




'''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							:				Optional[int]			     =	logging.getLogger(__name__)






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






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

def     snake_case_  (       a__		:     List[InputFeatures]      ):



    """simple docstring"""




    __lowercase          =						default_data_collator(a__      )
    __lowercase          =						batch.pop("""pairID"""   ,a__      )
    return batch
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
    __lowercase    ,__lowercase    ,__lowercase          =						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"""   ,a__      )

    # Set seed
    set_seed(training_args.seed      )

    try:
        __lowercase          =						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.

    __lowercase          =						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,num_labels=a__   ,finetuning_task=data_args.task_name   ,cache_dir=model_args.cache_dir   ,)
    __lowercase          =						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   ,)
    __lowercase          =						AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path   ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )   ,config=a__   ,cache_dir=model_args.cache_dir   ,)

    # Get datasets
    __lowercase          =						(
        HansDataset(
            data_dir=data_args.data_dir   ,tokenizer=a__   ,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
    )
    __lowercase          =						(
        HansDataset(
            data_dir=data_args.data_dir   ,tokenizer=a__   ,task=data_args.task_name   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,evaluate=a__   ,)
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    __lowercase          =						Trainer(
        model=a__   ,args=a__   ,train_dataset=a__   ,eval_dataset=a__   ,data_collator=a__   ,)

    # 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 ***"""      )

        __lowercase          =						trainer.predict(a__      )
        __lowercase          =						output.predictions
        __lowercase          =						np.argmax(a__   ,axis=1      )

        __lowercase          =						[ex.pairID for ex in eval_dataset]
        __lowercase          =						os.path.join(training_args.output_dir   ,"""hans_predictions.txt"""      )
        __lowercase          =						eval_dataset.get_labels()
        if trainer.is_world_master():
            with open(a__   ,"""w"""      ) as writer:
                writer.write("""pairID,gold_label\n"""      )
                for pid, pred in zip(a__   ,a__      ):
                    writer.write("""ex""" + str(a__      ) + """,""" + label_list[int(a__      )] + """\n"""      )

        trainer._log(output.metrics      )
def     snake_case_  (       a__		:     Any      ):



    """simple docstring"""




    main()


if __name__ == "__main__":
  main()










'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A							:				Tuple			     =	{
    """configuration_dpr""": ["""DPR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPRConfig"""],
    """tokenization_dpr""": [
        """DPRContextEncoderTokenizer""",
        """DPRQuestionEncoderTokenizer""",
        """DPRReaderOutput""",
        """DPRReaderTokenizer""",
    ],
}


try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				str			     =	[
      """DPRContextEncoderTokenizerFast""",
      """DPRQuestionEncoderTokenizerFast""",
      """DPRReaderTokenizerFast""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Tuple			     =	[
      """DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPRContextEncoder""",
      """DPRPretrainedContextEncoder""",
      """DPRPreTrainedModel""",
      """DPRPretrainedQuestionEncoder""",
      """DPRPretrainedReader""",
      """DPRQuestionEncoder""",
      """DPRReader""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Tuple			     =	[
      """TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDPRContextEncoder""",
      """TFDPRPretrainedContextEncoder""",
      """TFDPRPretrainedQuestionEncoder""",
      """TFDPRPretrainedReader""",
      """TFDPRQuestionEncoder""",
      """TFDPRReader""",
  ]


if TYPE_CHECKING:
  from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
  from .tokenization_dpr import (
      DPRContextEncoderTokenizer,
      DPRQuestionEncoderTokenizer,
      DPRReaderOutput,
      DPRReaderTokenizer,
  )

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_dpr_fast import (
        DPRContextEncoderTokenizerFast,
        DPRQuestionEncoderTokenizerFast,
        DPRReaderTokenizerFast,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpr import (
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPRContextEncoder,
        DPRPretrainedContextEncoder,
        DPRPreTrainedModel,
        DPRPretrainedQuestionEncoder,
        DPRPretrainedReader,
        DPRQuestionEncoder,
        DPRReader,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_dpr import (
        TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDPRContextEncoder,
        TFDPRPretrainedContextEncoder,
        TFDPRPretrainedQuestionEncoder,
        TFDPRPretrainedReader,
        TFDPRQuestionEncoder,
        TFDPRReader,
    )

else:
  import sys

  A							:				int			     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




