

'''simple docstring'''

from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_				  =							logging.get_logger(__name__)


A_				  =							{
    "nielsr/canine-s": 20_48,
}

# Unicode defines 1,114,112 total “codepoints”
A_				  =							1_11_41_12

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A_				  =							0

A_				  =							0Xe_0_0_0
A_				  =							0Xe_0_0_1
A_				  =							0Xe_0_0_2
A_				  =							0Xe_0_0_3
A_				  =							0Xe_0_0_4

# Maps special codepoints to human-readable names.
A_				  =							{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
A_				  =							{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 							_snake_case       (				_a  ):
   _A				:   Union[str, Any]			      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   def __init__(       self	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					Tuple=chr(SCREAMING_SNAKE_CASE__							)   ,SCREAMING_SNAKE_CASE__	:					Optional[int]=chr(SCREAMING_SNAKE_CASE__							)   ,SCREAMING_SNAKE_CASE__	:					str=chr(SCREAMING_SNAKE_CASE__							)   ,SCREAMING_SNAKE_CASE__	:					List[Any]=chr(SCREAMING_SNAKE_CASE__							)   ,SCREAMING_SNAKE_CASE__	:					Tuple=chr(SCREAMING_SNAKE_CASE__							)   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=chr(SCREAMING_SNAKE_CASE__							)   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any]=False   ,SCREAMING_SNAKE_CASE__	:					Optional[int]=2_048   ,**SCREAMING_SNAKE_CASE__	:					List[str]   ,):
     SCREAMING_SNAKE_CASE:Tuple  					=    AddedToken(SCREAMING_SNAKE_CASE__   ,lstrip=SCREAMING_SNAKE_CASE__   ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__							) else bos_token
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    AddedToken(SCREAMING_SNAKE_CASE__   ,lstrip=SCREAMING_SNAKE_CASE__   ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__							) else eos_token
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    AddedToken(SCREAMING_SNAKE_CASE__   ,lstrip=SCREAMING_SNAKE_CASE__   ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__							) else sep_token
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    AddedToken(SCREAMING_SNAKE_CASE__   ,lstrip=SCREAMING_SNAKE_CASE__   ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__							) else cls_token
     SCREAMING_SNAKE_CASE:Tuple  					=    AddedToken(SCREAMING_SNAKE_CASE__   ,lstrip=SCREAMING_SNAKE_CASE__   ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__							) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE:Optional[int]  					=    AddedToken(SCREAMING_SNAKE_CASE__   ,lstrip=SCREAMING_SNAKE_CASE__   ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__							) else mask_token

     super().__init__(
         bos_token=SCREAMING_SNAKE_CASE__   ,eos_token=SCREAMING_SNAKE_CASE__   ,sep_token=SCREAMING_SNAKE_CASE__   ,cls_token=SCREAMING_SNAKE_CASE__   ,pad_token=SCREAMING_SNAKE_CASE__   ,mask_token=SCREAMING_SNAKE_CASE__   ,add_prefix_space=SCREAMING_SNAKE_CASE__   ,model_max_length=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,)

     # Creates a mapping for looking up the IDs of special symbols.
     SCREAMING_SNAKE_CASE:Tuple  					=    {}
     for codepoint, name in SPECIAL_CODEPOINTS.items():
       SCREAMING_SNAKE_CASE:Optional[Any]  					=    codepoint

     # Creates a mapping for looking up the string forms of special symbol IDs.
     SCREAMING_SNAKE_CASE:int  					=    {
         codepoint: name for name, codepoint in self._special_codepoints.items()
     }

     SCREAMING_SNAKE_CASE:Any  					=    UNICODE_VOCAB_SIZE
     SCREAMING_SNAKE_CASE:str  					=    len(self._special_codepoints							)
   @property
   def 			__UpperCamelCase  (       self	:					Union[str, Any]							):
     return self._unicode_vocab_size
   def 			__UpperCamelCase  (       self	:					Optional[Any]   ,SCREAMING_SNAKE_CASE__	:					Dict							):
     return list(SCREAMING_SNAKE_CASE__							)
   def 			__UpperCamelCase  (       self	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]							):



     try:
       return ord(SCREAMING_SNAKE_CASE__							)
     except TypeError:
       raise ValueError(F'''invalid token: \'{token}\''''							)
   def 			__UpperCamelCase  (       self	:					int   ,SCREAMING_SNAKE_CASE__	:					Dict							):



     try:
       if index in SPECIAL_CODEPOINTS:
         return SPECIAL_CODEPOINTS[index]
       return chr(SCREAMING_SNAKE_CASE__							)
     except TypeError:
       raise ValueError(F'''invalid id: {index}'''							)
   def 			__UpperCamelCase  (       self	:					Dict   ,SCREAMING_SNAKE_CASE__	:					Tuple							):
     return "".join(SCREAMING_SNAKE_CASE__							)
   def 			__UpperCamelCase  (       self	:					List[str]   ,SCREAMING_SNAKE_CASE__	:					Dict   ,SCREAMING_SNAKE_CASE__	:					Optional[int] = None							):
     SCREAMING_SNAKE_CASE:Any  					=    [self.sep_token_id]
     SCREAMING_SNAKE_CASE:Optional[int]  					=    [self.cls_token_id]

     SCREAMING_SNAKE_CASE:str  					=    cls + token_ids_a + sep
     if token_ids_a is not None:
       result += token_ids_a + sep
     return result
   def 			__UpperCamelCase  (       self	:					Dict   ,SCREAMING_SNAKE_CASE__	:					str   ,SCREAMING_SNAKE_CASE__	:					List[str] = None   ,SCREAMING_SNAKE_CASE__	:					Optional[Any] = False							):



     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=SCREAMING_SNAKE_CASE__   ,token_ids_a=SCREAMING_SNAKE_CASE__   ,already_has_special_tokens=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
     if token_ids_a is not None:
       result += ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
     return result
   def 			__UpperCamelCase  (       self	:					Dict   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any]   ,SCREAMING_SNAKE_CASE__	:					str = None							):
     SCREAMING_SNAKE_CASE:Optional[int]  					=    [self.sep_token_id]
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    [self.cls_token_id]

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    len(cls + token_ids_a + sep							) * [0]
     if token_ids_a is not None:
       result += len(token_ids_a + sep							) * [1]
     return result







   def 			__UpperCamelCase  (       self	:					str   ,SCREAMING_SNAKE_CASE__	:					int   ,SCREAMING_SNAKE_CASE__	:					Any = None							):
     return ()











'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


lowercase_		       =  "\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"

lowercase_		       =  "\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"

lowercase_		       =  "\\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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """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(__A)
     _a      =  np.array(__A)

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

     _a      =  label != ignore_index
     _a      =  np.not_equal(__A  ,	__A)
     _a      =  pred_label[mask]
     _a      =  np.array(__A)[mask]

     _a      =  pred_label[pred_label == label]

     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """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(__A  ,	__A):
          _a  ,    _a  ,    _a  ,    _a      =  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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a  ,    _a  ,    _a  ,    _a      =  total_intersect_and_union(
         __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)

     # 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(__A)
     _a      =  np.nanmean(__A)
     _a      =  all_acc
     _a      =  iou
     _a      =  acc

     if nan_to_num is not None:
          _a      =  {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 __A			(  datasets.Metric						):



 '''simple docstring'''







 def 					a__  (self       )    ->     List[str]:


      """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 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = None ,   A = False ,   )    ->     List[Any]:


      """simple docstring"""



      _a      =  mean_iou(
          results=A ,   gt_seg_maps=A ,   num_labels=A ,   ignore_index=A ,   nan_to_num=A ,   label_map=A ,   reduce_labels=A ,   )
      return iou_result



