






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						= logging.getLogger(__name__)

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

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






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Dict[str, torch.Tensor]:







							"""simple docstring"""





							_a  :    str			=						default_data_collator(UpperCAmelCase     )
							_a  :    Optional[int]			=						batch.pop('''pairID'''    ,	UpperCAmelCase     )
							return batch






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







							"""simple docstring"""





							_a  :    int			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
							_a		,					_a		,					_a  :    List[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'''    ,	UpperCAmelCase     )

							# Set seed
							set_seed(training_args.seed     )

							try:
														_a  :    Optional[Any]			=						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.

							_a  :    int			=						AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,	num_labels=UpperCAmelCase    ,	finetuning_task=data_args.task_name    ,	cache_dir=model_args.cache_dir    ,	)
							_a  :    int			=						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    ,	)
							_a  :    int			=						AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path    ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )    ,	config=UpperCAmelCase    ,	cache_dir=model_args.cache_dir    ,	)

							# Get datasets
							_a  :    Optional[int]			=						(
							    HansDataset(
							        data_dir=data_args.data_dir    ,	tokenizer=UpperCAmelCase    ,	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
							)
							_a  :    Optional[int]			=						(
							    HansDataset(
							        data_dir=data_args.data_dir    ,	tokenizer=UpperCAmelCase    ,	task=data_args.task_name    ,	max_seq_length=data_args.max_seq_length    ,	overwrite_cache=data_args.overwrite_cache    ,	evaluate=UpperCAmelCase    ,	)
							    if training_args.do_eval
							    else None
							)

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

							# 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 ***'''     )

														_a  :    Dict			=						trainer.predict(UpperCAmelCase     )
														_a  :    Optional[int]			=						output.predictions
														_a  :    Dict			=						np.argmax(UpperCAmelCase    ,	axis=1     )

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

														trainer._log(output.metrics     )






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		int:







							"""simple docstring"""





							main()


if __name__ == "__main__":
						main()







__lowerCamelCase						= {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase						= {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    List[Any]			=						True
							_a  :    Optional[Any]			=						[]

							for neighbour in graph[vert]:
														if not visited[neighbour]:
																					connected_verts += dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )

							return [vert, *connected_verts]






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    Optional[int]			=						len(UpperCAmelCase     )
							_a  :    Optional[Any]			=						graph_size * [False]
							_a  :    Optional[int]			=						[]

							for i in range(UpperCAmelCase     ):
														if not visited[i]:
																					_a  :    List[str]			=						dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					components_list.append(UpperCAmelCase     )

							return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()