

"""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


_SCREAMING_SNAKE_CASE       :  Any        = logging.getLogger(__name__)

@dataclass
class     __a   :

			"""simple docstring"""

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



@dataclass
class     __a   :

			"""simple docstring"""

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



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[InputFeatures]				):


	'''simple docstring'''






	UpperCamelCase__		:    List[str]         =default_data_collator(a_				)
	UpperCamelCase__		:    int         =batch.pop('''pairID'''      ,							a_				)
	return batch



def        _lowerCAmelCase      (						):


	'''simple docstring'''






	UpperCamelCase__		:    Dict         =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
	UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__		:    str         =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:
		UpperCamelCase__		:    Tuple         =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__		:    Optional[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      ,							)
	UpperCamelCase__		:    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      ,							)
	UpperCamelCase__		:    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
	UpperCamelCase__		:    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
	)
	UpperCamelCase__		:    int         =(
	    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
	UpperCamelCase__		:    Dict         =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 ***'''				)

		UpperCamelCase__		:    Any         =trainer.predict(a_				)
		UpperCamelCase__		:    Union[str, Any]         =output.predictions
		UpperCamelCase__		:    Any         =np.argmax(a_      ,							axis=1				)

		UpperCamelCase__		:    Dict         =[ex.pairID for ex in eval_dataset]
		UpperCamelCase__		:    Optional[int]         =os.path.join(training_args.output_dir      ,							'''hans_predictions.txt'''				)
		UpperCamelCase__		:    Optional[Any]         =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      (						UpperCAmelCase     :							str				):


	'''simple docstring'''






	main()


if __name__ == "__main__":
				main()





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

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


