

from typing import Dict, Optional

import numpy as np

import datasets


a_    :						List[Any]    		=      "\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_    :						List[Any]    		=      "\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_    :						str    		=      "\\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    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  bool		,							_UpperCamelCase		:  Optional[Dict[int, int]] = None		,							_UpperCamelCase		:  bool = False		,							)		->							int:
 '''simple docstring'''


 if label_map is not None:
  for old_id, new_id in label_map.items():
   SCREAMING_SNAKE_CASE								=			new_id

    # turn into Numpy arrays
 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)

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

 SCREAMING_SNAKE_CASE								=			label != ignore_index
 SCREAMING_SNAKE_CASE								=			np.not_equal(_UpperCamelCase		,							_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			pred_label[mask]
 SCREAMING_SNAKE_CASE								=			np.array(_UpperCamelCase	)[mask]

 SCREAMING_SNAKE_CASE								=			pred_label[pred_label == label]

 SCREAMING_SNAKE_CASE								=			np.histogram(_UpperCamelCase		,							bins=_UpperCamelCase		,							range=(0, num_labels - 1)	)[0]
 SCREAMING_SNAKE_CASE								=			np.histogram(_UpperCamelCase		,							bins=_UpperCamelCase		,							range=(0, num_labels - 1)	)[0]
 SCREAMING_SNAKE_CASE								=			np.histogram(_UpperCamelCase		,							bins=_UpperCamelCase		,							range=(0, num_labels - 1)	)[0]

 SCREAMING_SNAKE_CASE								=			area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  Any		,							_UpperCamelCase		:  bool		,							_UpperCamelCase		:  Optional[Dict[int, int]] = None		,							_UpperCamelCase		:  bool = False		,							)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			np.zeros((num_labels,)		,							dtype=np.floataa	)
 SCREAMING_SNAKE_CASE								=			np.zeros((num_labels,)		,							dtype=np.floataa	)
 SCREAMING_SNAKE_CASE								=			np.zeros((num_labels,)		,							dtype=np.floataa	)
 SCREAMING_SNAKE_CASE								=			np.zeros((num_labels,)		,							dtype=np.floataa	)
 for result, gt_seg_map in zip(_UpperCamelCase		,							_UpperCamelCase	):
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			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        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  bool		,							_UpperCamelCase		:  Optional[int] = None		,							_UpperCamelCase		:  Optional[Dict[int, int]] = None		,							_UpperCamelCase		:  bool = False		,							)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			total_intersect_and_union(
     _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)

 # compute metrics
 SCREAMING_SNAKE_CASE								=			{}

 SCREAMING_SNAKE_CASE								=			total_area_intersect.sum() / total_area_label.sum()
 SCREAMING_SNAKE_CASE								=			total_area_intersect / total_area_union
 SCREAMING_SNAKE_CASE								=			total_area_intersect / total_area_label

 SCREAMING_SNAKE_CASE								=			np.nanmean(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			np.nanmean(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			all_acc
 SCREAMING_SNAKE_CASE								=			iou
 SCREAMING_SNAKE_CASE								=			acc

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

    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 :      List[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      bool							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[Dict[int, int]] = None							,    snake_case__ :      bool = False							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			mean_iou(
         results=snake_case__							,    gt_seg_maps=snake_case__							,    num_labels=snake_case__							,    ignore_index=snake_case__							,    nan_to_num=snake_case__							,    label_map=snake_case__							,    reduce_labels=snake_case__							,    )
     return iou_result




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="facebook/bart-large-mnli"
    __UpperCamelCase         =(
        "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
        "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
        "It returns the most likely label in the list of provided `labels` for the input text."
    )
    __UpperCamelCase         ="text_classifier"
    __UpperCamelCase         =AutoTokenizer
    __UpperCamelCase         =AutoModelForSequenceClassification

    __UpperCamelCase         =["text", ["text"]]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     super().setup()
     SCREAMING_SNAKE_CASE								=			self.model.config
     SCREAMING_SNAKE_CASE								=			-1
     for idx, label in config.idalabel.items():
      if label.lower().startswith('entail'    ):
       SCREAMING_SNAKE_CASE								=			int(snake_case__    )
     if self.entailment_id == -1:
      raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'    )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			labels
     return self.pre_processor(
         [text] * len(snake_case__    )							,    [F"""This example is {label}""" for label in labels]							,    return_tensors='pt'							,    padding='max_length'							,    )




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

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			outputs.logits
     SCREAMING_SNAKE_CASE								=			torch.argmax(logits[:, 2]    ).item()
     return self._labels[label_id]

