




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







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







def 						lowerCAmelCase_       (	__a )       ->			Dict[str, torch.Tensor]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: List[Any]	     =default_data_collator(__a )
							SCREAMING_SNAKE_CASE			: Optional[int]	     =batch.pop('''pairID'''	,	__a )
							return batch
def 						lowerCAmelCase_       (	)       ->			Dict:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[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'''	,	__a )

							# Set seed
							set_seed(training_args.seed )

							try:
														SCREAMING_SNAKE_CASE			: Dict	     =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.

							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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	,	)
							SCREAMING_SNAKE_CASE			: 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	,	)
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =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
							SCREAMING_SNAKE_CASE			: List[Any]	     =(
							    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
							)
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =(
							    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
							SCREAMING_SNAKE_CASE			: str	     =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 ***''' )

														SCREAMING_SNAKE_CASE			: Tuple	     =trainer.predict(__a )
														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =output.predictions
														SCREAMING_SNAKE_CASE			: List[Any]	     =np.argmax(__a	,	axis=1 )

														SCREAMING_SNAKE_CASE			: List[Any]	     =[ex.pairID for ex in eval_dataset]
														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(training_args.output_dir	,	'''hans_predictions.txt''' )
														SCREAMING_SNAKE_CASE			: int	     =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 						lowerCAmelCase_       (	__a )       ->			Dict:





							"""simple docstring"""





							main()


if __name__ == "__main__":
							main()












from typing import TYPE_CHECKING

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


_A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RegNetForImageClassification""",
							    """RegNetModel""",
							    """RegNetPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRegNetForImageClassification""",
							    """TFRegNetModel""",
							    """TFRegNetPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """FlaxRegNetForImageClassification""",
							    """FlaxRegNetModel""",
							    """FlaxRegNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_regnet import (
														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RegNetForImageClassification,
														    RegNetModel,
														    RegNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_regnet import (
														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRegNetForImageClassification,
														    TFRegNetModel,
														    TFRegNetPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_regnet import (
														    FlaxRegNetForImageClassification,
														    FlaxRegNetModel,
														    FlaxRegNetPreTrainedModel,
														)


else:
							import sys

							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)






