
'''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


a_											=						logging.getLogger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    return (preds == labels).mean()






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






@dataclass
class    UpperCAmelCase_				:
 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_2_8    ,						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=snake_case__    ,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)


def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
    snake_case_			, snake_case_			, snake_case_ :    Dict     			=						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:
        snake_case_ :    Dict     			=						processors[data_args.task_name]()
        snake_case_ :    Tuple     			=						processor.get_labels()
        snake_case_ :    Tuple     			=						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.

    snake_case_ :    Tuple     			=						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,		)
    snake_case_ :    Union[str, Any]     			=						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,		)
    snake_case_ :    int     			=						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
    snake_case_ :    Tuple     			=						(
        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
    )
    snake_case_ :    str     			=						(
        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:
        snake_case_ :    str     			=						np.argmax(p.predictions,		axis=1       )
        return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE,		p.label_ids       )}

    # Data collator
    snake_case_ :    int     			=						DataCollatorWithPadding(__SCREAMING_SNAKE_CASE,		pad_to_multiple_of=8       ) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case_ :    Union[str, Any]     			=						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
    snake_case_ :    Tuple     			=						{}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"       )

        snake_case_ :    Optional[Any]     			=						trainer.evaluate()

        snake_case_ :    Optional[Any]     			=						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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()






'''simple docstring'''
from __future__ import annotations

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

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





 def __repr__(    self):
     return (
         F'Node: key: {self.key}, val: {self.val}, '
         F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
     )






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

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

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

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

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						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(lowercase_)
         return node.val
     self.miss += 1
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						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(lowercase_) 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
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

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





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						func(*lowercase_)
                 cls.decorator_function_to_instance_map[func].put(args[0] ,       lowercase_)
             return result

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

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

         return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




