



from typing import Dict, Optional

import numpy as np

import datasets


lowerCAmelCase							:  Dict            =						"""
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							:  int            =						"""
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							:  List[str]            =						"""\
@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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = False    ,     ):
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		SCREAMING_SNAKE_CASE_:			Dict  		=	new_id

    # turn into Numpy arrays
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.array(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	np.array(_UpperCAmelCase   )

						if reduce_labels:
												SCREAMING_SNAKE_CASE_:			Any  		=	2_55
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	label - 1
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	2_55

						SCREAMING_SNAKE_CASE_:			Tuple  		=	label != ignore_index
						SCREAMING_SNAKE_CASE_:			List[str]  		=	np.not_equal(_UpperCAmelCase    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	pred_label[mask]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.array(_UpperCAmelCase   )[mask]

						SCREAMING_SNAKE_CASE_:			str  		=	pred_label[pred_label == label]

						SCREAMING_SNAKE_CASE_:			Tuple  		=	np.histogram(_UpperCAmelCase    ,     bins=_UpperCAmelCase    ,     range=(0, num_labels - 1)   )[0]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.histogram(_UpperCAmelCase    ,     bins=_UpperCAmelCase    ,     range=(0, num_labels - 1)   )[0]
						SCREAMING_SNAKE_CASE_:			Any  		=	np.histogram(_UpperCAmelCase    ,     bins=_UpperCAmelCase    ,     range=(0, num_labels - 1)   )[0]

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = False    ,     ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						SCREAMING_SNAKE_CASE_:			str  		=	np.zeros((num_labels,)    ,     dtype=np.floataa   )
						for result, gt_seg_map in zip(_UpperCAmelCase    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			Tuple  		=	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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = False    ,     ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	total_intersect_and_union(
						    _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

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

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	total_area_intersect.sum() / total_area_label.sum()
						SCREAMING_SNAKE_CASE_:			int  		=	total_area_intersect / total_area_union
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	total_area_intersect / total_area_label

						SCREAMING_SNAKE_CASE_:			Any  		=	np.nanmean(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.nanmean(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	all_acc
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	iou
						SCREAMING_SNAKE_CASE_:			str  		=	acc

						if nan_to_num is not None:
												SCREAMING_SNAKE_CASE_:			Dict  		=	{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   __lowercase    (			datasets.Metric     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									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  :					str       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					bool       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Optional[Dict[int, int]] = None       ,						lowerCAmelCase__  :					bool = False       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	mean_iou(
									    results=lowerCAmelCase__       ,						gt_seg_maps=lowerCAmelCase__       ,						num_labels=lowerCAmelCase__       ,						ignore_index=lowerCAmelCase__       ,						nan_to_num=lowerCAmelCase__       ,						label_map=lowerCAmelCase__       ,						reduce_labels=lowerCAmelCase__       ,						)
									return iou_result









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCAmelCase							:  Tuple            =						{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
						lowerCAmelCase							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

						env.seed(0)
						lowerCAmelCase							:  Optional[Any]            =						env.reset()
						lowerCAmelCase							:  Union[str, Any]            =						0
						lowerCAmelCase							:  Optional[int]            =						0
						lowerCAmelCase							:  Optional[int]            =						1000
						lowerCAmelCase							:  Dict            =						[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		lowerCAmelCase							:  Union[str, Any]            =						pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																		    f''' {total_score}'''
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

						print(f'''Total reward: {total_reward}''')






