






'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


A:      Optional[Any]  				=				logging.get_logger(__name__)






def 	_UpperCAmelCase    (  )			->			str:





						"""simple docstring"""
						lowercase_      :   Union[str, Any]        =			os.getenv('SM_HP_MP_PARAMETERS'						,			'{}'							)
						try:
												# Parse it and check the field "partitions" is included, it is required for model parallel.
												lowercase_      :   int        =			json.loads(_lowercase							)
												if "partitions" not in smp_options:
																		return False
						except json.JSONDecodeError:
												return False

						# Get the sagemaker specific framework parameters from mpi_options variable.
						lowercase_      :   Optional[Any]        =			os.getenv('SM_FRAMEWORK_PARAMS'						,			'{}'							)
						try:
												# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
												lowercase_      :   Dict        =			json.loads(_lowercase							)
												if not mpi_options.get('sagemaker_mpi_enabled'						,			_lowercase							):
																		return False
						except json.JSONDecodeError:
												return False
						# Lastly, check if the `smdistributed` module is present.
						return importlib.util.find_spec('smdistributed'							) is not None


if is_sagemaker_model_parallel_available():
						import smdistributed.modelparallel.torch as smp

						smp.init()






@dataclass
class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      str        =  field(
	    default='',						metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'},						)
	def        lowerCamelCase__		(							self       )						->  Tuple:
							super().__post_init__()
							warnings.warn(
							    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
							    '`TrainingArguments` instead.'  ,      __A  ,      )
	@cached_property
	def        lowerCamelCase__		(							self       )						->  "torch.device":
							logger.info('PyTorch: setting up devices'       )
							if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    'torch.distributed process group is initialized, but local_rank == -1. '
													    'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'       )
							if self.no_cuda:
													lowercase_      :   List[Any]        =			torch.device('cpu'       )
													lowercase_      :   str        =			0
							elif is_sagemaker_model_parallel_available():
													lowercase_      :   Tuple        =			smp.local_rank()
													lowercase_      :   List[Any]        =			torch.device('cuda'  ,      __A       )
													lowercase_      :   Union[str, Any]        =			1
							elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='smddp'  ,      timeout=self.ddp_timeout_delta       )
													lowercase_      :   int        =			int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'       )       )
													lowercase_      :   Tuple        =			torch.device('cuda'  ,      self.local_rank       )
													lowercase_      :   Optional[int]        =			1
							elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													lowercase_      :   Union[str, Any]        =			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'       )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													lowercase_      :   Dict        =			torch.cuda.device_count()
							else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																			torch.distributed.init_process_group(backend='nccl'  ,      timeout=self.ddp_timeout_delta       )
													lowercase_      :   Dict        =			torch.device('cuda'  ,      self.local_rank       )
													lowercase_      :   int        =			1

							if device.type == "cuda":
													torch.cuda.set_device(__A       )

							return device
	@property
	def        lowerCamelCase__		(							self       )						->  Tuple:
							if is_sagemaker_model_parallel_available():
													return smp.dp_size()

							return super().world_size
	@property
	def        lowerCamelCase__		(							self       )						->  Any:
							return not is_sagemaker_model_parallel_available()







	@property
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							return False








'''simple docstring'''

from typing import Dict, Optional

import numpy as np

import datasets


A:      int  				=				"\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:      List[str]  				=				"\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:      Union[str, Any]  				=				"\\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    (  a		:	str						,			a		:	Union[str, Any]						,			a		:	Dict						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			str:





						"""simple docstring"""
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		lowercase_      :   Union[str, Any]        =			new_id

    # turn into Numpy arrays
						lowercase_      :   List[Any]        =			np.array(a							)
						lowercase_      :   Optional[Any]        =			np.array(a							)

						if reduce_labels:
												lowercase_      :   Any        =			2_5_5
												lowercase_      :   Dict        =			label - 1
												lowercase_      :   List[Any]        =			2_5_5

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

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

						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Dict        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]

						lowercase_      :   Optional[Any]        =			area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label






def 	_UpperCAmelCase    (  a		:	int						,			a		:	Optional[Any]						,			a		:	Optional[int]						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Dict:





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





						"""simple docstring"""
						lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Optional[Any]        =			total_intersect_and_union(
						    a						,			a						,			a						,			a						,			a						,			a							)

						# compute metrics
						lowercase_      :   str        =			{}

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

						lowercase_      :   Any        =			np.nanmean(a							)
						lowercase_      :   Optional[Any]        =			np.nanmean(a							)
						lowercase_      :   int        =			all_acc
						lowercase_      :   Union[str, Any]        =			iou
						lowercase_      :   Optional[Any]        =			acc

						if nan_to_num is not None:
												lowercase_      :   Optional[int]        =			{metric: np.nan_to_num(a						,			nan=a							) for metric, metric_value in metrics.items()}

						return metrics






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







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      )						->  Tuple:
							lowercase_      :   Optional[int]        =			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
