



'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__      					=     "\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__      					=     "\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__      					=     "\\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  (				_snake_case  :						str     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Dict     ,_snake_case  :						bool     ,_snake_case  :						Optional[Dict[int, int]] = None     ,_snake_case  :						bool = False     ,):





    '''simple docstring'''


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

    # turn into Numpy arrays
    lowercase__		       =						np.array(_snake_case       )
    lowercase__		       =						np.array(_snake_case       )

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

    lowercase__		       =						label != ignore_index
    lowercase__		       =						np.not_equal(_snake_case     ,_snake_case       )
    lowercase__		       =						pred_label[mask]
    lowercase__		       =						np.array(_snake_case       )[mask]

    lowercase__		       =						pred_label[pred_label == label]

    lowercase__		       =						np.histogram(_snake_case     ,bins=_snake_case     ,range=(0, num_labels - 1)       )[0]
    lowercase__		       =						np.histogram(_snake_case     ,bins=_snake_case     ,range=(0, num_labels - 1)       )[0]
    lowercase__		       =						np.histogram(_snake_case     ,bins=_snake_case     ,range=(0, num_labels - 1)       )[0]

    lowercase__		       =						area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Optional[int]     ,_snake_case  :						bool     ,_snake_case  :						Optional[Dict[int, int]] = None     ,_snake_case  :						bool = False     ,):





    '''simple docstring'''


    lowercase__		       =						np.zeros((num_labels,)     ,dtype=np.floataa       )
    lowercase__		       =						np.zeros((num_labels,)     ,dtype=np.floataa       )
    lowercase__		       =						np.zeros((num_labels,)     ,dtype=np.floataa       )
    lowercase__		       =						np.zeros((num_labels,)     ,dtype=np.floataa       )
    for result, gt_seg_map in zip(_snake_case     ,_snake_case       ):
        lowercase__		       =						intersect_and_union(
            _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
        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  (				_snake_case  :						Optional[Any]     ,_snake_case  :						List[str]     ,_snake_case  :						Optional[Any]     ,_snake_case  :						bool     ,_snake_case  :						Optional[int] = None     ,_snake_case  :						Optional[Dict[int, int]] = None     ,_snake_case  :						bool = False     ,):





    '''simple docstring'''


    lowercase__		       =						total_intersect_and_union(
        _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )

    # compute metrics
    lowercase__		       =						{}

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

    lowercase__		       =						np.nanmean(_snake_case       )
    lowercase__		       =						np.nanmean(_snake_case       )
    lowercase__		       =						all_acc
    lowercase__		       =						iou
    lowercase__		       =						acc

    if nan_to_num is not None:
        lowercase__		       =						{metric: np.nan_to_num(_snake_case     ,nan=_snake_case       ) 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      )  ->							int:
           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     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = False     ,)  ->							Tuple:
           lowercase__		       =						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









'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
 import jax

 from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor


class 				snake_case (unittest.TestCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=100     ,UpperCAmelCase_=13     ,UpperCAmelCase_=30     ,UpperCAmelCase_=2     ,UpperCAmelCase_=3     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_=32     ,UpperCAmelCase_=5     ,UpperCAmelCase_=4     ,UpperCAmelCase_=37     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=10     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=3     ,)  ->							List[Any]:
           lowercase__		       =						parent
           lowercase__		       =						vocab_size
           lowercase__		       =						batch_size
           lowercase__		       =						image_size
           lowercase__		       =						patch_size
           lowercase__		       =						num_channels
           lowercase__		       =						is_training
           lowercase__		       =						use_labels
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						type_sequence_label_size
           lowercase__		       =						initializer_range

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



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

           lowercase__		       =						BeitConfig(
               vocab_size=self.vocab_size     ,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     ,is_decoder=UpperCAmelCase_     ,initializer_range=self.initializer_range     ,)

           return config, pixel_values, labels



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						FlaxBeitModel(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						FlaxBeitForImageClassification(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FlaxBeitForImageClassification(UpperCAmelCase_      )

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_      )







       def 							_a      (				self      )  ->							int:
           lowercase__		       =						self.prepare_config_and_inputs()
           (
               (
               lowercase__
           )       ,       (
               lowercase__
           )       ,       (
               lowercase__
           )       ,       
           )		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_flax
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[int]						   =					(
           (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
       )



       def 							_a      (				self      )  ->							None:
           lowercase__		       =						FlaxBeitModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,has_text_modality=UpperCAmelCase_     ,hidden_size=37      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           self.config_tester.run_common_tests()



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                   lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_      )
                   lowercase__		       =						model_class(UpperCAmelCase_      )

                   @jax.jit
                   def model_jitted(UpperCAmelCase_     ,**UpperCAmelCase_      ):
                       return model(pixel_values=UpperCAmelCase_     ,**UpperCAmelCase_      )

                   with self.subTest("JIT Enabled"      ):
                       lowercase__		       =						model_jitted(**UpperCAmelCase_      ).to_tuple()

                   with self.subTest("JIT Disabled"      ):
                       with jax.disable_jit():
                           lowercase__		       =						model_jitted(**UpperCAmelCase_      ).to_tuple()

                   self.assertEqual(len(UpperCAmelCase_      )     ,len(UpperCAmelCase_      )      )
                   for jitted_output, output in zip(UpperCAmelCase_     ,UpperCAmelCase_      ):
                       self.assertEqual(jitted_output.shape     ,output.shape      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )







       @slow
       def 							_a      (				self      )  ->							Tuple:
           for model_class_name in self.all_model_classes:
               lowercase__		       =						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"      )
               lowercase__		       =						model(np.ones((1, 3, 224, 224)      )      )
               self.assertIsNotNone(UpperCAmelCase_      )





def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image



@require_vision
@require_flax
class 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Optional[int]:
           return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      ).pixel_values

           # prepare bool_masked_pos
           lowercase__		       =						np.ones((1, 196)     ,dtype=UpperCAmelCase_      )

           # forward pass
           lowercase__		       =						model(pixel_values=UpperCAmelCase_     ,bool_masked_pos=UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 196, 8_192)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array(
               [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      )

           self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]     ,UpperCAmelCase_     ,atol=1E-2      )      )



       @slow
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      )

           # forward pass
           lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 1_000)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array([-1.23_85, -1.09_87, -1.01_08]      )

           self.assertTrue(np.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						281
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )







       @slow
       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      )

           # forward pass
           lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 21_841)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array([1.68_81, -0.27_87, 0.59_01]      )

           self.assertTrue(np.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						2_396
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )




