



'''simple docstring'''





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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowercase_		       =  logging.get_logger(__name__)


lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

lowercase_		       =  {
    "allenai/led-base-16384": 16_384,
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       List[str]          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       int          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Optional[Any]          =       LEDTokenizer
 __lowerCamelCase    :       int          =       ['input_ids', 'attention_mask']



 def __init__(self ,   A=None ,   A=None ,   A=None ,   A="replace" ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<s>" ,   A="<unk>" ,   A="<pad>" ,   A="<mask>" ,   A=False ,   A=True ,   **A ,   )    ->     Optional[Any]:


      """simple docstring"""



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

      _a      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
           _a      =  getattr(A ,   pre_tok_state.pop('''type'''       )       )
           _a      =  add_prefix_space
           _a      =  pre_tok_class(**A       )

      _a      =  add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      _a      =  '''post_processor'''
      _a      =  getattr(self.backend_tokenizer ,   A ,   A       )
      if tokenizer_component_instance:
           _a      =  json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                _a      =  tuple(state['''sep''']       )
           if "cls" in state:
                _a      =  tuple(state['''cls''']       )

           _a      =  False

           if state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
                _a      =  add_prefix_space
                _a      =  True

           if state.get('''trim_offsets''' ,   A       ) != trim_offsets:
                _a      =  trim_offsets
                _a      =  True

           if changes_to_apply:
                _a      =  getattr(A ,   state.pop('''type'''       )       )
                _a      =  component_class(**A       )
                setattr(self.backend_tokenizer ,   A ,   A       )



 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      if self._mask_token is None:
           if self.verbose:
                logger.error('''Using mask_token, but it is not set yet.'''       )
           return None
      return str(self._mask_token       )



 @mask_token.setter
 def 					a__  (self ,   A       )    ->     Any:


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else value
      _a      =  value



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )

      if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               '''to use it with pretokenized inputs.'''       )

      return super()._batch_encode_plus(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )

      if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               '''to use it with pretokenized inputs.'''       )

      return super()._encode_plus(*A ,   **A       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )



 def 					a__  (self ,   A ,   A=None       )    ->     str:


      """simple docstring"""



      _a      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """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 					a__  (self ,   A ,   A = None ,   A = PaddingStrategy.DO_NOT_PAD ,   A = None ,   A = None ,   )    ->     dict:


      """simple docstring"""



      _a      =  super()._pad(
          encoded_inputs=A ,   max_length=A ,   padding_strategy=A ,   pad_to_multiple_of=A ,   return_attention_mask=A ,   )

      # Load from model defaults
      if return_attention_mask is None:
           _a      =  '''attention_mask''' in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
           _a      =  encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           _a      =  len(encoded_inputs['''global_attention_mask''']       ) != len(A       )

           if needs_to_be_padded:
                _a      =  len(A       ) - len(encoded_inputs['''global_attention_mask''']       )

                if self.padding_side == "right":
                     # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                     _a      =  (
                         encoded_inputs['''global_attention_mask'''] + [-1] * difference
                     )
                elif self.padding_side == "left":
                     _a      =  [-1] * difference + encoded_inputs[
                         '''global_attention_mask'''
                     ]
                else:
                     raise ValueError('''Invalid padding strategy:''' + str(self.padding_side       )       )

      return encoded_inputs








'''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



