






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()














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'






