
'''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_ :    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 typing import Dict, Optional

import numpy as np

import datasets


a_											=						"\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

a_											=						"\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

a_											=						"\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    if label_map is not None:
        for old_id, new_id in label_map.items():
            snake_case_ :    int     			=						new_id

    # turn into Numpy arrays
    snake_case_ :    int     			=						np.array(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						np.array(__SCREAMING_SNAKE_CASE       )

    if reduce_labels:
        snake_case_ :    str     			=						2_5_5
        snake_case_ :    str     			=						label - 1
        snake_case_ :    List[Any]     			=						2_5_5

    snake_case_ :    Union[str, Any]     			=						label != ignore_index
    snake_case_ :    Dict     			=						np.not_equal(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						pred_label[mask]
    snake_case_ :    Any     			=						np.array(__SCREAMING_SNAKE_CASE       )[mask]

    snake_case_ :    List[str]     			=						pred_label[pred_label == label]

    snake_case_ :    int     			=						np.histogram(__SCREAMING_SNAKE_CASE,		bins=__SCREAMING_SNAKE_CASE,		range=(0, num_labels - 1)       )[0]
    snake_case_ :    Tuple     			=						np.histogram(__SCREAMING_SNAKE_CASE,		bins=__SCREAMING_SNAKE_CASE,		range=(0, num_labels - 1)       )[0]
    snake_case_ :    List[Any]     			=						np.histogram(__SCREAMING_SNAKE_CASE,		bins=__SCREAMING_SNAKE_CASE,		range=(0, num_labels - 1)       )[0]

    snake_case_ :    List[Any]     			=						area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    List[str]     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    int     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    int     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    for result, gt_seg_map in zip(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_			, snake_case_ :    int     			=						intersect_and_union(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        total_area_intersect += area_intersect
        total_area_union += area_union
        total_area_pred_label += area_pred_label
        total_area_label += area_label
    return total_area_intersect, total_area_union, total_area_pred_label, total_area_label

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    snake_case_			, snake_case_			, snake_case_			, snake_case_ :    Tuple     			=						total_intersect_and_union(
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    # compute metrics
    snake_case_ :    Union[str, Any]     			=						{}

    snake_case_ :    int     			=						total_area_intersect.sum() / total_area_label.sum()
    snake_case_ :    str     			=						total_area_intersect / total_area_union
    snake_case_ :    Union[str, Any]     			=						total_area_intersect / total_area_label

    snake_case_ :    List[str]     			=						np.nanmean(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						np.nanmean(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						all_acc
    snake_case_ :    Union[str, Any]     			=						iou
    snake_case_ :    Tuple     			=						acc

    if nan_to_num is not None:
        snake_case_ :    str     			=						{metric: np.nan_to_num(__SCREAMING_SNAKE_CASE,		nan=__SCREAMING_SNAKE_CASE       ) for metric, metric_value in metrics.items()}

    return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION					)
class    UpperCAmelCase_				(	datasets.Metric					):





 def        snake_case__		(    self):
     return datasets.MetricInfo(
         description=_DESCRIPTION ,       citation=_CITATION ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
             # 1st Seq - height dim, 2nd - width dim
             {
                 "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
                 "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
             }) ,       reference_urls=[
             "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
         ] ,       )





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = False ,       ):
     snake_case_ :    Optional[Any]     			=						mean_iou(
         results=lowercase_ ,       gt_seg_maps=lowercase_ ,       num_labels=lowercase_ ,       ignore_index=lowercase_ ,       nan_to_num=lowercase_ ,       label_map=lowercase_ ,       reduce_labels=lowercase_ ,       )
     return iou_result




