
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_(					__magic_name__      :	Dict	,    __magic_name__      :	int	,    __magic_name__      :	Tuple	,    __magic_name__      :	List[str]	,    __magic_name__      :	List[Any] = None	,    __magic_name__      :	Optional[int] = False	,    ):


							"""simple docstring"""





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

    # turn into Numpy arrays
							_lowerCAmelCase :Dict      	=						np.array(__snake_case      )
							_lowerCAmelCase :Union[str, Any]      	=						np.array(__snake_case      )

							if reduce_labels:
														_lowerCAmelCase :Optional[Any]      	=						255
														_lowerCAmelCase :int      	=						label - 1
														_lowerCAmelCase :Union[str, Any]      	=						255

							_lowerCAmelCase :Any      	=						label != ignore_index
							_lowerCAmelCase :Optional[int]      	=						np.not_equal(__snake_case	,    __snake_case      )
							_lowerCAmelCase :int      	=						pred_label[mask]
							_lowerCAmelCase :Tuple      	=						np.array(__snake_case      )[mask]

							_lowerCAmelCase :Optional[int]      	=						pred_label[pred_label == label]

							_lowerCAmelCase :Tuple      	=						np.histogram(__snake_case	,    bins=__snake_case	,    range=(0, num_labels - 1)      )[0]
							_lowerCAmelCase :Any      	=						np.histogram(__snake_case	,    bins=__snake_case	,    range=(0, num_labels - 1)      )[0]
							_lowerCAmelCase :List[str]      	=						np.histogram(__snake_case	,    bins=__snake_case	,    range=(0, num_labels - 1)      )[0]

							_lowerCAmelCase :int      	=						area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label







def   UpperCamelCase_(					__magic_name__      :	Dict	,    __magic_name__      :	List[str]	,    __magic_name__      :	Optional[Any]	,    __magic_name__      :	Optional[int]	,    __magic_name__      :	Dict = None	,    __magic_name__      :	Optional[int] = False	,    ):


							"""simple docstring"""





							_lowerCAmelCase :Union[str, Any]      	=						np.zeros((num_labels,)	,    dtype=np.floataa      )
							_lowerCAmelCase :List[str]      	=						np.zeros((num_labels,)	,    dtype=np.floataa      )
							_lowerCAmelCase :Dict      	=						np.zeros((num_labels,)	,    dtype=np.floataa      )
							_lowerCAmelCase :str      	=						np.zeros((num_labels,)	,    dtype=np.floataa      )
							for result, gt_seg_map in zip(__snake_case	,    __snake_case      ):
														_lowerCAmelCase :Union[str, Any]      	=						intersect_and_union(
														    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __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_(					__magic_name__      :	Any	,    __magic_name__      :	str	,    __magic_name__      :	str	,    __magic_name__      :	Optional[int]	,    __magic_name__      :	Optional[int] = None	,    __magic_name__      :	Any = None	,    __magic_name__      :	Dict = False	,    ):


							"""simple docstring"""





							_lowerCAmelCase :str      	=						total_intersect_and_union(
							    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case      )

							# compute metrics
							_lowerCAmelCase :Optional[Any]      	=						{}

							_lowerCAmelCase :List[Any]      	=						total_area_intersect.sum() / total_area_label.sum()
							_lowerCAmelCase :List[Any]      	=						total_area_intersect / total_area_union
							_lowerCAmelCase :Tuple      	=						total_area_intersect / total_area_label

							_lowerCAmelCase :List[Any]      	=						np.nanmean(__snake_case      )
							_lowerCAmelCase :List[str]      	=						np.nanmean(__snake_case      )
							_lowerCAmelCase :List[Any]      	=						all_acc
							_lowerCAmelCase :Union[str, Any]      	=						iou
							_lowerCAmelCase :List[Any]      	=						acc

							if nan_to_num is not None:
														_lowerCAmelCase :List[str]      	=						{metric: np.nan_to_num(__snake_case	,    nan=__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							):
	"""simple docstring"""

	def       SCREAMING_SNAKE_CASE__     (     self:     Tuple			):
								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       SCREAMING_SNAKE_CASE__     (     self:     int							,   _UpperCAmelCase:     Optional[Any]							,   _UpperCAmelCase:     Dict							,   _UpperCAmelCase:     int							,   _UpperCAmelCase:     bool							,   _UpperCAmelCase:     Optional[int] = None							,   _UpperCAmelCase:     Optional[Dict[int, int]] = None							,   _UpperCAmelCase:     bool = False							,   ):
								_lowerCAmelCase :Optional[Any]      	=						mean_iou(
								    results=_SCREAMING_SNAKE_CASE							,   gt_seg_maps=_SCREAMING_SNAKE_CASE							,   num_labels=_SCREAMING_SNAKE_CASE							,   ignore_index=_SCREAMING_SNAKE_CASE							,   nan_to_num=_SCREAMING_SNAKE_CASE							,   label_map=_SCREAMING_SNAKE_CASE							,   reduce_labels=_SCREAMING_SNAKE_CASE							,   )
								return iou_result




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     decoder_start_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )