code: 
  
  
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__		:       Dict   							=							logging.getLogger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
       __lowerCamelCase										=     field(
           default=__lowerCamelCase       ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
       __lowerCamelCase										=     field(
           default=__lowerCamelCase       ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
       __lowerCamelCase										=     field(
           default=__lowerCamelCase       ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}       ,			)
  
  
  
  
  
  
  
  @dataclass
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     field(
           metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()		)}		)
       __lowerCamelCase										=     field(
           metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}		)
       __lowerCamelCase										=     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										=     field(
           default=__lowerCamelCase       ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict[str, torch.Tensor]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= default_data_collator(__lowerCamelCase					)
       UpperCAmelCase__			:     Optional[int]										= batch.pop("""pairID"""   ,       __lowerCamelCase					)
       return batch
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> Optional[Any]:
  
       '''simple docstring'''
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
  
       UpperCAmelCase__			:     List[str]										= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			:     Union[str, Any]										= 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"""   ,       __lowerCamelCase					)
  
       # Set seed
       set_seed(training_args.seed					)
  
       try:
            UpperCAmelCase__			:     str										= 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.
  
       UpperCAmelCase__			:     Optional[Any]										= AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,       num_labels=__lowerCamelCase   ,       finetuning_task=data_args.task_name   ,       cache_dir=model_args.cache_dir   ,       )
       UpperCAmelCase__			:     List[Any]										= 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   ,       )
       UpperCAmelCase__			:     int										= AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path   ,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)   ,       config=__lowerCamelCase   ,       cache_dir=model_args.cache_dir   ,       )
  
       # Get datasets
       UpperCAmelCase__			:     List[Any]										= (
           HansDataset(
               data_dir=data_args.data_dir   ,       tokenizer=__lowerCamelCase   ,       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
       )
       UpperCAmelCase__			:     Tuple										= (
           HansDataset(
               data_dir=data_args.data_dir   ,       tokenizer=__lowerCamelCase   ,       task=data_args.task_name   ,       max_seq_length=data_args.max_seq_length   ,       overwrite_cache=data_args.overwrite_cache   ,       evaluate=__lowerCamelCase   ,       )
           if training_args.do_eval
           else None
       )
  
       # Initialize our Trainer
       UpperCAmelCase__			:     int										= Trainer(
           model=__lowerCamelCase   ,       args=__lowerCamelCase   ,       train_dataset=__lowerCamelCase   ,       eval_dataset=__lowerCamelCase   ,       data_collator=__lowerCamelCase   ,       )
  
       # 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 ***"""					)
  
            UpperCAmelCase__			:     Dict										= trainer.predict(__lowerCamelCase					)
            UpperCAmelCase__			:     List[str]										= output.predictions
            UpperCAmelCase__			:     List[str]										= np.argmax(__lowerCamelCase   ,       axis=1					)
  
            UpperCAmelCase__			:     Dict										= [ex.pairID for ex in eval_dataset]
            UpperCAmelCase__			:     Dict										= os.path.join(training_args.output_dir   ,       """hans_predictions.txt"""					)
            UpperCAmelCase__			:     int										= eval_dataset.get_labels()
            if trainer.is_world_master():
                 with open(__lowerCamelCase   ,       """w"""					) as writer:
                      writer.write("""pairID,gold_label\n"""					)
                      for pid, pred in zip(__lowerCamelCase   ,       __lowerCamelCase					):
                           writer.write("""ex""" + str(__lowerCamelCase					) + """,""" + label_list[int(__lowerCamelCase					)] + """\n"""					)
  
            trainer._log(output.metrics					)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       # For xla_spawn (TPUs)
       main()
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
        import torch
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=21  ,	_lowerCAmelCase=[]  ,	_lowerCAmelCase=None		):
            UpperCAmelCase__			:     Union[str, Any]										= model
            UpperCAmelCase__			:     Union[str, Any]										= trace_batch_nums
            UpperCAmelCase__			:     Optional[Any]										= abort_after_batch_num
  
            # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
            UpperCAmelCase__			:     int										= collections.deque([]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     str										= []
            UpperCAmelCase__			:     Optional[int]										= 0
            UpperCAmelCase__			:     int										= 0
            UpperCAmelCase__			:     int										= False
            UpperCAmelCase__			:     List[str]										= """                 """
  
            self.analyse_model()
  
            self.register_forward_hook()
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=None		):
            if frame is not None:
                 self.expand_frame(_lowerCAmelCase		)
            self.frames.append("""\n""".join(self.frame		)		)
            UpperCAmelCase__			:     str										= []  # start a new frame
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            self.frame.append(_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            print("""\n""".join(self.frames		)		)
            UpperCAmelCase__			:     List[Any]										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            print(f"\nDetected inf/nan during batch_number={self.batch_number}"		)
            print(f"Last {len(self.frames		)} forward frames:"		)
            print(f"{'abs min':8} {'abs max':8} metadata"		)
            print("""\n""".join(self.frames		)		)
            print("""\n\n"""		)
            UpperCAmelCase__			:     Any										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # extract the fully qualified module names, to be able to report at run time. e.g.:
            # encoder.block.2.layer.0.SelfAttention.o
            #
            # for shared weights only the first shared module name will be registered
            UpperCAmelCase__			:     str										= {m: name for name, m in self.model.named_modules()}
            # self.longest_module_name = max(len(v) for v in self.module_names.values())
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if torch.is_tensor(_lowerCAmelCase		):
                 self.expand_frame(get_abs_min_max(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
                 if detect_overflow(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      UpperCAmelCase__			:     Optional[int]										= True
            elif var is None:
                 self.expand_frame(f"{'None':>17} {ctx}"		)
            else:
                 self.expand_frame(f"{'not a tensor':>17} {ctx}"		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"		)
            self.expand_frame(f"{'abs min':8} {'abs max':8} metadata"		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)
  
            # params
            for name, p in module.named_parameters(recurse=_lowerCAmelCase		):
                 self.analyse_variable(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # inputs
            if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 for i, x in enumerate(_lowerCAmelCase		):
                      self.analyse_variable(_lowerCAmelCase  ,	f"input[{i}]"		)
            else:
                 self.analyse_variable(_lowerCAmelCase  ,	"""input"""		)
  
            # outputs
            if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 for i, x in enumerate(_lowerCAmelCase		):
                      # possibly a tuple of tuples
                      if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                           for j, y in enumerate(_lowerCAmelCase		):
                                self.analyse_variable(_lowerCAmelCase  ,	f"output[{i}][{j}]"		)
                      else:
                           self.analyse_variable(_lowerCAmelCase  ,	f"output[{i}]"		)
            else:
                 self.analyse_variable(_lowerCAmelCase  ,	"""output"""		)
  
            self.save_frame()
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.model.apply(self._register_forward_hook		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            module.register_forward_hook(self.forward_hook		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            # - input is a tuple of packed inputs (could be non-Tensors)
            # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
            UpperCAmelCase__			:     Union[str, Any]										= False
  
            UpperCAmelCase__			:     List[str]										= True if self.batch_number in self.trace_batch_nums else False
            if trace_mode:
                 self.reset_saved_frames()
  
            if self.total_calls == 0:
                 self.batch_start_frame()
            self.total_calls += 1
  
            # count batch numbers - the very first forward hook of the batch will be called when the
            # batch completes - i.e. it gets called very last - we know this batch has finished
            if module == self.model:
                 self.batch_number += 1
                 UpperCAmelCase__			:     Dict										= True
  
            self.create_frame(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # if last_frame_of_batch:
            #     self.batch_end_frame()
  
            if trace_mode:
                 self.trace_frames()
  
            if last_frame_of_batch:
                 self.batch_start_frame()
  
            if self.detected_overflow and not trace_mode:
                 self.dump_saved_frames()
  
                 # now we can abort, as it's pointless to continue running
                 raise ValueError(
                     """DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. """
                     """Please scroll up above this traceback to see the activation values prior to this event."""		)
  
            # abort after certain batch if requested to do so
            if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
                 raise ValueError(
                     f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
                     f" `abort_after_batch_num={self.abort_after_batch_num}` arg"		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= var.abs()
       return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= False
       if torch.isnan(__lowerCamelCase					).any().item():
            UpperCAmelCase__			:     List[Any]										= True
            print(F"{ctx} has nans"					)
       if torch.isinf(__lowerCamelCase					).any().item():
            UpperCAmelCase__			:     List[str]										= True
            print(F"{ctx} has infs"					)
  
       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
            UpperCAmelCase__			:     Dict										= var[torch.ge(var.abs()   ,       100					)]
            if naaa.numel() > 0:
                 print(F"{ctx}:  n100={naaa.numel()}"					)
            UpperCAmelCase__			:     Tuple										= var[torch.ge(var.abs()   ,       1000					)]
            if naaaa.numel() > 0:
                 print(F"{ctx}: n1000={naaaa.numel()}"					)
            UpperCAmelCase__			:     Optional[int]										= var[torch.ge(var.abs()   ,       1_0000					)]
            if naaaaa.numel() > 0:
                 print(F"{ctx}: n10000={naaaaa.numel()}"					)
  
       if 0:
            print(F"min={var.min():9.2e} max={var.max():9.2e}"					)
  
       if 0:
            print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"					)
  
       return detected
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
       __lowerCamelCase										=     'underflow_overflow'
       __lowerCamelCase										=     'tpu_metrics_debug'
  
  
  
  
  
  
  