowercase__							 =       parser.parse_args_into_dataclasses()
  
  						if (
  						    os.path.exists(training_args.output_dir	)
  						    and os.listdir(training_args.output_dir	)
  						    and training_args.do_train
  						    and not training_args.overwrite_output_dir
  						):
  												raise ValueError(
  												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
  												    """ --overwrite_output_dir to overcome."""	)
  
  						# Setup logging
  						logging.basicConfig(
  						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,		datefmt="""%m/%d/%Y %H:%M:%S"""		,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,		)
  						logger.warning(
  						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""		,		training_args.local_rank		,		training_args.device		,		training_args.n_gpu		,		bool(training_args.local_rank != -1	)		,		training_args.fpaa		,		)
  						# Set the verbosity to info of the Transformers logger (on main process only):
  						if is_main_process(training_args.local_rank	):
  												transformers.utils.logging.set_verbosity_info()
  												transformers.utils.logging.enable_default_handler()
  												transformers.utils.logging.enable_explicit_format()
  						logger.info("""Training/evaluation parameters %s"""		,		lowercase__	)
  
  						# Set seed
  						set_seed(training_args.seed	)
  
  						try:
  												lowercase__							 =       hans_tasks_num_labels[data_args.task_name]
  						except KeyError:
  												raise ValueError("""Task not found: %s""" % (data_args.task_name)	)
  
  						# Load pretrained model and tokenizer
  						#
  						# Distributed training:
  						# The .from_pretrained methods guarantee that only one local process can concurrently
  						# download model & vocab.
  
  						lowercase__							 =       AutoConfig.from_pretrained(
  						    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,		num_labels=lowercase__		,		finetuning_task=data_args.task_name		,		cache_dir=model_args.cache_dir		,		)
  						lowercase__							 =       AutoTokenizer.from_pretrained(
  						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,		cache_dir=model_args.cache_dir		,		)
  						lowercase__							 =       AutoModelForSequenceClassification.from_pretrained(
  						    model_args.model_name_or_path		,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)		,		config=lowercase__		,		cache_dir=model_args.cache_dir		,		)
  
  						# Get datasets
  						lowercase__							 =       (
  						    HansDataset(
  						        data_dir=data_args.data_dir		,		tokenizer=lowercase__		,		task=data_args.task_name		,		max_seq_length=data_args.max_seq_length		,		overwrite_cache=data_args.overwrite_cache		,		)
  						    if training_args.do_train
  						    else None
  						)
  						lowercase__							 =       (
  						    HansDataset(
  						        data_dir=data_args.data_dir		,		tokenizer=lowercase__		,		task=data_args.task_name		,		max_seq_length=data_args.max_seq_length		,		overwrite_cache=data_args.overwrite_cache		,		evaluate=lowercase__		,		)
  						    if training_args.do_eval
  						    else None
  						)
  
  						# Initialize our Trainer
  						lowercase__							 =       Trainer(
  						    model=lowercase__		,		args=lowercase__		,		train_dataset=lowercase__		,		eval_dataset=lowercase__		,		data_collator=lowercase__		,		)
  
  						# Training
  						if training_args.do_train:
  												trainer.train(
  												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
  												trainer.save_model()
  												# For convenience, we also re-save the tokenizer to the same directory,
  												# so that you can share your model easily on huggingface.co/models =)
  												if trainer.is_world_master():
  																		tokenizer.save_pretrained(training_args.output_dir	)
  
      # Evaluation
  						if training_args.do_eval:
  												logger.info("""*** Evaluate ***"""	)
  
  												lowercase__							 =       trainer.predict(lowercase__	)
  												lowercase__							 =       output.predictions
  												lowercase__							 =       np.argmax(lowercase__		,		axis=1	)
  
  												lowercase__							 =       [ex.pairID for ex in eval_dataset]
  												lowercase__							 =       os.path.join(training_args.output_dir		,		"""hans_predictions.txt"""	)
  												lowercase__							 =       eval_dataset.get_labels()
  												if trainer.is_world_master():
  																		with open(lowercase__		,		"""w"""	) as writer:
  																								writer.write("""pairID,gold_label\n"""	)
  																								for pid, pred in zip(lowercase__		,		lowercase__	):
  																														writer.write("""ex""" + str(lowercase__	) + """,""" + label_list[int(lowercase__	)] + """\n"""	)
  
  												trainer._log(output.metrics	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  
  						'''simple docstring'''
  
  
  
  
  
  						main()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  
style_context: 
  '''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  _A		(							lowercase__=None		,		lowercase__=None	):
  						return field(default_factory=lambda: default		,		metadata=lowercase__	)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The csv file to plot."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }		,						)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
  			lowerCamelCase							:							Optional[List[str]]       		=  list_field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}				)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												int(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												float(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       args
  									lowercase__							 =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )
  
  									with open(self.args.csv_file       ,     newline=""""""       ) as csv_file:
  															lowercase__							 =       csv.DictReader(lowerCamelCase__       )
  															for row in reader:
  																					lowercase__							 =       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
  																											lowercase__							 =       int(row["""result"""]       )
  																					elif can_convert_to_float(row["""result"""]       ):
  																											# value is not None
  																											lowercase__							 =       float(row["""result"""]       )
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       plt.subplots()
  									lowercase__							 =       """Time usage""" if self.args.is_time else """Memory usage"""
  									lowercase__							 =       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()       ):
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
  															lowercase__							 =       self.result_dict[model_name]["""result"""]
  
  															((lowercase__) ,						(lowercase__))							 =       (
  															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  															)
  
  															lowercase__							 =       (
  															    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:
  																											lowercase__							 =       np.asarray(
  																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=lowerCamelCase__       ,     )
  																					else:
  																											lowercase__							 =       np.asarray(
  																											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,     dtype=np.floataa       ,     )
  
  																					((lowercase__) ,						(lowercase__))							 =       (
  																					    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
  																					)
  
  																					lowercase__							 =       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.'''
  
  									lowercase__							 =       title_str[:-4]
  									lowercase__							 =       """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		(							):
  						lowercase__							 =       HfArgumentParser(lowercase__	)
  						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
  						lowercase__							 =       Plot(args=lowercase__	)
  						plot.plot()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  