




"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_      			=       logging.getLogger(__name__)


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      str					):
  """simple docstring"""

  return (preds == labels).mean()






@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,    )







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()				)}				)
 lowercase__				      =      field(metadata={"help": "Should contain the data files for the task."}				)
 lowercase__				      =      field(
     default=1_28 ,    metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     } ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Overwrite the cached training and evaluation sets"}				)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Optional[int]			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
  _snake_case	,      _snake_case	,      _snake_case							:	Dict			=						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:
    _snake_case							:	Any			=						processors[data_args.task_name]()
    _snake_case							:	int			=						processor.get_labels()
    _snake_case							:	str			=						len(_lowerCamelCase					)
  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.

  _snake_case							:	Dict			=						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	,     )
  _snake_case							:	str			=						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	,     )
  _snake_case							:	Dict			=						AutoModelForMultipleChoice.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
  _snake_case							:	Dict			=						(
      MultipleChoiceDataset(
          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	,     mode=Split.train	,     )
      if training_args.do_train
      else None
  )
  _snake_case							:	Optional[Any]			=						(
      MultipleChoiceDataset(
          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	,     mode=Split.dev	,     )
      if training_args.do_eval
      else None
  )

  def compute_metrics(snake_case__							:      EvalPrediction					) -> Dict:
    _snake_case							:	Union[str, Any]			=						np.argmax(p.predictions	,     axis=1					)
    return {"acc": simple_accuracy(_lowerCamelCase	,     p.label_ids					)}

  # Data collator
  _snake_case							:	Tuple			=						DataCollatorWithPadding(_lowerCamelCase	,     pad_to_multiple_of=8					) if training_args.fpaa else None

  # Initialize our Trainer
  _snake_case							:	Tuple			=						Trainer(
      model=_lowerCamelCase	,     args=_lowerCamelCase	,     train_dataset=_lowerCamelCase	,     eval_dataset=_lowerCamelCase	,     compute_metrics=_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
  _snake_case							:	Tuple			=						{}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""					)

    _snake_case							:	Dict			=						trainer.evaluate()

    _snake_case							:	List[Any]			=						os.path.join(training_args.output_dir	,     """eval_results.txt"""					)
    if trainer.is_world_master():
      with open(_lowerCamelCase	,     """w"""					) as writer:
        logger.info("""***** Eval results *****"""					)
        for key, value in result.items():
          logger.info("""  %s = %s"""	,     _lowerCamelCase	,     _lowerCamelCase					)
          writer.write("""%s = %s\n""" % (key, value)					)

        results.update(_lowerCamelCase					)

  return results


def       UpperCAmelCase__ (snake_case__							:      Tuple					):
  """simple docstring"""

  main()


if __name__ == "__main__":
    main()








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_      			=       logging.get_logger(__name__)

A_      			=       {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "roformer"


 def __init__(       self:	List[str],   a_:	Tuple=50_000,   a_:	Optional[Any]=None,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	Optional[int]=12,   a_:	Optional[Any]=3_072,   a_:	List[str]="gelu",   a_:	List[str]=0.1,   a_:	Tuple=0.1,   a_:	Optional[int]=1_536,   a_:	Any=2,   a_:	Optional[int]=0.02,   a_:	Tuple=1E-12,   a_:	Dict=0,   a_:	str=False,   a_:	Dict=True,   **a_:	Dict,   ):




   '''simple docstring'''



   super().__init__(pad_token_id=a_,   **a_	)

   _snake_case							:	int			=						vocab_size
   _snake_case							:	int			=						hidden_size if embedding_size is None else embedding_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	Any			=						num_attention_heads
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[Any]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	List[Any]			=						initializer_range
   _snake_case							:	List[Any]			=						layer_norm_eps
   _snake_case							:	Optional[Any]			=						rotary_value
   _snake_case							:	List[str]			=						use_cache







class       lowercase(  __a				):







 '''simple docstring'''





 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   if self.task == "multiple-choice":
     _snake_case							:	str			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     _snake_case							:	List[str]			=						{0: """batch""", 1: """sequence"""}
   _snake_case							:	List[Any]			=						{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ]	)


