






'''simple docstring'''





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


a_      = logging.getLogger(__name__)





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





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





def 			__UpperCAmelCase				(lowercase__				)		->				Dict[str, torch.Tensor]:

						'''simple docstring'''


						a_				     =   default_data_collator(lowercase__				)
						a_				     =   batch.pop("pairID"	,lowercase__				)
						return batch



def 			__UpperCAmelCase				()		->				Tuple:

						'''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"	,lowercase__				)

						# 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=lowercase__	,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=lowercase__	,cache_dir=model_args.cache_dir	,)

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

						# Initialize our Trainer
						a_				     =   Trainer(
						    model=lowercase__	,args=lowercase__	,train_dataset=lowercase__	,eval_dataset=lowercase__	,data_collator=lowercase__	,)

						# 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(lowercase__				)
												a_				     =   output.predictions
												a_				     =   np.argmax(lowercase__	,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(lowercase__	,"w"				) as writer:
																								writer.write("pairID,gold_label\n"				)
																								for pid, pred in zip(lowercase__	,lowercase__				):
																														writer.write("ex" + str(lowercase__				) + "," + label_list[int(lowercase__				)] + "\n"				)

												trainer._log(output.metrics				)



def 			__UpperCAmelCase				(lowercase__				)		->				Any:

						'''simple docstring'''


						main()


if __name__ == "__main__":
							main()











'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



