






"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_		:			Dict				   =					{
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_		:			Any				   =					[
     '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
     '''MODEL_FOR_BACKBONE_MAPPING''',
     '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
     '''MODEL_FOR_CAUSAL_LM_MAPPING''',
     '''MODEL_FOR_CTC_MAPPING''',
     '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
     '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
     '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
     '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
     '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
     '''MODEL_FOR_MASKED_LM_MAPPING''',
     '''MODEL_FOR_MASK_GENERATION_MAPPING''',
     '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
     '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
     '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
     '''MODEL_FOR_PRETRAINING_MAPPING''',
     '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
     '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
     '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
     '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
     '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
     '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
     '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
     '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
     '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
     '''MODEL_MAPPING''',
     '''MODEL_WITH_LM_HEAD_MAPPING''',
     '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
     '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
     '''AutoModel''',
     '''AutoBackbone''',
     '''AutoModelForAudioClassification''',
     '''AutoModelForAudioFrameClassification''',
     '''AutoModelForAudioXVector''',
     '''AutoModelForCausalLM''',
     '''AutoModelForCTC''',
     '''AutoModelForDepthEstimation''',
     '''AutoModelForImageClassification''',
     '''AutoModelForImageSegmentation''',
     '''AutoModelForInstanceSegmentation''',
     '''AutoModelForMaskGeneration''',
     '''AutoModelForTextEncoding''',
     '''AutoModelForMaskedImageModeling''',
     '''AutoModelForMaskedLM''',
     '''AutoModelForMultipleChoice''',
     '''AutoModelForNextSentencePrediction''',
     '''AutoModelForObjectDetection''',
     '''AutoModelForPreTraining''',
     '''AutoModelForQuestionAnswering''',
     '''AutoModelForSemanticSegmentation''',
     '''AutoModelForSeq2SeqLM''',
     '''AutoModelForSequenceClassification''',
     '''AutoModelForSpeechSeq2Seq''',
     '''AutoModelForTableQuestionAnswering''',
     '''AutoModelForTokenClassification''',
     '''AutoModelForUniversalSegmentation''',
     '''AutoModelForVideoClassification''',
     '''AutoModelForVision2Seq''',
     '''AutoModelForVisualQuestionAnswering''',
     '''AutoModelForDocumentQuestionAnswering''',
     '''AutoModelWithLMHead''',
     '''AutoModelForZeroShotImageClassification''',
     '''AutoModelForZeroShotObjectDetection''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_		:			List[Any]				   =					[
     '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
     '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
     '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
     '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
     '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
     '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
     '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
     '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
     '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
     '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
     '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
     '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
     '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
     '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
     '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
     '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
     '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
     '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
     '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
     '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
     '''TF_MODEL_MAPPING''',
     '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
     '''TFAutoModel''',
     '''TFAutoModelForAudioClassification''',
     '''TFAutoModelForCausalLM''',
     '''TFAutoModelForImageClassification''',
     '''TFAutoModelForMaskedImageModeling''',
     '''TFAutoModelForMaskedLM''',
     '''TFAutoModelForMaskGeneration''',
     '''TFAutoModelForMultipleChoice''',
     '''TFAutoModelForNextSentencePrediction''',
     '''TFAutoModelForPreTraining''',
     '''TFAutoModelForDocumentQuestionAnswering''',
     '''TFAutoModelForQuestionAnswering''',
     '''TFAutoModelForSemanticSegmentation''',
     '''TFAutoModelForSeq2SeqLM''',
     '''TFAutoModelForSequenceClassification''',
     '''TFAutoModelForSpeechSeq2Seq''',
     '''TFAutoModelForTableQuestionAnswering''',
     '''TFAutoModelForTextEncoding''',
     '''TFAutoModelForTokenClassification''',
     '''TFAutoModelForVision2Seq''',
     '''TFAutoModelForZeroShotImageClassification''',
     '''TFAutoModelWithLMHead''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_		:			Any				   =					[
     '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
     '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
     '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
     '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
     '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
     '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
     '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
     '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
     '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
     '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
     '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
     '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
     '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
     '''FLAX_MODEL_MAPPING''',
     '''FlaxAutoModel''',
     '''FlaxAutoModelForCausalLM''',
     '''FlaxAutoModelForImageClassification''',
     '''FlaxAutoModelForMaskedLM''',
     '''FlaxAutoModelForMultipleChoice''',
     '''FlaxAutoModelForNextSentencePrediction''',
     '''FlaxAutoModelForPreTraining''',
     '''FlaxAutoModelForQuestionAnswering''',
     '''FlaxAutoModelForSeq2SeqLM''',
     '''FlaxAutoModelForSequenceClassification''',
     '''FlaxAutoModelForSpeechSeq2Seq''',
     '''FlaxAutoModelForTokenClassification''',
     '''FlaxAutoModelForVision2Seq''',
 ]


if TYPE_CHECKING:
 from .auto_factory import get_values
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
 from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
 from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
 from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
 from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_auto import (
      MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
      MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
      MODEL_FOR_AUDIO_XVECTOR_MAPPING,
      MODEL_FOR_BACKBONE_MAPPING,
      MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
      MODEL_FOR_CAUSAL_LM_MAPPING,
      MODEL_FOR_CTC_MAPPING,
      MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
      MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
      MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
      MODEL_FOR_MASK_GENERATION_MAPPING,
      MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
      MODEL_FOR_MASKED_LM_MAPPING,
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      MODEL_FOR_OBJECT_DETECTION_MAPPING,
      MODEL_FOR_PRETRAINING_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
      MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_TEXT_ENCODING_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      MODEL_FOR_VISION_2_SEQ_MAPPING,
      MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
      MODEL_MAPPING,
      MODEL_WITH_LM_HEAD_MAPPING,
      AutoBackbone,
      AutoModel,
      AutoModelForAudioClassification,
      AutoModelForAudioFrameClassification,
      AutoModelForAudioXVector,
      AutoModelForCausalLM,
      AutoModelForCTC,
      AutoModelForDepthEstimation,
      AutoModelForDocumentQuestionAnswering,
      AutoModelForImageClassification,
      AutoModelForImageSegmentation,
      AutoModelForInstanceSegmentation,
      AutoModelForMaskedImageModeling,
      AutoModelForMaskedLM,
      AutoModelForMaskGeneration,
      AutoModelForMultipleChoice,
      AutoModelForNextSentencePrediction,
      AutoModelForObjectDetection,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSemanticSegmentation,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForSpeechSeqaSeq,
      AutoModelForTableQuestionAnswering,
      AutoModelForTextEncoding,
      AutoModelForTokenClassification,
      AutoModelForUniversalSegmentation,
      AutoModelForVideoClassification,
      AutoModelForVisionaSeq,
      AutoModelForVisualQuestionAnswering,
      AutoModelForZeroShotImageClassification,
      AutoModelForZeroShotObjectDetection,
      AutoModelWithLMHead,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_auto import (
      TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_MASK_GENERATION_MAPPING,
      TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
      TF_MODEL_FOR_MASKED_LM_MAPPING,
      TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      TF_MODEL_FOR_PRETRAINING_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
      TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
      TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_MAPPING,
      TF_MODEL_WITH_LM_HEAD_MAPPING,
      TFAutoModel,
      TFAutoModelForAudioClassification,
      TFAutoModelForCausalLM,
      TFAutoModelForDocumentQuestionAnswering,
      TFAutoModelForImageClassification,
      TFAutoModelForMaskedImageModeling,
      TFAutoModelForMaskedLM,
      TFAutoModelForMaskGeneration,
      TFAutoModelForMultipleChoice,
      TFAutoModelForNextSentencePrediction,
      TFAutoModelForPreTraining,
      TFAutoModelForQuestionAnswering,
      TFAutoModelForSemanticSegmentation,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSequenceClassification,
      TFAutoModelForSpeechSeqaSeq,
      TFAutoModelForTableQuestionAnswering,
      TFAutoModelForTextEncoding,
      TFAutoModelForTokenClassification,
      TFAutoModelForVisionaSeq,
      TFAutoModelForZeroShotImageClassification,
      TFAutoModelWithLMHead,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_auto import (
      FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_MASKED_LM_MAPPING,
      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      FLAX_MODEL_FOR_PRETRAINING_MAPPING,
      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
      FLAX_MODEL_MAPPING,
      FlaxAutoModel,
      FlaxAutoModelForCausalLM,
      FlaxAutoModelForImageClassification,
      FlaxAutoModelForMaskedLM,
      FlaxAutoModelForMultipleChoice,
      FlaxAutoModelForNextSentencePrediction,
      FlaxAutoModelForPreTraining,
      FlaxAutoModelForQuestionAnswering,
      FlaxAutoModelForSeqaSeqLM,
      FlaxAutoModelForSequenceClassification,
      FlaxAutoModelForSpeechSeqaSeq,
      FlaxAutoModelForTokenClassification,
      FlaxAutoModelForVisionaSeq,
  )

else:
 import sys

 lowerCAmelCase_		:			List[Any]				   =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




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





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






def    a__       ( __lowercase      )		->		Dict[str, torch.Tensor]:
				_A         =	default_data_collator(__lowercase      )
				_A         =	batch.pop("pairID"   ,	__lowercase      )
				return batch






def    a__       ( )		->		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.

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

				# Set seed
				set_seed(training_args.seed      )

				try:
								_A         =	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.

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

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

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

				# 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 ***"      )

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

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

								trainer._log(output.metrics      )






def    a__       ( __lowercase      )		->		Dict:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()