




"""simple docstring"""

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 		_snake_case	(   _snake_case				:   Any							,    _snake_case				:   Dict							,    _snake_case				:   Any							,    _snake_case				:   bool							,    _snake_case				:   Optional[Dict[int, int]] = None							,    _snake_case				:   bool = False							,    )       ->   List[Any]:






      '''simple docstring'''


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

    # turn into Numpy arrays
      _A     =  np.array(_snake_case				)
      _A     =  np.array(_snake_case				)

      if reduce_labels:
            _A     =  2_55
            _A     =  label - 1
            _A     =  2_55

      _A     =  label != ignore_index
      _A     =  np.not_equal(_snake_case							,    _snake_case				)
      _A     =  pred_label[mask]
      _A     =  np.array(_snake_case				)[mask]

      _A     =  pred_label[pred_label == label]

      _A     =  np.histogram(_snake_case							,    bins=_snake_case							,    range=(0, num_labels - 1)				)[0]
      _A     =  np.histogram(_snake_case							,    bins=_snake_case							,    range=(0, num_labels - 1)				)[0]
      _A     =  np.histogram(_snake_case							,    bins=_snake_case							,    range=(0, num_labels - 1)				)[0]

      _A     =  area_pred_label + area_label - area_intersect

      return area_intersect, area_union, area_pred_label, area_label






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   List[Any]							,    _snake_case				:   bool							,    _snake_case				:   Optional[Dict[int, int]] = None							,    _snake_case				:   bool = False							,    )       ->   str:






      '''simple docstring'''


      _A     =  np.zeros((num_labels,)							,    dtype=np.floataa				)
      _A     =  np.zeros((num_labels,)							,    dtype=np.floataa				)
      _A     =  np.zeros((num_labels,)							,    dtype=np.floataa				)
      _A     =  np.zeros((num_labels,)							,    dtype=np.floataa				)
      for result, gt_seg_map in zip(_snake_case							,    _snake_case				):
            _A  ,       _A  ,       _A  ,       _A     =  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 		_snake_case	(   _snake_case				:   Tuple							,    _snake_case				:   Optional[int]							,    _snake_case				:   List[Any]							,    _snake_case				:   bool							,    _snake_case				:   Optional[int] = None							,    _snake_case				:   Optional[Dict[int, int]] = None							,    _snake_case				:   bool = False							,    )       ->   int:






      '''simple docstring'''


      _A  ,       _A  ,       _A  ,       _A     =  total_intersect_and_union(
          _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case				)

      # compute metrics
      _A     =  {}

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

      _A     =  np.nanmean(_snake_case				)
      _A     =  np.nanmean(_snake_case				)
      _A     =  all_acc
      _A     =  iou
      _A     =  acc

      if nan_to_num is not None:
            _A     =  {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 							lowercase_			(	datasets.Metric				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Any						):
       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			:			Optional[int]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			bool       ,				_UpperCAmelCase			:			Optional[int] = None       ,				_UpperCAmelCase			:			Optional[Dict[int, int]] = None       ,				_UpperCAmelCase			:			bool = False       ,				):
       _A     =  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







"""simple docstring"""

import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


a							      =     logging.get_logger(__name__)

a							      =     {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict=None       ,				**_UpperCAmelCase			:			Optional[Any]						):
       logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
       _A     =  model
       _A     =  kwargs.get('model_save_dir'       ,				_UpperCAmelCase						)
       _A     =  kwargs.get('latest_model_name'       ,				_UpperCAmelCase						)

 def __call__(					self			:			Dict       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  {k: np.array(_UpperCAmelCase						) for k, v in kwargs.items()}
       return self.model.run(_UpperCAmelCase       ,				_UpperCAmelCase						)

 @staticmethod
 def      lowerCAmelCase_				(					_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[Any]=None						):
       if provider is None:
             logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
             _A     =  'CPUExecutionProvider'

       return ort.InferenceSession(_UpperCAmelCase       ,				providers=[provider]       ,				sess_options=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

       _A     =  self.model_save_dir.joinpath(self.latest_model_name						)
       _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
       try:
             shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
       except shutil.SameFileError:
             pass

       # copy external weights (for models >2GB)
       _A     =  self.model_save_dir.joinpath(_UpperCAmelCase						)
       if src_path.exists():
             _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
             try:
                   shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
             except shutil.SameFileError:
                   pass

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, os.PathLike]       ,				**_UpperCAmelCase			:			List[str]       ,				):
       if os.path.isfile(_UpperCAmelCase						):
             logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
             return

       os.makedirs(_UpperCAmelCase       ,				exist_ok=_UpperCAmelCase						)

       # saving model weights/files
       self._save_pretrained(_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @classmethod
 def      lowerCAmelCase_				(					cls			:			Tuple       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[Union[bool, str, None]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, None]] = None       ,				_UpperCAmelCase			:			bool = False       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional["ort.SessionOptions"] = None       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(_UpperCAmelCase						):
             _A     =  OnnxRuntimeModel.load_model(
                 os.path.join(_UpperCAmelCase       ,				_UpperCAmelCase						)       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
             _A     =  Path(_UpperCAmelCase						)
       # load model from hub
       else:
             # download model
             _A     =  hf_hub_download(
                 repo_id=_UpperCAmelCase       ,				filename=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				)
             _A     =  Path(_UpperCAmelCase						).parent
             _A     =  Path(_UpperCAmelCase						).name
             _A     =  OnnxRuntimeModel.load_model(_UpperCAmelCase       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
       return cls(model=_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  None
       if len(str(_UpperCAmelCase						).split('@'						)						) == 2:
             _A  ,       _A     =  model_id.split('@'						)

       return cls._from_pretrained(
           model_id=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

