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__	:      List[str]				=						logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      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      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      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=1_2_8      ,	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     UpperCAmelCase__      (			UpperCAmelCase_					:       List[InputFeatures]							)  ->	Dict[str, torch.Tensor]:
       __lowerCamelCase     :			Optional[Any]              =	default_data_collator(UpperCAmelCase_							)
       __lowerCamelCase     :			Dict              =	batch.pop('pairID'   ,					UpperCAmelCase_							)
       return batch
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[Any]:
       # 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.
  
       __lowerCamelCase     :			str              =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
       __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			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'   ,					UpperCAmelCase_							)
  
       # Set seed
       set_seed(training_args.seed							)
  
       try:
            __lowerCamelCase     :			Tuple              =	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.
  
       __lowerCamelCase     :			str              =	AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,					num_labels=UpperCAmelCase_   ,					finetuning_task=data_args.task_name   ,					cache_dir=model_args.cache_dir   ,					)
       __lowerCamelCase     :			Union[str, 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   ,					)
       __lowerCamelCase     :			Tuple              =	AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path   ,					from_tf=bool('.ckpt' in model_args.model_name_or_path							)   ,					config=UpperCAmelCase_   ,					cache_dir=model_args.cache_dir   ,					)
  
       # Get datasets
       __lowerCamelCase     :			List[str]              =	(
           HansDataset(
               data_dir=data_args.data_dir   ,					tokenizer=UpperCAmelCase_   ,					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
       )
       __lowerCamelCase     :			Tuple              =	(
           HansDataset(
               data_dir=data_args.data_dir   ,					tokenizer=UpperCAmelCase_   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					evaluate=UpperCAmelCase_   ,					)
           if training_args.do_eval
           else None
       )
  
       # Initialize our Trainer
       __lowerCamelCase     :			str              =	Trainer(
           model=UpperCAmelCase_   ,					args=UpperCAmelCase_   ,					train_dataset=UpperCAmelCase_   ,					eval_dataset=UpperCAmelCase_   ,					data_collator=UpperCAmelCase_   ,					)
  
       # 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 ***'							)
  
            __lowerCamelCase     :			Union[str, Any]              =	trainer.predict(UpperCAmelCase_							)
            __lowerCamelCase     :			Optional[Any]              =	output.predictions
            __lowerCamelCase     :			List[str]              =	np.argmax(UpperCAmelCase_   ,					axis=1							)
  
            __lowerCamelCase     :			Union[str, Any]              =	[ex.pairID for ex in eval_dataset]
            __lowerCamelCase     :			Tuple              =	os.path.join(training_args.output_dir   ,					'hans_predictions.txt'							)
            __lowerCamelCase     :			str              =	eval_dataset.get_labels()
            if trainer.is_world_master():
                 with open(UpperCAmelCase_   ,					'w'							) as writer:
                      writer.write('pairID,gold_label\n'							)
                      for pid, pred in zip(UpperCAmelCase_   ,					UpperCAmelCase_							):
                           writer.write('ex' + str(UpperCAmelCase_							) + ',' + label_list[int(UpperCAmelCase_							)] + '\n'							)
  
            trainer._log(output.metrics							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[Any]							)  ->	Any:
       # For xla_spawn (TPUs)
       main()
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)
  
  A__	:      str				=						{
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    'prophetnet'
      lowerCamelCase						:      List[str]							  =    ['past_key_values']
      lowerCamelCase						:      List[str]							  =    {
          'num_attention_heads': 'num_encoder_attention_heads',
      }
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[Any]              =	vocab_size
           __lowerCamelCase     :			Union[str, Any]              =	hidden_size
           __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
           __lowerCamelCase     :			Any              =	num_encoder_layers
           __lowerCamelCase     :			int              =	num_encoder_attention_heads
           __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
           __lowerCamelCase     :			str              =	num_decoder_layers
           __lowerCamelCase     :			int              =	num_decoder_attention_heads
           __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
           __lowerCamelCase     :			Union[str, Any]              =	activation_function
  
           # parameters for prophetnet
           __lowerCamelCase     :			int              =	ngram
           __lowerCamelCase     :			Optional[int]              =	num_buckets
           __lowerCamelCase     :			str              =	relative_max_distance
           __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
           __lowerCamelCase     :			str              =	eps
  
           # 3 Types of Dropout
           __lowerCamelCase     :			Optional[Any]              =	attention_dropout
           __lowerCamelCase     :			str              =	activation_dropout
           __lowerCamelCase     :			Optional[Any]              =	dropout
  
           __lowerCamelCase     :			Optional[Any]              =	use_cache
  
           super().__init__(
               pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
      @num_hidden_layers.setter
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'			)
  
  
  
  
  
  
  