from typing import Dict, Optional

import numpy as np

import datasets


__lowerCamelCase			: str							   =		'''
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.
'''

__lowerCamelCase			: 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.        ])}
'''

__lowerCamelCase			: Optional[Any]							   =		'''\
@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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool ,     __UpperCamelCase    :       Optional[Dict[int, int]] = None ,     __UpperCamelCase    :       bool = False ,     )							->					Tuple:







  """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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       bool ,     __UpperCamelCase    :       Optional[Dict[int, int]] = None ,     __UpperCamelCase    :       bool = False ,     )							->					Tuple:







  """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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       bool ,     __UpperCamelCase    :       Optional[int] = None ,     __UpperCamelCase    :       Optional[Dict[int, int]] = None ,     __UpperCamelCase    :       bool = False ,     )							->					Tuple:







  """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 __snake_case				(						datasets.Metric		):




   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			:				Optional[int]       ,	_lowercase			:				Any       ,	_lowercase			:				List[Any]       ,	_lowercase			:				int       ,	_lowercase			:				bool       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				Optional[Dict[int, int]] = None       ,	_lowercase			:				bool = False       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      mean_iou(
         results=_lowercase       ,	gt_seg_maps=_lowercase       ,	num_labels=_lowercase       ,	ignore_index=_lowercase       ,	nan_to_num=_lowercase       ,	label_map=_lowercase       ,	reduce_labels=_lowercase       ,	)
     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.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






     return self.pre_processor.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )[0].strip()
