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


UpperCamelCase_    :    str											=						logging.getLogger(__name__)


@dataclass
class      _lowercase						:
				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      _lowercase						:
				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 					a__ (						UpperCAmelCase__    :     List[InputFeatures]     )->		Dict[str, torch.Tensor]:
						UpperCAmelCase__   		=						default_data_collator(UpperCAmelCase__     )
						UpperCAmelCase__   		=						batch.pop("""pairID"""						,     UpperCAmelCase__     )
						return batch


def 					a__ (						)->		str:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						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"""						,     UpperCAmelCase__     )

						# 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=UpperCAmelCase__						,     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=UpperCAmelCase__						,     cache_dir=model_args.cache_dir						,     )

						# Get datasets
						UpperCAmelCase__   		=						(
						    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
						)
						UpperCAmelCase__   		=						(
						    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
						UpperCAmelCase__   		=						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 ***"""     )

												UpperCAmelCase__   		=						trainer.predict(UpperCAmelCase__     )
												UpperCAmelCase__   		=						output.predictions
												UpperCAmelCase__   		=						np.argmax(UpperCAmelCase__						,     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(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 					a__ (						UpperCAmelCase__    :     Any     )->		List[Any]:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


"""simple docstring"""







import safs


class      _lowercase						(				safs.SaFileSystem					):
				pass

