






from typing import Dict, Optional

import numpy as np

import datasets


__A      =				'\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'

__A      =				'\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'

__A      =				'\\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      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase = None					,			_lowercase = 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(_lowercase )
					_A								= np.array(_lowercase )

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

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

					_A								= pred_label[pred_label == label]

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

					'''simple docstring'''







					_A							,_A							,_A							,_A								= total_intersect_and_union(
					    _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					# 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(_lowercase )
					_A								= np.nanmean(_lowercase )
					_A								= all_acc
					_A								= iou
					_A								= acc

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





							"""simple docstring"""

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











import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				str:
												_A								= question_encoder
												_A								= generator
												_A								= self.question_encoder

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Dict:
												if os.path.isfile(__A   ):
																	raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
												os.makedirs(__A		,     exist_ok=__A   )
												_A								= os.path.join(__A		,     '''question_encoder_tokenizer'''   )
												_A								= os.path.join(__A		,     '''generator_tokenizer'''   )
												self.question_encoder.save_pretrained(__A   )
												self.generator.save_pretrained(__A   )

							@classmethod
							def       __A (  cls:   Optional[Any]		,     __A:   List[str]		,     **__A:   int   )    ->				Any:
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												_A								= kwargs.pop('''config'''		,     __A   )

												if config is None:
																	_A								= RagConfig.from_pretrained(__A   )

												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.question_encoder		,     subfolder='''question_encoder_tokenizer'''   )
												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.generator		,     subfolder='''generator_tokenizer'''   )
												return cls(question_encoder=__A		,     generator=__A   )

							def __call__(  self:   int		,     *__A:   Optional[int]		,     **__A:   List[str]   )    ->				int:
												return self.current_tokenizer(*__A		,     **__A   )

							def       __A (  self:   Dict		,     *__A:   List[str]		,     **__A:   List[str]   )    ->				Dict:
												return self.generator.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Union[str, Any]		,     *__A:   Tuple		,     **__A:   List[str]   )    ->				Tuple:
												return self.generator.decode(*__A		,     **__A   )

							def       __A (  self:   Dict   )    ->				List[str]:
												_A								= self.question_encoder

							def       __A (  self:   Union[str, Any]   )    ->				int:
												_A								= self.generator



							def       __A (  self:   Dict		,     __A:   List[str]		,     __A:   Optional[List[str]] = None		,     __A:   Optional[int] = None		,     __A:   Optional[int] = None		,     __A:   str = "longest"		,     __A:   str = None		,     __A:   bool = True		,     **__A:   Tuple		,     )    ->				BatchEncoding:
												warnings.warn(
												    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
												    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
												    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
												    '''details'''		,     __A		,     )
												if max_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    __A		,     add_special_tokens=__A		,     return_tensors=__A		,     max_length=__A		,     padding=__A		,     truncation=__A		,     **__A		,     )
												if tgt_texts is None:
																	return model_inputs
												# Process tgt_texts
												if max_target_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    text_target=__A		,     add_special_tokens=__A		,     return_tensors=__A		,     padding=__A		,     max_length=__A		,     truncation=__A		,     **__A		,     )
												_A								= labels['''input_ids''']
												return model_inputs



