



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       _lowercase				:





	"""simple docstring"""
	__A		      =				field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
	__A		      =				field(
	    default=lowerCAmelCase, metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
	__A		      =				field(
	    default=lowerCAmelCase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
	__A		      =				field(
	    default=lowerCAmelCase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )




@dataclass
class       _lowercase				:





	"""simple docstring"""
	__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=lowerCAmelCase, metadata={"help": "Overwrite the cached training and evaluation sets"}      )






def        a( A			:       List[InputFeatures]			)						->			Dict[str, torch.Tensor]:
					"""simple docstring"""





					a   	=  default_data_collator(A			)
					a   	=  batch.pop("pairID"					,	A			)
					return batch






def        a( )						->			int:
					"""simple docstring"""





					a   	=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
					a	,							a	,							a   	=  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:
										a   	=  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   	=  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					,	)
					a   	=  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   	=  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
					a   	=  (
					    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
					)
					a   	=  (
					    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
					a   	=  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 ***"			)

										a   	=  trainer.predict(A			)
										a   	=  output.predictions
										a   	=  np.argmax(A					,	axis=1			)

										a   	=  [ex.pairID for ex in eval_dataset]
										a   	=  os.path.join(training_args.output_dir					,	"hans_predictions.txt"			)
										a   	=  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        a( A			:       Dict			)						->			Any:
					"""simple docstring"""





					main()


if __name__ == "__main__":
				main()





import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def        a( A			:       Dict			)						->			Optional[int]:
					"""simple docstring"""





					return EnvironmentCommand()







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						a   	=  parser.add_parser("env"						,			help="Print relevant system environment info."						)
						download_parser.set_defaults(func=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "`datasets` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "Huggingface_hub version": huggingface_hub.__version__,
						    "PyArrow version": pyarrow.__version__,
						    "Pandas version": pandas.__version__,
						}

						print("\nCopy-and-paste the text below in your GitHub issue.\n"						)
						print(self.format_dict(lowerCamelCase_						)						)

						return info




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"
