
'''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


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






@dataclass
class 					lowerCAmelCase__  :
					lowerCAmelCase_	  =	field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
					lowerCAmelCase_	  =	field(
					    default=lowerCamelCase_       ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
					lowerCAmelCase_	  =	field(
					    default=lowerCamelCase_       ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
					lowerCAmelCase_	  =	field(
					    default=lowerCamelCase_       ,		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_2_8       ,		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=lowerCamelCase_       ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[InputFeatures]				):



						"""simple docstring"""





						lowercase_      :	Union[str, Any]						=			default_data_collator(snake_case_				)
						lowercase_      :	List[Any]						=			batch.pop('''pairID''' , snake_case_				)
						return batch
def    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	List[str]						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
						lowercase_      :	Any						=			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''' , snake_case_				)

						# Set seed
						set_seed(training_args.seed				)

						try:
												lowercase_      :	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.

						lowercase_      :	List[Any]						=			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=snake_case_ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
						lowercase_      :	List[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_      :	int						=			AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path				) , config=snake_case_ , cache_dir=model_args.cache_dir , )

						# Get datasets
						lowercase_      :	List[str]						=			(
						    HansDataset(
						        data_dir=data_args.data_dir , tokenizer=snake_case_ , 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_      :	str						=			(
						    HansDataset(
						        data_dir=data_args.data_dir , tokenizer=snake_case_ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , evaluate=snake_case_ , )
						    if training_args.do_eval
						    else None
						)

						# Initialize our Trainer
						lowercase_      :	Union[str, Any]						=			Trainer(
						    model=snake_case_ , args=snake_case_ , train_dataset=snake_case_ , eval_dataset=snake_case_ , data_collator=snake_case_ , )

						# 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_      :	Tuple						=			trainer.predict(snake_case_				)
												lowercase_      :	List[str]						=			output.predictions
												lowercase_      :	str						=			np.argmax(snake_case_ , axis=1				)

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

												trainer._log(output.metrics				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						main()


if __name__ == "__main__":
							main()





import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
