



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case_    					=	logging.getLogger(__name__)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						return (preds == labels).mean()







@dataclass
class 				A_      :



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







@dataclass
class 				A_      :



						"""simple docstring"""
						__UpperCamelCase		    =							field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()    )}    )
						__UpperCamelCase		    =							field(metadata={"""help""": """Should contain the data files for the task."""}    )
						__UpperCamelCase		    =							field(
						    default=1_28     ,       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=_lowerCAmelCase     ,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )


def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
						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'		,     lowerCAmelCase__						)

						# Set seed
						set_seed(training_args.seed						)

						try:
												UpperCAmelCase					=			processors[data_args.task_name]()
												UpperCAmelCase					=			processor.get_labels()
												UpperCAmelCase					=			len(lowerCAmelCase__						)
						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=lowerCAmelCase__		,     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					=			AutoModelForMultipleChoice.from_pretrained(
						    model_args.model_name_or_path		,     from_tf=bool('.ckpt' in model_args.model_name_or_path						)		,     config=lowerCAmelCase__		,     cache_dir=model_args.cache_dir		,     )

						# Get datasets
						UpperCAmelCase					=			(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir		,     tokenizer=lowerCAmelCase__		,     task=data_args.task_name		,     max_seq_length=data_args.max_seq_length		,     overwrite_cache=data_args.overwrite_cache		,     mode=Split.train		,     )
						    if training_args.do_train
						    else None
						)
						UpperCAmelCase					=			(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir		,     tokenizer=lowerCAmelCase__		,     task=data_args.task_name		,     max_seq_length=data_args.max_seq_length		,     overwrite_cache=data_args.overwrite_cache		,     mode=Split.dev		,     )
						    if training_args.do_eval
						    else None
						)

						def compute_metrics(lowercase_						) -> Dict:
												UpperCAmelCase					=			np.argmax(p.predictions		,     axis=1						)
												return {"acc": simple_accuracy(lowerCAmelCase__		,     p.label_ids						)}

						# Data collator
						UpperCAmelCase					=			DataCollatorWithPadding(lowerCAmelCase__		,     pad_to_multiple_of=8						) if training_args.fpaa else None

						# Initialize our Trainer
						UpperCAmelCase					=			Trainer(
						    model=lowerCAmelCase__		,     args=lowerCAmelCase__		,     train_dataset=lowerCAmelCase__		,     eval_dataset=lowerCAmelCase__		,     compute_metrics=lowerCAmelCase__		,     data_collator=lowerCAmelCase__		,     )

						# 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
						UpperCAmelCase					=			{}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'						)

												UpperCAmelCase					=			trainer.evaluate()

												UpperCAmelCase					=			os.path.join(training_args.output_dir		,     'eval_results.txt'						)
												if trainer.is_world_master():
																		with open(lowerCAmelCase__		,     'w'						) as writer:
																								logger.info('***** Eval results *****'						)
																								for key, value in result.items():
																														logger.info('  %s = %s'		,     lowerCAmelCase__		,     lowerCAmelCase__						)
																														writer.write('%s = %s\n' % (key, value)						)

																								results.update(lowerCAmelCase__						)

						return results

def 			_lowerCAmelCase				(       lowercase_						):
						main()


if __name__ == "__main__":
				main()










"""simple docstring"""


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
				import transformers

def 			_lowerCAmelCase				(       lowercase_						):
						random.seed(lowercase_						)
						np.random.seed(lowercase_						)
						torch.manual_seed(lowercase_						)
						torch.cuda.manual_seed_all(lowercase_						)
						# ^^ safe to call this function even if cuda is not available







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:Iterable[torch.nn.Parameter]       ,  lowercase_			:float = 0.9999       ,  lowercase_			:float = 0.0       ,  lowercase_			:int = 0       ,  lowercase_			:bool = False       ,  lowercase_			:Union[float, int] = 1.0       ,  lowercase_			:Union[float, int] = 2 / 3       ,  lowercase_			:Optional[Any] = None       ,  lowercase_			:Dict[str, Any] = None       ,  **lowercase_			:Dict       ,  )    ->      Optional[int]:

												if isinstance(lowercase_       ,  torch.nn.Module ):
																		UpperCAmelCase					=			(
																		    'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
																		    'Please pass the parameters of the module instead.'
																		)
																		deprecate(
																		    'passing a `torch.nn.Module` to `ExponentialMovingAverage`'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_       ,  )
																		UpperCAmelCase					=			parameters.parameters()

																		# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																		UpperCAmelCase					=			True

												if kwargs.get('max_value'       ,  lowercase_ ) is not None:
																		UpperCAmelCase					=			'The `max_value` argument is deprecated. Please use `decay` instead.'
																		deprecate('max_value'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			kwargs['max_value']

												if kwargs.get('min_value'       ,  lowercase_ ) is not None:
																		UpperCAmelCase					=			'The `min_value` argument is deprecated. Please use `min_decay` instead.'
																		deprecate('min_value'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			kwargs['min_value']

												UpperCAmelCase					=			list(lowercase_ )
												UpperCAmelCase					=			[p.clone().detach() for p in parameters]

												if kwargs.get('device'       ,  lowercase_ ) is not None:
																		UpperCAmelCase					=			'The `device` argument is deprecated. Please use `to` instead.'
																		deprecate('device'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		self.to(device=kwargs['device'] )

												UpperCAmelCase					=			None

												UpperCAmelCase					=			decay
												UpperCAmelCase					=			min_decay
												UpperCAmelCase					=			update_after_step
												UpperCAmelCase					=			use_ema_warmup
												UpperCAmelCase					=			inv_gamma
												UpperCAmelCase					=			power
												UpperCAmelCase					=			0
												UpperCAmelCase					=			None  # set in `step()`

												UpperCAmelCase					=			model_cls
												UpperCAmelCase					=			model_config






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Any )    ->      "EMAModel":
												UpperCAmelCase				,   UpperCAmelCase					=			model_cls.load_config(lowercase_       ,  return_unused_kwargs=lowercase_ )
												UpperCAmelCase					=			model_cls.from_pretrained(lowercase_ )

												UpperCAmelCase					=			cls(model.parameters()       ,  model_cls=lowercase_       ,  model_config=model.config )

												ema_model.load_state_dict(lowercase_ )
												return ema_model






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:List[str] )    ->      int:
												if self.model_cls is None:
																		raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.' )

												if self.model_config is None:
																		raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.' )

												UpperCAmelCase					=			self.model_cls.from_config(self.model_config )
												UpperCAmelCase					=			self.state_dict()
												state_dict.pop('shadow_params'       ,  lowercase_ )

												model.register_to_config(**lowercase_ )
												self.copy_to(model.parameters() )
												model.save_pretrained(lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:int )    ->      float:
												UpperCAmelCase					=			max(0       ,  optimization_step - self.update_after_step - 1 )

												if step <= 0:
																		return 0.0

												if self.use_ema_warmup:
																		UpperCAmelCase					=			1 - (1 + step / self.inv_gamma) ** -self.power
												else:
																		UpperCAmelCase					=			(1 + step) / (10 + step)

												UpperCAmelCase					=			min(lowercase_       ,  self.decay )
												# make sure decay is not smaller than min_decay
												UpperCAmelCase					=			max(lowercase_       ,  self.min_decay )
												return cur_decay_value






						@torch.no_grad()
						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      Optional[int]:
												if isinstance(lowercase_       ,  torch.nn.Module ):
																		UpperCAmelCase					=			(
																		    'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
																		    'Please pass the parameters of the module instead.'
																		)
																		deprecate(
																		    'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_       ,  )
																		UpperCAmelCase					=			parameters.parameters()

												UpperCAmelCase					=			list(lowercase_ )

												self.optimization_step += 1

												# Compute the decay factor for the exponential moving average.
												UpperCAmelCase					=			self.get_decay(self.optimization_step )
												UpperCAmelCase					=			decay
												UpperCAmelCase					=			1 - decay

												UpperCAmelCase					=			contextlib.nullcontext
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																		import deepspeed

												for s_param, param in zip(self.shadow_params       ,  lowercase_ ):
																		if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																								UpperCAmelCase					=			deepspeed.zero.GatheredParameters(lowercase_       ,  modifier_rank=lowercase_ )

																		with context_manager():
																								if param.requires_grad:
																														s_param.sub_(one_minus_decay * (s_param - param) )
																								else:
																														s_param.copy_(lowercase_ )






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												UpperCAmelCase					=			list(lowercase_ )
												for s_param, param in zip(self.shadow_params       ,  lowercase_ ):
																		param.data.copy_(s_param.to(param.device ).data )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple=None       ,  lowercase_			:Union[str, Any]=None )    ->      None:
												UpperCAmelCase					=			[
												    p.to(device=lowercase_       ,  dtype=lowercase_ ) if p.is_floating_point() else p.to(device=lowercase_ )
												    for p in self.shadow_params
												]






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      dict:
												return {
												    "decay": self.decay,
												    "min_decay": self.min_decay,
												    "optimization_step": self.optimization_step,
												    "update_after_step": self.update_after_step,
												    "use_ema_warmup": self.use_ema_warmup,
												    "inv_gamma": self.inv_gamma,
												    "power": self.power,
												    "shadow_params": self.shadow_params,
												}






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												UpperCAmelCase					=			[param.detach().cpu().clone() for param in parameters]






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												if self.temp_stored_params is None:
																		raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`' )
												for c_param, param in zip(self.temp_stored_params       ,  lowercase_ ):
																		param.data.copy_(c_param.data )

												# Better memory-wise.
												UpperCAmelCase					=			None






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:dict )    ->      None:
												UpperCAmelCase					=			copy.deepcopy(lowercase_ )

												UpperCAmelCase					=			state_dict.get('decay'       ,  self.decay )
												if self.decay < 0.0 or self.decay > 1.0:
																		raise ValueError('Decay must be between 0 and 1' )

												UpperCAmelCase					=			state_dict.get('min_decay'       ,  self.min_decay )
												if not isinstance(self.min_decay       ,  lowercase_ ):
																		raise ValueError('Invalid min_decay' )

												UpperCAmelCase					=			state_dict.get('optimization_step'       ,  self.optimization_step )
												if not isinstance(self.optimization_step       ,  lowercase_ ):
																		raise ValueError('Invalid optimization_step' )

												UpperCAmelCase					=			state_dict.get('update_after_step'       ,  self.update_after_step )
												if not isinstance(self.update_after_step       ,  lowercase_ ):
																		raise ValueError('Invalid update_after_step' )

												UpperCAmelCase					=			state_dict.get('use_ema_warmup'       ,  self.use_ema_warmup )
												if not isinstance(self.use_ema_warmup       ,  lowercase_ ):
																		raise ValueError('Invalid use_ema_warmup' )

												UpperCAmelCase					=			state_dict.get('inv_gamma'       ,  self.inv_gamma )
												if not isinstance(self.inv_gamma       ,  (float, int) ):
																		raise ValueError('Invalid inv_gamma' )

												UpperCAmelCase					=			state_dict.get('power'       ,  self.power )
												if not isinstance(self.power       ,  (float, int) ):
																		raise ValueError('Invalid power' )

												UpperCAmelCase					=			state_dict.get('shadow_params'       ,  lowercase_ )
												if shadow_params is not None:
																		UpperCAmelCase					=			shadow_params
																		if not isinstance(self.shadow_params       ,  lowercase_ ):
																								raise ValueError('shadow_params must be a list' )
																		if not all(isinstance(lowercase_       ,  torch.Tensor ) for p in self.shadow_params ):
																								raise ValueError('shadow_params must all be Tensors' )





