

"""simple docstring"""



import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor



class 					a						:



    """simple docstring"""



    def __init__(     self: str    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Dict=13    ,						UpperCamelCase: List[str]=30    ,						UpperCamelCase: Union[str, Any]=2    ,						UpperCamelCase: str=3    ,						UpperCamelCase: Union[str, Any]=True    ,						UpperCamelCase: List[Any]=True    ,						UpperCamelCase: List[str]=32    ,						UpperCamelCase: Optional[Any]=5    ,						UpperCamelCase: List[Any]=4    ,						UpperCamelCase: Tuple=37    ,						UpperCamelCase: Optional[Any]="gelu"    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: Tuple=0.1    ,						UpperCamelCase: Union[str, Any]=10    ,						UpperCamelCase: int=0.02    ,						UpperCamelCase: Optional[int]=None    ,						):






         """simple docstring"""


         A__										=  parent
         A__										=  batch_size
         A__										=  image_size
         A__										=  patch_size
         A__										=  num_channels
         A__										=  is_training
         A__										=  use_labels
         A__										=  hidden_size
         A__										=  num_hidden_layers
         A__										=  num_attention_heads
         A__										=  intermediate_size
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  type_sequence_label_size
         A__										=  initializer_range
         A__										=  scope

         # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         A__										=  (image_size // patch_size) ** 2
         A__										=  num_patches + 1

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         A__										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         A__										=  None
         if self.use_labels:
              A__										=  ids_tensor([self.batch_size]    ,						self.type_sequence_label_size       )

         A__										=  self.get_config()

         return config, pixel_values, labels

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         return ViTMSNConfig(
             image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						intermediate_size=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						initializer_range=self.initializer_range    ,						)

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: Dict    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  ViTMSNModel(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)       )

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: str    ,						UpperCamelCase: int       ):






         """simple docstring"""


         A__										=  self.type_sequence_label_size
         A__										=  ViTMSNForImageClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						labels=UpperCamelCase       )
         print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""       )
         print("""Labels: {labels}"""       )
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         A__										=  1
         A__										=  ViTMSNForImageClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()

         A__										=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         A__										=  model(UpperCamelCase       )
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)       )





    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  self.prepare_config_and_inputs()
         A__			,				A__			,				A__										=  config_and_inputs
         A__										=  {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 					a						(       _lowerCamelCase,	_lowerCamelCase,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    UpperCAmelCase						 =					(
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  ViTMSNModelTester(self       )
         A__										=  ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase    ,						hidden_size=37       )

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""       )
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         pass

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              A__										=  model_class(UpperCamelCase       )
              self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)       )
              A__										=  model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCamelCase    ,						nn.Linear       )       )

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              A__										=  model_class(UpperCamelCase       )
              A__										=  inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              A__										=  [*signature.parameters.keys()]

              A__										=  ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase       )

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase       )





    @slow
    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  ViTMSNModel.from_pretrained(UpperCamelCase       )
              self.assertIsNotNone(UpperCamelCase       )





def  _snake_case     (    ):
     A__										=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
     return image



@require_torch
@require_vision
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @cached_property
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""       ) if is_vision_available() else None





    @slow
    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         torch.manual_seed(2       )
         A__										=  ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""       ).to(UpperCamelCase       )

         A__										=  self.default_image_processor
         A__										=  prepare_img()
         A__										=  image_processor(images=UpperCamelCase    ,						return_tensors="""pt"""       ).to(UpperCamelCase       )

         # forward pass
         with torch.no_grad():
              A__										=  model(**UpperCamelCase       )

         # verify the logits
         A__										=  torch.Size((1, 10_00)       )
         self.assertEqual(outputs.logits.shape    ,						UpperCamelCase       )

         A__										=  torch.tensor([-0.0_803, -0.4_454, -0.2_375]       ).to(UpperCamelCase       )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						UpperCamelCase    ,						atol=1e-4       )       )









"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE_					:	Tuple							  =						'\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'

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'\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'

SCREAMING_SNAKE_CASE_					:	int							  =						'\\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  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     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(UpperCAmelCase_			)
     A__										=  np.array(UpperCAmelCase_			)

     if reduce_labels:
          A__										=  255
          A__										=  label - 1
          A__										=  255

     A__										=  label != ignore_index
     A__										=  np.not_equal(UpperCAmelCase_					,   UpperCAmelCase_			)
     A__										=  pred_label[mask]
     A__										=  np.array(UpperCAmelCase_			)[mask]

     A__										=  pred_label[pred_label == label]

     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]
     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]
     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   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  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     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(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__			,				A__			,				A__			,				A__										=  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  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[int] = None					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     A__			,				A__			,				A__			,				A__										=  total_intersect_and_union(
         UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)

     # 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(UpperCAmelCase_			)
     A__										=  np.nanmean(UpperCAmelCase_			)
     A__										=  all_acc
     A__										=  iou
     A__										=  acc

     if nan_to_num is not None:
          A__										=  {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 					a						(       datasets.Metric				):



    """simple docstring"""



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






         """simple docstring"""


         A__										=  mean_iou(
             results=UpperCamelCase    ,						gt_seg_maps=UpperCamelCase    ,						num_labels=UpperCamelCase    ,						ignore_index=UpperCamelCase    ,						nan_to_num=UpperCamelCase    ,						label_map=UpperCamelCase    ,						reduce_labels=UpperCamelCase    ,						)
         return iou_result






