


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


lowerCAmelCase__						:str         =							logging.getLogger(__name__)

@dataclass
class 	__a   :
		_a			:						Optional[Any]    			=			field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
		_a			:						Optional[int]    			=			field(
		    default=SCREAMING_SNAKE_CASE__					,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
		_a			:						int    			=			field(
		    default=SCREAMING_SNAKE_CASE__					,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
		_a			:						Dict    			=			field(
		    default=SCREAMING_SNAKE_CASE__					,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,   )



@dataclass
class 	__a   :
		_a			:						Optional[Any]    			=			field(
		    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()    )}    )
		_a			:						List[str]    			=			field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
		_a			:						List[Any]    			=			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			:						int    			=			field(
		    default=SCREAMING_SNAKE_CASE__					,   metadata={'help': 'Overwrite the cached training and evaluation sets'}    )



def  lowerCAmelCase__				(    a__:	Optional[int] )						->	Dict[str, torch.Tensor]:


							'''simple docstring'''





							_UpperCAmelCase					=    default_data_collator(a__ )
							_UpperCAmelCase					=    batch.pop('pairID'    ,						a__ )
							return batch

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


							'''simple docstring'''





							_UpperCAmelCase					=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							_UpperCAmelCase	, _UpperCAmelCase	, _UpperCAmelCase					=    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					=    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					=    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					=    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					=    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					=    (
							    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					=    (
							    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					=    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					=    trainer.predict(a__ )
														_UpperCAmelCase					=    output.predictions
														_UpperCAmelCase					=    np.argmax(a__    ,						axis=1 )

														_UpperCAmelCase					=    [ex.pairID for ex in eval_dataset]
														_UpperCAmelCase					=    os.path.join(training_args.output_dir    ,						'hans_predictions.txt' )
														_UpperCAmelCase					=    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__:	int )						->	List[Any]:


							'''simple docstring'''





							main()


if __name__ == "__main__":
		main()





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 	__a   (       UpperCAmelCase    ):
		_a			:						int    			=			['image_processor', 'tokenizer']
		_a			:						Union[str, Any]    			=			'AutoImageProcessor'
		_a			:						Optional[int]    			=			'AutoTokenizer'
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
																_UpperCAmelCase					=    kwargs.pop('feature_extractor'		)

									_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You have to specify an image_processor.'		)
									if tokenizer is None:
																raise ValueError('You have to specify a tokenizer.'		)

									super().__init__(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.image_processor
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									if text is None and images is None:
																raise ValueError('You have to specify either text or images. Both cannot be none.'		)

									if text is not None:
																_UpperCAmelCase					=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if images is not None:
																_UpperCAmelCase					=    self.image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if text is not None and images is not None:
																_UpperCAmelCase					=    image_features.pixel_values
																return encoding
									elif text is not None:
																return encoding
									else:
																return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,					tensor_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.tokenizer.model_input_names
									_UpperCAmelCase					=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor_class






		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor
