


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		int        =	'''bert'''


	def __init__(						self							,    lowercase__=3_0_5_2_2							,    lowercase__=7_6_8							,    lowercase__=1_2							,    lowercase__=1_2							,    lowercase__=3_0_7_2							,    lowercase__="gelu"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=5_1_2							,    lowercase__=2							,    lowercase__=0.0_2							,    lowercase__=1e-12							,    lowercase__=0							,    lowercase__="absolute"							,    lowercase__=True							,    lowercase__=None							,    **lowercase__							,    ):
							super().__init__(pad_token_id=lowercase__							,    **lowercase__)

							__UpperCAmelCase :       Tuple  						=  vocab_size
							__UpperCAmelCase :       Any  						=  hidden_size
							__UpperCAmelCase :       str  						=  num_hidden_layers
							__UpperCAmelCase :       Dict  						=  num_attention_heads
							__UpperCAmelCase :       Tuple  						=  hidden_act
							__UpperCAmelCase :       int  						=  intermediate_size
							__UpperCAmelCase :       List[Any]  						=  hidden_dropout_prob
							__UpperCAmelCase :       Union[str, Any]  						=  attention_probs_dropout_prob
							__UpperCAmelCase :       List[Any]  						=  max_position_embeddings
							__UpperCAmelCase :       Union[str, Any]  						=  type_vocab_size
							__UpperCAmelCase :       List[Any]  						=  initializer_range
							__UpperCAmelCase :       List[Any]  						=  layer_norm_eps
							__UpperCAmelCase :       List[str]  						=  position_embedding_type
							__UpperCAmelCase :       Optional[Any]  						=  use_cache
							__UpperCAmelCase :       List[Any]  						=  classifier_dropout






class        lowerCamelCase      (     _UpperCamelCase   ):


	@property
	def    A(						self):
							if self.task == "multiple-choice":
													__UpperCAmelCase :       Optional[int]  						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
													__UpperCAmelCase :       Optional[Any]  						=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ])





from typing import Dict, Optional

import numpy as np

import datasets


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

lowerCAmelCase							=						"""
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'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.        ])}
"""

lowerCAmelCase							=						"""\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Optional[Any]:




						'''simple docstring'''







						if label_map is not None:
												for old_id, new_id in label_map.items():
																		__UpperCAmelCase :       List[str]  						=  new_id

    # turn into Numpy arrays
						__UpperCAmelCase :       Tuple  						=  np.array(lowercase_			)
						__UpperCAmelCase :       str  						=  np.array(lowercase_			)

						if reduce_labels:
												__UpperCAmelCase :       List[Any]  						=  255
												__UpperCAmelCase :       str  						=  label - 1
												__UpperCAmelCase :       Dict  						=  255

						__UpperCAmelCase :       str  						=  label != ignore_index
						__UpperCAmelCase :       Optional[int]  						=  np.not_equal(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       List[str]  						=  pred_label[mask]
						__UpperCAmelCase :       Any  						=  np.array(lowercase_			)[mask]

						__UpperCAmelCase :       Optional[Any]  						=  pred_label[pred_label == label]

						__UpperCAmelCase :       Optional[Any]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       Any  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       List[str]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]

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

						return area_intersect, area_union, area_pred_label, area_label




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						for result, gt_seg_map in zip(lowercase_			,					lowercase_			):
												__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  intersect_and_union(
												    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)
												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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = None			,					lowercase_ = False			,					)		->    str:




						'''simple docstring'''







						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  total_intersect_and_union(
						    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)

						# compute metrics
						__UpperCAmelCase :       Any  						=  {}

						__UpperCAmelCase :       Union[str, Any]  						=  total_area_intersect.sum() / total_area_label.sum()
						__UpperCAmelCase :       Optional[Any]  						=  total_area_intersect / total_area_union
						__UpperCAmelCase :       List[str]  						=  total_area_intersect / total_area_label

						__UpperCAmelCase :       Optional[int]  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       int  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       List[str]  						=  all_acc
						__UpperCAmelCase :       Any  						=  iou
						__UpperCAmelCase :       str  						=  acc

						if nan_to_num is not None:
												__UpperCAmelCase :       Any  						=  {metric: np.nan_to_num(lowercase_			,					nan=lowercase_			) for metric, metric_value in metrics.items()}

						return metrics





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


	def    A(						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    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = False							,    ):
							__UpperCAmelCase :       str  						=  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

