

"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase	=			logging.get_logger(__name__)







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    """speech-encoder-decoder"""
  snake_case_			    =    True

  def __init__(					self						:     Dict   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   super().__init__(**A      )
   if "encoder" not in kwargs or "decoder" not in kwargs:
    raise ValueError(
        f"A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"
        f" `decoder` sub-configurations are passed, but only {kwargs}"      )

   UpperCAmelCase__   :					List[str]   				=   kwargs.pop("""encoder"""      )
   UpperCAmelCase__   :					Dict   				=   encoder_config.pop("""model_type"""      )
   UpperCAmelCase__   :					Optional[int]   				=   kwargs.pop("""decoder"""      )
   UpperCAmelCase__   :					Union[str, Any]   				=   decoder_config.pop("""model_type"""      )

   UpperCAmelCase__   :					List[Any]   				=   AutoConfig.for_model(A   ,**A      )
   UpperCAmelCase__   :					Optional[int]   				=   AutoConfig.for_model(A   ,**A      )
   UpperCAmelCase__   :					List[str]   				=   True

  @classmethod
  def 				__lowercase    (					cls						:     int   ,A						:     PretrainedConfig   ,A						:     PretrainedConfig   ,**A						:     Tuple      ):






   '''simple docstring'''
   logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""      )
   UpperCAmelCase__   :					str   				=   True
   UpperCAmelCase__   :					Union[str, Any]   				=   True

   return cls(encoder=encoder_config.to_dict()   ,decoder=decoder_config.to_dict()   ,**A      )




  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   copy.deepcopy(self.__dict__      )
   UpperCAmelCase__   :					str   				=   self.encoder.to_dict()
   UpperCAmelCase__   :					List[Any]   				=   self.decoder.to_dict()
   UpperCAmelCase__   :					int   				=   self.__class__.model_type
   return output








"""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


__UpperCAmelCase	=			logging.getLogger(__name__)







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







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
  snake_case_			    =    field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
  snake_case_			    =    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."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )







def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   default_data_collator(__UpperCamelCase )
 UpperCAmelCase__   :					List[str]   				=   batch.pop("""pairID"""							,      __UpperCamelCase )
 return batch
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   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"""							,      __UpperCamelCase )

 # Set seed
 set_seed(training_args.seed )

 try:
  UpperCAmelCase__   :					Optional[int]   				=   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__   :					Tuple   				=   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							,      )
 UpperCAmelCase__   :					int   				=   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__   :					List[Any]   				=   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
 UpperCAmelCase__   :					Optional[int]   				=   (
     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
 )
 UpperCAmelCase__   :					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							,      evaluate=__UpperCamelCase							,      )
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 UpperCAmelCase__   :					List[Any]   				=   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 ***""" )

  UpperCAmelCase__   :					Optional[Any]   				=   trainer.predict(__UpperCamelCase )
  UpperCAmelCase__   :					List[str]   				=   output.predictions
  UpperCAmelCase__   :					Dict   				=   np.argmax(__UpperCamelCase							,      axis=1 )

  UpperCAmelCase__   :					Optional[Any]   				=   [ex.pairID for ex in eval_dataset]
  UpperCAmelCase__   :					Dict   				=   os.path.join(training_args.output_dir							,      """hans_predictions.txt""" )
  UpperCAmelCase__   :					Dict   				=   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 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 main()


if __name__ == "__main__":
      main()





