



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__					:     Dict    		=   logging.getLogger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	return (preds == labels).mean()

@dataclass
class   _A   :


						'''simple docstring'''





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

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     str      							=      field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()	)}	)
						_snake_case			:     str      							=      field(metadata={"""help""": """Should contain the data files for the task."""}	)
						_snake_case			:     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."""
						        )
						    }  ,						)
						_snake_case			:     bool      							=      field(
						    default=_lowercase  ,						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.

	__lowercase		       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
	__lowercase       ,    __lowercase       ,    __lowercase		       =			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"  ,	_SCREAMING_SNAKE_CASE   )

	# Set seed
	set_seed(training_args.seed   )

	try:
		__lowercase		       =			processors[data_args.task_name]()
		__lowercase		       =			processor.get_labels()
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )
	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.

	__lowercase		       =			AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,	num_labels=_SCREAMING_SNAKE_CASE  ,	finetuning_task=data_args.task_name  ,	cache_dir=model_args.cache_dir  ,	)
	__lowercase		       =			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  ,	)
	__lowercase		       =			AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path  ,	from_tf=bool(".ckpt" in model_args.model_name_or_path   )  ,	config=_SCREAMING_SNAKE_CASE  ,	cache_dir=model_args.cache_dir  ,	)

	# Get datasets
	__lowercase		       =			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir  ,	tokenizer=_SCREAMING_SNAKE_CASE  ,	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
	)
	__lowercase		       =			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir  ,	tokenizer=_SCREAMING_SNAKE_CASE  ,	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(_SCREAMING_SNAKE_CASE   ) -> Dict:
		__lowercase		       =			np.argmax(p.predictions  ,	axis=1   )
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE  ,	p.label_ids   )}

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

	# Initialize our Trainer
	__lowercase		       =			Trainer(
	    model=_SCREAMING_SNAKE_CASE  ,	args=_SCREAMING_SNAKE_CASE  ,	train_dataset=_SCREAMING_SNAKE_CASE  ,	eval_dataset=_SCREAMING_SNAKE_CASE  ,	compute_metrics=_SCREAMING_SNAKE_CASE  ,	data_collator=_SCREAMING_SNAKE_CASE  ,	)

	# 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
	__lowercase		       =			{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"   )

		__lowercase		       =			trainer.evaluate()

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

				results.update(_SCREAMING_SNAKE_CASE   )

	return results



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()








from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

snake_case__					:     Union[str, Any]    		=   TypeVar("""T""")
snake_case__					:     Optional[int]    		=   TypeVar("""U""")

class   _A   ( Generic[T, U]	):


						'''simple docstring'''





						def __init__(	self	:							Optional[int]							,      lowerCamelCase	:							T | None							,      lowerCamelCase	:							U | None	):






							'''simple docstring'''



							__lowercase		       =			key
							__lowercase		       =			val
							__lowercase		       =			None
							__lowercase		       =			None



						def __repr__(	self	:							Any	):






							'''simple docstring'''



							return (
							    f"""Node: key: {self.key}, val: {self.val}, """
							    f"""has next: {bool(self.next	)}, has prev: {bool(self.prev	)}"""
							)

class   _A   ( Generic[T, U]	):


						'''simple docstring'''





						def __init__(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			DoubleLinkedListNode(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			DoubleLinkedListNode(lowerCamelCase							,      lowerCamelCase	)
							__lowercase       ,    __lowercase		       =			self.rear, self.head



						def __repr__(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			["DoubleLinkedList"]
							__lowercase		       =			self.head
							while node.next is not None:
								rep.append(str(lowerCamelCase	)	)
								__lowercase		       =			node.next
							rep.append(str(self.rear	)	)
							return ",\n    ".join(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							DoubleLinkedListNode[T, U]	):






							'''simple docstring'''



							__lowercase		       =			self.rear.prev

							# All nodes other than self.head are guaranteed to have non-None previous
							assert previous is not None

							__lowercase		       =			node
							__lowercase		       =			previous
							__lowercase		       =			node
							__lowercase		       =			self.rear



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							DoubleLinkedListNode[T, U]	):






							'''simple docstring'''



							if node.prev is None or node.next is None:
								return None

							__lowercase		       =			node.next
							__lowercase		       =			node.prev
							__lowercase		       =			None
							__lowercase		       =			None
							return node

class   _A   ( Generic[T, U]	):


						'''simple docstring'''





						_snake_case			:     dict[Callable[[T], U], LRUCache[T, U]]      							=      {}



						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			DoubleLinkedList()
							__lowercase		       =			capacity
							__lowercase		       =			0
							__lowercase		       =			0
							__lowercase		       =			0
							__lowercase		       =			{}



						def __repr__(	self	:							Optional[Any]	):






							'''simple docstring'''



							return (
							    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
							    f"""capacity={self.capacity}, current size={self.num_keys})"""
							)



						def __contains__(	self	:							Dict							,      lowerCamelCase	:							T	):






							'''simple docstring'''



							return key in self.cache



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							T	):






							'''simple docstring'''



							if key in self.cache:
								self.hits += 1
								__lowercase		       =			self.cache[key]
								__lowercase		       =			self.list.remove(self.cache[key]	)
								assert node == value_node

								# node is guaranteed not None because it is in self.cache
								assert node is not None
								self.list.add(lowerCamelCase	)
								return node.val
							self.miss += 1
							return None



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							T							,      lowerCamelCase	:							U	):






							'''simple docstring'''



							if key not in self.cache:
								if self.num_keys >= self.capacity:
									# delete first node (oldest) when over capacity
									__lowercase		       =			self.list.head.next

									# guaranteed to have a non-None first node when num_keys > 0
									# explain to type checker via assertions
									assert first_node is not None
									assert first_node.key is not None
									assert (
									    self.list.remove(lowerCamelCase	) is not None
									)  # node guaranteed to be in list assert node.key is not None

									del self.cache[first_node.key]
									self.num_keys -= 1
								__lowercase		       =			DoubleLinkedListNode(lowerCamelCase							,      lowerCamelCase	)
								self.list.add(self.cache[key]	)
								self.num_keys += 1

							else:
								# bump node to the end of the list, update value
								__lowercase		       =			self.list.remove(self.cache[key]	)
								assert node is not None  # node guaranteed to be in list
								__lowercase		       =			value
								self.list.add(lowerCamelCase	)



						@classmethod
						def     _snake_case	(	cls	:							Union[str, Any]							,      lowerCamelCase	:							int = 128	):






							'''simple docstring'''



							def cache_decorator_inner(lowerCamelCase	:							Callable[[T], U]	) -> Callable[..., U]:
								def cache_decorator_wrapper(*lowerCamelCase	:							T	) -> U:
									if func not in cls.decorator_function_to_instance_map:
										__lowercase		       =			LRUCache(lowerCamelCase	)

									__lowercase		       =			cls.decorator_function_to_instance_map[func].get(args[0]	)
									if result is None:
										__lowercase		       =			func(*lowerCamelCase	)
										cls.decorator_function_to_instance_map[func].put(args[0]							,      lowerCamelCase	)
									return result

								def cache_info() -> LRUCache[T, U]:
									return cls.decorator_function_to_instance_map[func]

								setattr(lowerCamelCase							,      "cache_info"							,      lowerCamelCase	)  # noqa: B010

								return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()



