from typing import Dict, Optional

import numpy as np

import datasets


a  :      List[Any]            =    """
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  :      Any            =    """
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  :      Tuple            =    """\
@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__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase = None							,				lowercase = False							,				):
   if label_map is not None:
      for old_id, new_id in label_map.items():
         lowerCAmelCase_:							Dict       =	new_id

    # turn into Numpy arrays
   lowerCAmelCase_:							Any       =	np.array(lowercase					)
   lowerCAmelCase_:							Union[str, Any]       =	np.array(lowercase					)

   if reduce_labels:
      lowerCAmelCase_:							str       =	255
      lowerCAmelCase_:							Optional[Any]       =	label - 1
      lowerCAmelCase_:							Optional[Any]       =	255

   lowerCAmelCase_:							Dict       =	label != ignore_index
   lowerCAmelCase_:							Union[str, Any]       =	np.not_equal(lowercase							,				lowercase					)
   lowerCAmelCase_:							Tuple       =	pred_label[mask]
   lowerCAmelCase_:							Tuple       =	np.array(lowercase					)[mask]

   lowerCAmelCase_:							Optional[Any]       =	pred_label[pred_label == label]

   lowerCAmelCase_:							Optional[int]       =	np.histogram(lowercase							,				bins=lowercase							,				range=(0, num_labels - 1)					)[0]
   lowerCAmelCase_:							Optional[Any]       =	np.histogram(lowercase							,				bins=lowercase							,				range=(0, num_labels - 1)					)[0]
   lowerCAmelCase_:							Any       =	np.histogram(lowercase							,				bins=lowercase							,				range=(0, num_labels - 1)					)[0]

   lowerCAmelCase_:							str       =	area_pred_label + area_label - area_intersect

   return area_intersect, area_union, area_pred_label, area_label
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase = None							,				lowercase = False							,				):
   lowerCAmelCase_:							Optional[Any]       =	np.zeros((num_labels,)							,				dtype=np.floataa					)
   lowerCAmelCase_:							Tuple       =	np.zeros((num_labels,)							,				dtype=np.floataa					)
   lowerCAmelCase_:							Tuple       =	np.zeros((num_labels,)							,				dtype=np.floataa					)
   lowerCAmelCase_:							int       =	np.zeros((num_labels,)							,				dtype=np.floataa					)
   for result, gt_seg_map in zip(lowercase							,				lowercase					):
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	intersect_and_union(
          lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)
      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__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase = None							,				lowercase = None							,				lowercase = False							,				):
   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	total_intersect_and_union(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)

   # compute metrics
   lowerCAmelCase_:							str       =	{}

   lowerCAmelCase_:							str       =	total_area_intersect.sum() / total_area_label.sum()
   lowerCAmelCase_:							int       =	total_area_intersect / total_area_union
   lowerCAmelCase_:							List[str]       =	total_area_intersect / total_area_label

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

   if nan_to_num is not None:
      lowerCAmelCase_:							List[str]       =	{metric: np.nan_to_num(lowercase							,				nan=lowercase					) 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      _a    (    self ):
       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       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False       ,						):
       lowerCAmelCase_:							Any       =	mean_iou(
           results=lowerCamelCase__       ,						gt_seg_maps=lowerCamelCase__       ,						num_labels=lowerCamelCase__       ,						ignore_index=lowerCamelCase__       ,						nan_to_num=lowerCamelCase__       ,						label_map=lowerCamelCase__       ,						reduce_labels=lowerCamelCase__       ,						)
       return iou_result
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Union[str, Any]            =    {
    """bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[str]          =    'gpt_bigcode'
    SCREAMING_SNAKE_CASE:				Union[str, Any]          =    ['past_key_values']
    SCREAMING_SNAKE_CASE:				Tuple          =    {
        'hidden_size': 'n_embd',
        'max_position_embeddings': 'n_positions',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=50_257       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=768       ,						lowerCamelCase__=12       ,						lowerCamelCase__=12       ,						lowerCamelCase__=None       ,						lowerCamelCase__="gelu_pytorch_tanh"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=50_256       ,						lowerCamelCase__=50_256       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							List[str]       =	n_positions
       lowerCAmelCase_:							Union[str, Any]       =	n_embd
       lowerCAmelCase_:							Union[str, Any]       =	n_layer
       lowerCAmelCase_:							Union[str, Any]       =	n_head
       lowerCAmelCase_:							List[Any]       =	n_inner
       lowerCAmelCase_:							List[Any]       =	activation_function
       lowerCAmelCase_:							Optional[int]       =	resid_pdrop
       lowerCAmelCase_:							Union[str, Any]       =	embd_pdrop
       lowerCAmelCase_:							Any       =	attn_pdrop
       lowerCAmelCase_:							Union[str, Any]       =	layer_norm_epsilon
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	scale_attn_weights
       lowerCAmelCase_:							int       =	use_cache
       lowerCAmelCase_:							int       =	attention_softmax_in_fpaa
       lowerCAmelCase_:							int       =	scale_attention_softmax_in_fpaa
       lowerCAmelCase_:							Union[str, Any]       =	multi_query

       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							List[str]       =	eos_token_id

       super().__init__(bos_token_id=lowerCamelCase__       ,						eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )