





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:  Optional[Any]   =   logging.getLogger(__name__)


@dataclass
class 			__lowerCAmelCase				:


	'''simple docstring'''





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


@dataclass
class 			__lowerCAmelCase				:


	'''simple docstring'''





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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[InputFeatures] )      ->       Dict[str, torch.Tensor]:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		default_data_collator(_lowercase )
		lowercase__				:			Optional[Any]            =		batch.pop('pairID'      ,						_lowercase )
		return batch


def 			SCREAMING_SNAKE_CASE__     (					)      ->       Union[str, Any]:




		'''simple docstring'''
		# 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.

		lowercase__				:			Union[str, Any]            =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
		lowercase__      ,							lowercase__      ,							lowercase__				:			int            =		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:
				lowercase__				:			Optional[Any]            =		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__				:			Union[str, Any]            =		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      ,						)
		lowercase__				:			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      ,						)
		lowercase__				:			Any            =		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
		lowercase__				:			int            =		(
		    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
		)
		lowercase__				:			Tuple            =		(
		    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
		lowercase__				:			List[Any]            =		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 ***' )

				lowercase__				:			int            =		trainer.predict(_lowercase )
				lowercase__				:			List[str]            =		output.predictions
				lowercase__				:			Any            =		np.argmax(_lowercase      ,						axis=1 )

				lowercase__				:			List[str]            =		[ex.pairID for ex in eval_dataset]
				lowercase__				:			int            =		os.path.join(training_args.output_dir      ,						'hans_predictions.txt' )
				lowercase__				:			Optional[Any]            =		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 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[Any] )      ->       Tuple:




		'''simple docstring'''
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()









from typing import TYPE_CHECKING

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


__UpperCamelCase:  Dict   =   {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFDebertaV2ForMaskedLM""",
			    """TFDebertaV2ForQuestionAnswering""",
			    """TFDebertaV2ForSequenceClassification""",
			    """TFDebertaV2ForTokenClassification""",
			    """TFDebertaV2Model""",
			    """TFDebertaV2PreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   [
			    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DebertaV2ForMaskedLM""",
			    """DebertaV2ForMultipleChoice""",
			    """DebertaV2ForQuestionAnswering""",
			    """DebertaV2ForSequenceClassification""",
			    """DebertaV2ForTokenClassification""",
			    """DebertaV2Model""",
			    """DebertaV2PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  Optional[Any]   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


