




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__    :    str								=					logging.getLogger(__name__)



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




@dataclass
class 			a_					:
	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=128     ,						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=SCREAMING_SNAKE_CASE__     ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )




def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Dict[str, torch.Tensor]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	default_data_collator(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	batch.pop('pairID'					,						SCREAMING_SNAKE_CASE   )
			return batch

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


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	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'					,						SCREAMING_SNAKE_CASE   )

			# Set seed
			set_seed(training_args.seed   )

			try:
						SCREAMING_SNAKE_CASE_			=	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_			=	AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						num_labels=SCREAMING_SNAKE_CASE					,						finetuning_task=data_args.task_name					,						cache_dir=model_args.cache_dir					,						)
			SCREAMING_SNAKE_CASE_			=	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_			=	AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path					,						from_tf=bool('.ckpt' in model_args.model_name_or_path   )					,						config=SCREAMING_SNAKE_CASE					,						cache_dir=model_args.cache_dir					,						)

			# Get datasets
			SCREAMING_SNAKE_CASE_			=	(
			    HansDataset(
			        data_dir=data_args.data_dir					,						tokenizer=SCREAMING_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
			)
			SCREAMING_SNAKE_CASE_			=	(
			    HansDataset(
			        data_dir=data_args.data_dir					,						tokenizer=SCREAMING_SNAKE_CASE					,						task=data_args.task_name					,						max_seq_length=data_args.max_seq_length					,						overwrite_cache=data_args.overwrite_cache					,						evaluate=SCREAMING_SNAKE_CASE					,						)
			    if training_args.do_eval
			    else None
			)

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE_			=	Trainer(
			    model=SCREAMING_SNAKE_CASE					,						args=SCREAMING_SNAKE_CASE					,						train_dataset=SCREAMING_SNAKE_CASE					,						eval_dataset=SCREAMING_SNAKE_CASE					,						data_collator=SCREAMING_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 ***'   )

						SCREAMING_SNAKE_CASE_			=	trainer.predict(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE_			=	output.predictions
						SCREAMING_SNAKE_CASE_			=	np.argmax(SCREAMING_SNAKE_CASE					,						axis=1   )

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

						trainer._log(output.metrics   )

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[Any]:


			'''simple docstring'''







			main()


if __name__ == "__main__":
				main()






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available



@dataclass
class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
				from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
				from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
