






from typing import Dict, Optional

import numpy as np

import datasets


a_							=    '''
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.
'''

a_							=    '''
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.        ])}
'''

a_							=    '''\
@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_					:				Optional[Any]    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				bool    ,   UpperCamelCase_					:				Optional[Dict[int, int]] = None    ,   UpperCamelCase_					:				bool = False    ,   ) ->   Union[str, Any]:
						"""simple docstring"""







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

    # turn into Numpy arrays
						lowerCAmelCase__       =						np.array(UpperCamelCase_   )
						lowerCAmelCase__       =						np.array(UpperCamelCase_   )

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

						lowerCAmelCase__       =						label != ignore_index
						lowerCAmelCase__       =						np.not_equal(UpperCamelCase_    ,   UpperCamelCase_   )
						lowerCAmelCase__       =						pred_label[mask]
						lowerCAmelCase__       =						np.array(UpperCamelCase_   )[mask]

						lowerCAmelCase__       =						pred_label[pred_label == label]

						lowerCAmelCase__       =						np.histogram(UpperCamelCase_    ,   bins=UpperCamelCase_    ,   range=(0, num_labels - 1)   )[0]
						lowerCAmelCase__       =						np.histogram(UpperCamelCase_    ,   bins=UpperCamelCase_    ,   range=(0, num_labels - 1)   )[0]
						lowerCAmelCase__       =						np.histogram(UpperCamelCase_    ,   bins=UpperCamelCase_    ,   range=(0, num_labels - 1)   )[0]

						lowerCAmelCase__       =						area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label


def        _a  (     UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				bool    ,   UpperCamelCase_					:				Optional[Dict[int, int]] = None    ,   UpperCamelCase_					:				bool = False    ,   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						np.zeros((num_labels,)    ,   dtype=np.floataa   )
						lowerCAmelCase__       =						np.zeros((num_labels,)    ,   dtype=np.floataa   )
						lowerCAmelCase__       =						np.zeros((num_labels,)    ,   dtype=np.floataa   )
						lowerCAmelCase__       =						np.zeros((num_labels,)    ,   dtype=np.floataa   )
						for result, gt_seg_map in zip(UpperCamelCase_    ,   UpperCamelCase_   ):
												lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						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_					:				Tuple    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				bool    ,   UpperCamelCase_					:				Optional[int] = None    ,   UpperCamelCase_					:				Optional[Dict[int, int]] = None    ,   UpperCamelCase_					:				bool = False    ,   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						total_intersect_and_union(
						    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )

						# compute metrics
						lowerCAmelCase__       =						{}

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

						lowerCAmelCase__       =						np.nanmean(UpperCamelCase_   )
						lowerCAmelCase__       =						np.nanmean(UpperCamelCase_   )
						lowerCAmelCase__       =						all_acc
						lowerCAmelCase__       =						iou
						lowerCAmelCase__       =						acc

						if nan_to_num is not None:
												lowerCAmelCase__       =						{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	):

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							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 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = False		,    )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						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














from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





							super().__init__()
							self.register_modules(transformer=__UpperCAmelCase		,    vae=__UpperCAmelCase		,    scheduler=__UpperCAmelCase       )

							# create a imagenet -> id dictionary for easier use
							lowerCAmelCase__       =						{}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(","       ):
																									lowerCAmelCase__       =						int(__UpperCAmelCase       )
													lowerCAmelCase__       =						dict(sorted(self.labels.items()       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[int]:







							'''simple docstring'''





							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						list(__UpperCAmelCase       )

							for l in label:
													if l not in self.labels:
																			raise ValueError(
																			    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."       )

							return [self.labels[l] for l in label]


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 4.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.transformer.config.sample_size
							lowerCAmelCase__       =						self.transformer.config.in_channels

							lowerCAmelCase__       =						randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=self.transformer.dtype		,    )
							lowerCAmelCase__       =						torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

							lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    device=self.device       ).reshape(-1       )
							lowerCAmelCase__       =						torch.tensor([1000] * batch_size		,    device=self.device       )
							lowerCAmelCase__       =						torch.cat([class_labels, class_null]		,    0       ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													if guidance_scale > 1:
																			lowerCAmelCase__       =						latent_model_input[: len(__UpperCAmelCase       ) // 2]
																			lowerCAmelCase__       =						torch.cat([half, half]		,    dim=0       )
													lowerCAmelCase__       =						self.scheduler.scale_model_input(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						t
													if not torch.is_tensor(__UpperCAmelCase       ):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			lowerCAmelCase__       =						latent_model_input.device.type == "mps"
																			if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									lowerCAmelCase__       =						torch.floataa if is_mps else torch.floataa
																			else:
																									lowerCAmelCase__       =						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__       =						torch.tensor([timesteps]		,    dtype=__UpperCAmelCase		,    device=latent_model_input.device       )
													elif len(timesteps.shape       ) == 0:
																			lowerCAmelCase__       =						timesteps[None].to(latent_model_input.device       )
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													lowerCAmelCase__       =						timesteps.expand(latent_model_input.shape[0]       )
													# predict noise model_output
													lowerCAmelCase__       =						self.transformer(
													    __UpperCAmelCase		,    timestep=__UpperCAmelCase		,    class_labels=__UpperCAmelCase       ).sample

													# perform guidance
													if guidance_scale > 1:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    len(__UpperCAmelCase       ) // 2		,    dim=0       )

																			lowerCAmelCase__       =						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__       =						torch.cat([half_eps, half_eps]		,    dim=0       )

																			lowerCAmelCase__       =						torch.cat([eps, rest]		,    dim=1       )

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    __UpperCAmelCase		,    dim=1       )
													else:
																			lowerCAmelCase__       =						noise_pred

													# compute previous image: x_t -> x_t-1
													lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ).prev_sample

							if guidance_scale > 1:
													lowerCAmelCase__      ,							lowerCAmelCase__       =						latent_model_input.chunk(2		,    dim=0       )
							else:
													lowerCAmelCase__       =						latent_model_input

							lowerCAmelCase__       =						1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

							lowerCAmelCase__       =						(samples / 2 + 0.5).clamp(0		,    1       )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__       =						samples.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )






