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      		=					logging.getLogger(__name__)




@dataclass
class  __lowerCAmelCase	:



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







@dataclass
class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			field(
     metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()    )}    )
 _lowerCamelCase    :						str					     =			field(
     metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
 _lowerCamelCase    :						int					     =			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.'
         )
     },						)
 _lowerCamelCase    :						bool					     =			field(
     default=a_,						metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







def SCREAMING_SNAKE_CASE					(  lowercase_:						List[InputFeatures]					):
       _UpperCamelCase  :							Union[str, Any]	    =	default_data_collator(lowercase_					)
       _UpperCamelCase  :							List[Any]	    =	batch.pop('pairID'		,    lowercase_					)
       return batch


def SCREAMING_SNAKE_CASE					(  ):
       # 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  :							Tuple	    =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	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:
              _UpperCamelCase  :							str	    =	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  :							List[Any]	    =	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		,    )
       _UpperCamelCase  :							List[str]	    =	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  :							List[str]	    =	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
       _UpperCamelCase  :							Any	    =	(
           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
       )
       _UpperCamelCase  :							Optional[int]	    =	(
           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
       _UpperCamelCase  :							Dict	    =	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 ***'					)

              _UpperCamelCase  :							List[Any]	    =	trainer.predict(lowercase_					)
              _UpperCamelCase  :							Any	    =	output.predictions
              _UpperCamelCase  :							Optional[int]	    =	np.argmax(lowercase_		,    axis=1					)

              _UpperCamelCase  :							Dict	    =	[ex.pairID for ex in eval_dataset]
              _UpperCamelCase  :							Tuple	    =	os.path.join(training_args.output_dir		,    'hans_predictions.txt'					)
              _UpperCamelCase  :							Union[str, Any]	    =	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 SCREAMING_SNAKE_CASE					(  lowercase_:						Dict					):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()









import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
