"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

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


__lowerCamelCase											=  logging.getLogger(__name__)
@dataclass
class     UpperCamelCase__:
      lowerCAmelCase__  :						Optional[Any]													=		field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
      lowerCAmelCase__  :						Dict													=		field(
          default=_A		,						metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      lowerCAmelCase__  :						Dict													=		field(
          default='NER'		,						metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
      lowerCAmelCase__  :						List[str]													=		field(
          default=_A		,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
      lowerCAmelCase__  :						Tuple													=		field(default=_A		,						metadata={'help': 'Set this flag to use fast tokenization.'} )
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      lowerCAmelCase__  :						Optional[int]													=		field(
          default=_A		,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,						)






@dataclass
class     UpperCamelCase__:
      lowerCAmelCase__  :						Optional[Any]													=		field(
          metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
      lowerCAmelCase__  :						List[str]													=		field(
          default=_A		,						metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}		,						)
      lowerCAmelCase__  :						List[Any]													=		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.'
              )
          }		,						)
      lowerCAmelCase__  :						List[Any]													=		field(
          default=_A		,						metadata={'help': 'Overwrite the cached training and evaluation sets'} )






def   UpperCAmelCase					(	):






       """simple docstring"""





       A__	     =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
       if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              A__     ,  A__     ,  A__	     =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
       else:
              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.'				)

       A__	     =							import_module('tasks'				)
       try:
              A__	     =							getattr(A_       ,    model_args.task_type				)
              A__	     =							token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                  F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''				)

       # 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'       ,    A_				)

       # Set seed
       set_seed(training_args.seed				)

       # Prepare CONLL-2003 task
       A__	     =							token_classification_task.get_labels(data_args.labels				)
       A__	     =							dict(enumerate(A_				)				)
       A__	     =							len(A_				)

       # 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=A_       ,    idalabel=A_       ,    labelaid={label: i for i, label in enumerate(A_				)}       ,    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       ,    use_fast=model_args.use_fast       ,    )
       A__	     =							AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path       ,    from_tf=bool('.ckpt' in model_args.model_name_or_path				)       ,    config=A_       ,    cache_dir=model_args.cache_dir       ,    )

       # Get datasets
       A__	     =							(
           TokenClassificationDataset(
               token_classification_task=A_       ,    data_dir=data_args.data_dir       ,    tokenizer=A_       ,    labels=A_       ,    model_type=config.model_type       ,    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__	     =							(
           TokenClassificationDataset(
               token_classification_task=A_       ,    data_dir=data_args.data_dir       ,    tokenizer=A_       ,    labels=A_       ,    model_type=config.model_type       ,    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 align_predictions(UpperCamelCase__       ,    UpperCamelCase__				) -> Tuple[List[int], List[int]]:
              A__	     =							np.argmax(A_       ,    axis=2				)

              A__     ,  A__	     =							preds.shape

              A__	     =							[[] for _ in range(A_				)]
              A__	     =							[[] for _ in range(A_				)]

              for i in range(A_				):
                     for j in range(A_				):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]				)
                                   preds_list[i].append(label_map[preds[i][j]]				)

              return preds_list, out_label_list

       def compute_metrics(UpperCamelCase__				) -> Dict:
              A__     ,  A__	     =							align_predictions(p.predictions       ,    p.label_ids				)
              return {
                  "accuracy_score": accuracy_score(A_       ,    A_				),
                  "precision": precision_score(A_       ,    A_				),
                  "recall": recall_score(A_       ,    A_				),
                  "f1": fa_score(A_       ,    A_				),
              }

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

       # Initialize our Trainer
       A__	     =							Trainer(
           model=A_       ,    args=A_       ,    train_dataset=A_       ,    eval_dataset=A_       ,    compute_metrics=A_       ,    data_collator=A_       ,    )

       # 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_process_zero():
                     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_process_zero():
                     with open(A_       ,    'w'				) as writer:
                            logger.info('***** Eval results *****'				)
                            for key, value in result.items():
                                   logger.info('  %s = %s'       ,    A_       ,    A_				)
                                   writer.write('%s = %s\n' % (key, value)				)

                     results.update(A_				)

              # Predict
       if training_args.do_predict:
              A__	     =							TokenClassificationDataset(
                  token_classification_task=A_       ,    data_dir=data_args.data_dir       ,    tokenizer=A_       ,    labels=A_       ,    model_type=config.model_type       ,    max_seq_length=data_args.max_seq_length       ,    overwrite_cache=data_args.overwrite_cache       ,    mode=Split.test       ,    )

              A__     ,  A__     ,  A__	     =							trainer.predict(A_				)
              A__     ,  A__	     =							align_predictions(A_       ,    A_				)

              A__	     =							os.path.join(training_args.output_dir       ,    'test_results.txt'				)
              if trainer.is_world_process_zero():
                     with open(A_       ,    'w'				) as writer:
                            for key, value in metrics.items():
                                   logger.info('  %s = %s'       ,    A_       ,    A_				)
                                   writer.write('%s = %s\n' % (key, value)				)

                            # Save predictions
              A__	     =							os.path.join(training_args.output_dir       ,    'test_predictions.txt'				)
              if trainer.is_world_process_zero():
                     with open(A_       ,    'w'				) as writer:
                            with open(os.path.join(data_args.data_dir       ,    'test.txt'				)       ,    'r'				) as f:
                                   token_classification_task.write_predictions_to_file(A_       ,    A_       ,    A_				)

       return results






def   UpperCAmelCase					(	UpperCamelCase__				):






       """simple docstring"""





       main()


if __name__ == "__main__":
    main()






"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


__lowerCamelCase											=  "\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"

__lowerCamelCase											=  "\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"

__lowerCamelCase											=  "\\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					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





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

    # turn into Numpy arrays
							A__	     =							np.array(UpperCamelCase__				)
							A__	     =							np.array(UpperCamelCase__				)

							if reduce_labels:
														A__	     =							255
														A__	     =							label - 1
														A__	     =							255

							A__	     =							label != ignore_index
							A__	     =							np.not_equal(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							pred_label[mask]
							A__	     =							np.array(UpperCamelCase__				)[mask]

							A__	     =							pred_label[pred_label == label]

							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]

							A__	     =							area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							for result, gt_seg_map in zip(UpperCamelCase__       ,    UpperCamelCase__				):
														A__     ,  A__     ,  A__     ,  A__	     =							intersect_and_union(
														    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)
														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					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__     ,  A__     ,  A__     ,  A__	     =							total_intersect_and_union(
							    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)

							# compute metrics
							A__	     =							{}

							A__	     =							total_area_intersect.sum() / total_area_label.sum()
							A__	     =							total_area_intersect / total_area_union
							A__	     =							total_area_intersect / total_area_label

							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							all_acc
							A__	     =							iou
							A__	     =							acc

							if nan_to_num is not None:
														A__	     =							{metric: np.nan_to_num(UpperCamelCase__       ,    nan=UpperCamelCase__				) 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     )					->				str:
													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		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,)					->				Tuple:
													A__	     =							mean_iou(
													    results=__UpperCAmelCase		,gt_seg_maps=__UpperCAmelCase		,num_labels=__UpperCAmelCase		,ignore_index=__UpperCAmelCase		,nan_to_num=__UpperCAmelCase		,label_map=__UpperCAmelCase		,reduce_labels=__UpperCAmelCase		,)
													return iou_result





