






'''simple docstring'''





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


lowerCAmelCase_           = logging.getLogger(__name__)







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       List[Any]   ):
				return (preds == labels).mean()
@dataclass
class      UpperCamelCase :







		"""simple docstring"""



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


@dataclass
class      UpperCamelCase :







		"""simple docstring"""



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







def      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.

				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',						__UpperCamelCase   )

				# Set seed
				set_seed(training_args.seed   )

				try:
								A	 =      processors[data_args.task_name]()
								A	 =      processor.get_labels()
								A	 =      len(__UpperCamelCase   )
				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=__UpperCamelCase,						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	 =      AutoModelForMultipleChoice.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
				A	 =      (
				    MultipleChoiceDataset(
				        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,						mode=Split.train,						)
				    if training_args.do_train
				    else None
				)
				A	 =      (
				    MultipleChoiceDataset(
				        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,						mode=Split.dev,						)
				    if training_args.do_eval
				    else None
				)

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

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

				# Initialize our Trainer
				A	 =      Trainer(
				    model=__UpperCamelCase,						args=__UpperCamelCase,						train_dataset=__UpperCamelCase,						eval_dataset=__UpperCamelCase,						compute_metrics=__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
				A	 =      {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'   )

								A	 =      trainer.evaluate()

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

																results.update(__UpperCamelCase   )

				return results







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()













from collections.abc import Callable
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Callable | None = None		)    ->   None:


						'''simple docstring'''
						# Stores actual heap items.
						A	 =      []
						# Stores indexes of each item for supporting updates and deletion.
						A	 =      {}
						# Stores current size of heap.
						A	 =      0
						# Stores function used to evaluate the score of an item on which basis ordering
						# will be done.
						A	 =      key or (lambda _SCREAMING_SNAKE_CASE		: x)
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						return int((i - 1) / 2		) if i > 0 else None
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						A	 =      int(2 * i + 1		)
						return left if 0 < left < self.size else None
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int | None:


						'''simple docstring'''
						A	 =      int(2 * i + 2		)
						return right if 0 < right < self.size else None
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A ,		A	 =      (
						    self.pos_map[self.arr[j][0]],
						    self.pos_map[self.arr[i][0]],
						)
						# Then swap the items in the list.
						A ,		A	 =      self.arr[j], self.arr[i]
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   bool:


						'''simple docstring'''
						return self.arr[i][1] < self.arr[j][1]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      self._left(_SCREAMING_SNAKE_CASE		)
						A	 =      self._right(_SCREAMING_SNAKE_CASE		)
						A	 =      i

						if left is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      left
						if right is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      right

						return valid_parent
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      self._parent(_SCREAMING_SNAKE_CASE		)
						while parent is not None and not self._cmp(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										self._swap(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A ,		A	 =      parent, self._parent(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      self._get_valid_parent(_SCREAMING_SNAKE_CASE		)
						while valid_parent != index:
										self._swap(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A ,		A	 =      valid_parent, self._get_valid_parent(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						if item not in self.pos_map:
										return
						A	 =      self.pos_map[item]
						A	 =      [item, self.key(_SCREAMING_SNAKE_CASE		)]
						# Make sure heap is right in both up and down direction.
						# Ideally only one of them will make any change.
						self._heapify_up(_SCREAMING_SNAKE_CASE		)
						self._heapify_down(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						if item not in self.pos_map:
										return
						A	 =      self.pos_map[item]
						del self.pos_map[item]
						A	 =      self.arr[self.size - 1]
						A	 =      index
						self.size -= 1
						# Make sure heap is right in both up and down direction. Ideally only one
						# of them will make any change- so no performance loss in calling both.
						if self.size > index:
										self._heapify_up(_SCREAMING_SNAKE_CASE		)
										self._heapify_down(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      len(self.arr		)
						if arr_len == self.size:
										self.arr.append([item, self.key(_SCREAMING_SNAKE_CASE		)]		)
						else:
										A	 =      [item, self.key(_SCREAMING_SNAKE_CASE		)]
						A	 =      self.size
						self.size += 1
						self._heapify_up(self.size - 1		)
		def       A(  self		:       str		)    ->   tuple | None:


						'''simple docstring'''
						return self.arr[0] if self.size else None







		def       A(  self		:       Any		)    ->   tuple | None:


						'''simple docstring'''
						A	 =      self.get_top()
						if top_item_tuple:
										self.delete_item(top_item_tuple[0]		)
						return top_item_tuple







def      snake_case		(							):
				pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()





