





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor




class 							UpperCamelCase_							:







 """simple docstring"""

 def __init__(   self  ,				UpperCAmelCase  ,				UpperCAmelCase=1_3  ,				UpperCAmelCase=3  ,				UpperCAmelCase=True  ,				UpperCAmelCase=True  ,				UpperCAmelCase=0.1  ,				UpperCAmelCase=0.1  ,				UpperCAmelCase=2_2_4  ,				UpperCAmelCase=1_0_0_0  ,				UpperCAmelCase=[3, 3, 6, 4]  ,				UpperCAmelCase=[4_8, 5_6, 1_1_2, 2_2_0]  ,				):
       __lowerCamelCase				   =       parent
       __lowerCamelCase				   =       batch_size
       __lowerCamelCase				   =       num_channels
       __lowerCamelCase				   =       is_training
       __lowerCamelCase				   =       use_labels
       __lowerCamelCase				   =       hidden_dropout_prob
       __lowerCamelCase				   =       attention_probs_dropout_prob
       __lowerCamelCase				   =       num_labels
       __lowerCamelCase				   =       image_size
       __lowerCamelCase				   =       layer_depths
       __lowerCamelCase				   =       embed_dims


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       __lowerCamelCase				   =       None
       if self.use_labels:
             __lowerCamelCase				   =       ids_tensor([self.batch_size]  ,				self.num_labels							)

       __lowerCamelCase				   =       self.get_config()

       return config, pixel_values, labels


 def 		lowerCamelCase_ (   self							):
       return SwiftFormerConfig(
           depths=self.layer_depths  ,				embed_dims=self.embed_dims  ,				mlp_ratio=4  ,				downsamples=[True, True, True, True]  ,				hidden_act="""gelu"""  ,				num_labels=self.num_labels  ,				down_patch_size=3  ,				down_stride=2  ,				down_pad=1  ,				drop_rate=0.0  ,				drop_path_rate=0.0  ,				use_layer_scale=__UpperCAmelCase  ,				layer_scale_init_value=1E-5  ,				)


 def 		lowerCamelCase_ (   self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase							):
       __lowerCamelCase				   =       SwiftFormerModel(config=__UpperCAmelCase							)
       model.to(__UpperCAmelCase							)
       model.eval()
       __lowerCamelCase				   =       model(__UpperCAmelCase							)
       self.parent.assertEqual(result.last_hidden_state.shape  ,				(self.batch_size, self.embed_dims[-1], 7, 7)							)


 def 		lowerCamelCase_ (   self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase							):
       __lowerCamelCase				   =       self.num_labels
       __lowerCamelCase				   =       SwiftFormerForImageClassification(__UpperCAmelCase							)
       model.to(__UpperCAmelCase							)
       model.eval()
       __lowerCamelCase				   =       model(__UpperCAmelCase  ,				labels=__UpperCAmelCase							)
       self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)							)

       __lowerCamelCase				   =       SwiftFormerForImageClassification(__UpperCAmelCase							)
       model.to(__UpperCAmelCase							)
       model.eval()

       __lowerCamelCase				   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
       __lowerCamelCase				   =       model(__UpperCAmelCase							)
       self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)							)



 def 		lowerCamelCase_ (   self							):
       (__lowerCamelCase)				   =       self.prepare_config_and_inputs()
       __lowerCamelCase				   =       {"pixel_values": pixel_values}
       return config, inputs_dict




@require_torch
class 							UpperCamelCase_							(       __UpperCamelCase							,__UpperCamelCase							,unittest.TestCase		):







 """simple docstring"""

 A			 =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
 A			 =       (
     {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 A			 =       False
 A			 =       False
 A			 =       False
 A			 =       False
 A			 =       False


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       SwiftFormerModelTester(self							)
       __lowerCamelCase				   =       ConfigTester(
           self  ,				config_class=__UpperCAmelCase  ,				has_text_modality=__UpperCAmelCase  ,				hidden_size=3_7  ,				num_attention_heads=1_2  ,				num_hidden_layers=1_2  ,				)


 def 		lowerCamelCase_ (   self							):
       self.config_tester.run_common_tests()


 @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
 def 		lowerCamelCase_ (   self							):
       pass


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __lowerCamelCase				   =       model_class(__UpperCAmelCase							)
             __lowerCamelCase				   =       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCAmelCase  ,				nn.Linear							)							)


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

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

             __lowerCamelCase				   =       ["pixel_values"]
             self.assertListEqual(arg_names[:1]  ,				__UpperCAmelCase							)


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase							)


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)


 @slow
 def 		lowerCamelCase_ (   self							):



       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __lowerCamelCase				   =       SwiftFormerModel.from_pretrained(__UpperCAmelCase							)
             self.assertIsNotNone(__UpperCAmelCase							)


 @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
 def 		lowerCamelCase_ (   self							):
       pass


 def 		lowerCamelCase_ (   self							):



       def check_hidden_states_output(UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase							):
             __lowerCamelCase				   =       model_class(__UpperCAmelCase							)
             model.to(__UpperCAmelCase							)
             model.eval()

             with torch.no_grad():
                   __lowerCamelCase				   =       model(**self._prepare_for_class(__UpperCAmelCase  ,				__UpperCAmelCase							)							)

             __lowerCamelCase				   =       outputs.hidden_states

             __lowerCamelCase				   =       8
             self.assertEqual(len(__UpperCAmelCase							)  ,				__UpperCAmelCase							)  # TODO

             # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
             # with the width and height being successively divided by 2, after every 2 blocks
             for i in range(len(__UpperCAmelCase							)							):
                   self.assertEqual(
                       hidden_states[i].shape  ,				torch.Size(
                           [
                               self.model_tester.batch_size,
                               self.model_tester.embed_dims[i // 2],
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           ]							)  ,				)

       __lowerCamelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __lowerCamelCase				   =       True
             check_hidden_states_output(__UpperCAmelCase  ,				__UpperCAmelCase  ,				__UpperCAmelCase							)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __lowerCamelCase				   =       True

             check_hidden_states_output(__UpperCAmelCase  ,				__UpperCAmelCase  ,				__UpperCAmelCase							)


 def 		lowerCamelCase_ (   self							):



       def _config_zero_init(UpperCAmelCase							):
             __lowerCamelCase				   =       copy.deepcopy(__UpperCAmelCase							)
             for key in configs_no_init.__dict__.keys():
                   if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                         setattr(__UpperCAmelCase  ,				__UpperCAmelCase  ,				1E-10							)
                   if isinstance(getattr(__UpperCAmelCase  ,				__UpperCAmelCase  ,				__UpperCAmelCase							)  ,				__UpperCAmelCase							):
                         __lowerCamelCase				   =       _config_zero_init(getattr(__UpperCAmelCase  ,				__UpperCAmelCase							)							)
                         setattr(__UpperCAmelCase  ,				__UpperCAmelCase  ,				__UpperCAmelCase							)
             return configs_no_init

       __lowerCamelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

       __lowerCamelCase				   =       _config_zero_init(__UpperCAmelCase							)
       for model_class in self.all_model_classes:
             __lowerCamelCase				   =       model_class(config=__UpperCAmelCase							)
             for name, param in model.named_parameters():
                   if param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1E9) / 1E9).round().item()  ,				[0.0, 1.0]  ,				msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,				)



 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def 		lowerCamelCase_ (   self							):
       pass



def  UpperCamelCase__     (   ):




      '''simple docstring'''







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






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







 """simple docstring"""

 @cached_property
 def 		lowerCamelCase_ (   self							):
       return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None



 @slow
 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(__UpperCAmelCase							)

       __lowerCamelCase				   =       self.default_image_processor
       __lowerCamelCase				   =       prepare_img()
       __lowerCamelCase				   =       image_processor(images=__UpperCAmelCase  ,				return_tensors="""pt"""							).to(__UpperCAmelCase							)

       # forward pass
       with torch.no_grad():
             __lowerCamelCase				   =       model(**__UpperCAmelCase							)

       # verify the logits
       __lowerCamelCase				   =       torch.Size((1, 1_0_0_0)							)
       self.assertEqual(outputs.logits.shape  ,				__UpperCAmelCase							)

       __lowerCamelCase				   =       torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]							).to(__UpperCAmelCase							)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,				__UpperCAmelCase  ,				atol=1E-4							)							)














from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase__      			=						'''
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.
'''

UpperCAmelCase__      			=						'''
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.        ])}
'''

UpperCAmelCase__      			=						'''\
@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     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = False				,       )				->						Dict:



					"""simple docstring"""
					if label_map is not None:
										for old_id, new_id in label_map.items():
															__a					:						str      =					new_id

    # turn into Numpy arrays
					__a					:						Union[str, Any]      =					np.array(__A      )
					__a					:						Any      =					np.array(__A      )

					if reduce_labels:
										__a					:						Dict      =					255
										__a					:						Union[str, Any]      =					label - 1
										__a					:						int      =					255

					__a					:						Optional[Any]      =					label != ignore_index
					__a					:						int      =					np.not_equal(__A				,       __A      )
					__a					:						str      =					pred_label[mask]
					__a					:						List[str]      =					np.array(__A      )[mask]

					__a					:						Optional[int]      =					pred_label[pred_label == label]

					__a					:						Dict      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						Union[str, Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						List[Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]

					__a					:						Optional[int]      =					area_pred_label + area_label - area_intersect

					return area_intersect, area_union, area_pred_label, area_label







def     a_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = False				,       )				->						Dict:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						List[str]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[int]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					for result, gt_seg_map in zip(__A				,       __A      ):
										__a      ,			__a      ,			__a      ,			__a					:						Dict      =					intersect_and_union(
										    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )
										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_   (__A				,       __A				,       __A				,       __A				,       __A = None				,       __A = None				,       __A = False				,       )				->						Optional[int]:



					"""simple docstring"""
					__a      ,			__a      ,			__a      ,			__a					:						Optional[int]      =					total_intersect_and_union(
					    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )

					# compute metrics
					__a					:						Any      =					{}

					__a					:						str      =					total_area_intersect.sum() / total_area_label.sum()
					__a					:						List[Any]      =					total_area_intersect / total_area_union
					__a					:						Union[str, Any]      =					total_area_intersect / total_area_label

					__a					:						Optional[int]      =					np.nanmean(__A      )
					__a					:						str      =					np.nanmean(__A      )
					__a					:						List[str]      =					all_acc
					__a					:						Dict      =					iou
					__a					:						Union[str, Any]      =					acc

					if nan_to_num is not None:
										__a					:						Tuple      =					{metric: np.nan_to_num(__A				,       nan=__A      ) for metric, metric_value in metrics.items()}

					return metrics




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,							_KWARGS_DESCRIPTION      )
class 							snake_case_       (    datasets.Metric      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Tuple:




											'''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:					Optional[int] ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					bool ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					Optional[Dict[int, int]] = None ,					__UpperCAmelCase:					bool = False ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					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





