

"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


SCREAMING_SNAKE_CASE_					:	Tuple							  =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_					:	Any							  =						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

SCREAMING_SNAKE_CASE_					:	str							  =						{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}





@lru_cache()
def  _snake_case     (    ):
     A__										=  (
         list(range(ord("""!"""			)					,   ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)					,   ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)					,   ord("""ÿ"""			) + 1			)			)
     )
     A__										=  bs[:]
     A__										=  0
     for b in range(2**8			):
          if b not in bs:
               bs.append(UpperCAmelCase_			)
               cs.append(2**8 + n			)
               n += 1
     A__										=  [chr(UpperCAmelCase_			) for n in cs]
     return dict(zip(UpperCAmelCase_					,   UpperCAmelCase_			)			)





def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]			):
     A__										=  set()
     A__										=  word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)			)
          A__										=  char
     return pairs



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					VOCAB_FILES_NAMES
    UpperCAmelCase						 =					PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase						 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase						 =					["input_ids", "attention_mask"]

    def __init__(     self: List[Any]    ,						UpperCamelCase: int    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Dict="replace"    ,						UpperCamelCase: Union[str, Any]="<s>"    ,						UpperCamelCase: Tuple="</s>"    ,						UpperCamelCase: Union[str, Any]="</s>"    ,						UpperCamelCase: Optional[int]="<s>"    ,						UpperCamelCase: Any="<unk>"    ,						UpperCamelCase: int="<pad>"    ,						UpperCamelCase: Union[str, Any]="<mask>"    ,						UpperCamelCase: Optional[int]=False    ,						**UpperCamelCase: List[str]    ,						):






         """simple docstring"""


         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else bos_token
         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else eos_token
         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else sep_token
         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else cls_token
         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else unk_token
         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else mask_token

         super().__init__(
             errors=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						sep_token=UpperCamelCase    ,						cls_token=UpperCamelCase    ,						pad_token=UpperCamelCase    ,						mask_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

         with open(UpperCamelCase    ,						encoding="""utf-8"""       ) as vocab_handle:
              A__										=  json.load(UpperCamelCase       )
         A__										=  {v: k for k, v in self.encoder.items()}
         A__										=  errors  # how to handle errors in decoding
         A__										=  bytes_to_unicode()
         A__										=  {v: k for k, v in self.byte_encoder.items()}
         with open(UpperCamelCase    ,						encoding="""utf-8"""       ) as merges_handle:
              A__										=  merges_handle.read().split("""\n"""       )[1:-1]
         A__										=  [tuple(merge.split()       ) for merge in bpe_merges]
         A__										=  dict(zip(UpperCamelCase    ,						range(len(UpperCamelCase       )       )       )       )
         A__										=  {}
         A__										=  add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         A__										=  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )

    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         return len(self.encoder       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         return dict(self.encoder    ,						**self.added_tokens_encoder       )

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         if token in self.cache:
              return self.cache[token]
         A__										=  tuple(UpperCamelCase       )
         A__										=  get_pairs(UpperCamelCase       )

         if not pairs:
              return token

         while True:
              A__										=  min(UpperCamelCase    ,						key=lambda UpperCamelCase       : self.bpe_ranks.get(UpperCamelCase    ,						float("""inf"""       )       )       )
              if bigram not in self.bpe_ranks:
                   break
              A__			,				A__										=  bigram
              A__										=  []
              A__										=  0
              while i < len(UpperCamelCase       ):
                   try:
                        A__										=  word.index(UpperCamelCase    ,						UpperCamelCase       )
                   except ValueError:
                        new_word.extend(word[i:]       )
                        break
                   else:
                        new_word.extend(word[i:j]       )
                        A__										=  j

                   if word[i] == first and i < len(UpperCamelCase       ) - 1 and word[i + 1] == second:
                        new_word.append(first + second       )
                        i += 2
                   else:
                        new_word.append(word[i]       )
                        i += 1
              A__										=  tuple(UpperCamelCase       )
              A__										=  new_word
              if len(UpperCamelCase       ) == 1:
                   break
              else:
                   A__										=  get_pairs(UpperCamelCase       )
         A__										=  """ """.join(UpperCamelCase       )
         A__										=  word
         return word

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: int       ):






         """simple docstring"""


         A__										=  []
         for token in re.findall(self.pat    ,						UpperCamelCase       ):
              A__										=  """""".join(
                  self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase       ).split(""" """       )       )
         return bpe_tokens

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         return self.encoder.get(UpperCamelCase    ,						self.encoder.get(self.unk_token       )       )

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: str       ):






         """simple docstring"""


         return self.decoder.get(UpperCamelCase       )

    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple       ):






         """simple docstring"""


         A__										=  """""".join(UpperCamelCase       )
         A__										=  bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""    ,						errors=self.errors       )
         return text

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[str] = None       ):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase       ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
              return
         A__										=  os.path.join(
             UpperCamelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
         A__										=  os.path.join(
             UpperCamelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

         with open(UpperCamelCase    ,						"""w"""    ,						encoding="""utf-8"""       ) as f:
              f.write(json.dumps(self.encoder    ,						indent=2    ,						sort_keys=UpperCamelCase    ,						ensure_ascii=UpperCamelCase       ) + """\n"""       )

         A__										=  0
         with open(UpperCamelCase    ,						"""w"""    ,						encoding="""utf-8"""       ) as writer:
              writer.write("""#version: 0.2\n"""       )
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,						key=lambda UpperCamelCase       : kv[1]       ):
                   if index != token_index:
                        logger.warning(
                            f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                            """ Please check that the tokenizer is not corrupted!"""       )
                        A__										=  token_index
                   writer.write(""" """.join(UpperCamelCase       ) + """\n"""       )
                   index += 1

         return vocab_file, merge_file

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None       ):






         """simple docstring"""


         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__										=  [self.cls_token_id]
         A__										=  [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None    ,						UpperCamelCase: bool = False       ):






         """simple docstring"""


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

         if token_ids_a is None:
              return [1] + ([0] * len(UpperCamelCase       )) + [1]
         return [1] + ([0] * len(UpperCamelCase       )) + [1, 1] + ([0] * len(UpperCamelCase       )) + [1]

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None       ):






         """simple docstring"""


         A__										=  [self.sep_token_id]
         A__										=  [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]





    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Union[str, Any]=False    ,						**UpperCamelCase: Tuple       ):






         """simple docstring"""


         A__										=  kwargs.pop("""add_prefix_space"""    ,						self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase       ) > 0 and not text[0].isspace()):
              A__										=  """ """ + text
         return (text, kwargs)









"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE_					:	Tuple							  =						'\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'

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'\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'

SCREAMING_SNAKE_CASE_					:	int							  =						'\\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  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     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(UpperCAmelCase_			)
     A__										=  np.array(UpperCAmelCase_			)

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

     A__										=  label != ignore_index
     A__										=  np.not_equal(UpperCAmelCase_					,   UpperCAmelCase_			)
     A__										=  pred_label[mask]
     A__										=  np.array(UpperCAmelCase_			)[mask]

     A__										=  pred_label[pred_label == label]

     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]
     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]
     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   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     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     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(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__			,				A__			,				A__			,				A__										=  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  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[int] = None					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     A__			,				A__			,				A__			,				A__										=  total_intersect_and_union(
         UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)

     # 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(UpperCAmelCase_			)
     A__										=  np.nanmean(UpperCAmelCase_			)
     A__										=  all_acc
     A__										=  iou
     A__										=  acc

     if nan_to_num is not None:
          A__										=  {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 					a						(       datasets.Metric				):



    """simple docstring"""



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






         """simple docstring"""


         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






