

from typing import Dict, Optional

import numpy as np

import datasets


A											=     '\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											=     '\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											=     '\\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 a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__ = None			, lowercase__ = False			, ):





	'''simple docstring'''
	if label_map is not None:
		for old_id, new_id in label_map.items():
			snake_case_      		= new_id

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

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

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

	snake_case_      		= pred_label[pred_label == label]

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

	snake_case_      		= area_pred_label + area_label - area_intersect

	return area_intersect, area_union, area_pred_label, area_label





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__ = None			, lowercase__ = False			, ):





	'''simple docstring'''
	snake_case_      		= np.zeros((num_labels,)			, dtype=np.floataa	)
	snake_case_      		= np.zeros((num_labels,)			, dtype=np.floataa	)
	snake_case_      		= np.zeros((num_labels,)			, dtype=np.floataa	)
	snake_case_      		= np.zeros((num_labels,)			, dtype=np.floataa	)
	for result, gt_seg_map in zip(lowercase__			, lowercase__	):
		snake_case_		,       snake_case_		,       snake_case_		,       snake_case_      		= intersect_and_union(
		    lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	)
		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(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__ = None			, lowercase__ = None			, lowercase__ = False			, ):





	'''simple docstring'''
	snake_case_		,       snake_case_		,       snake_case_		,       snake_case_      		= total_intersect_and_union(
	    lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	)

	# compute metrics
	snake_case_      		= {}

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

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

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

	return metrics






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






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = False ,				):

				"""simple docstring"""


				snake_case_      		= 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



import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
		from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=3 ,				__UpperCamelCase=16 ,				__UpperCamelCase=[1, 2, 1] ,				__UpperCamelCase=[2, 2, 4] ,				__UpperCamelCase=2 ,				__UpperCamelCase=2.0 ,				__UpperCamelCase=True ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase=True ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				__UpperCamelCase=10 ,				__UpperCamelCase=8 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= patch_size
				snake_case_      		= num_channels
				snake_case_      		= embed_dim
				snake_case_      		= depths
				snake_case_      		= num_heads
				snake_case_      		= window_size
				snake_case_      		= mlp_ratio
				snake_case_      		= qkv_bias
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= drop_path_rate
				snake_case_      		= hidden_act
				snake_case_      		= use_absolute_embeddings
				snake_case_      		= patch_norm
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= is_training
				snake_case_      		= scope
				snake_case_      		= use_labels
				snake_case_      		= type_sequence_label_size
				snake_case_      		= encoder_stride


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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

				snake_case_      		= self.get_config()

				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return SwinvaConfig(
				    image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				embed_dim=self.embed_dim ,				depths=self.depths ,				num_heads=self.num_heads ,				window_size=self.window_size ,				mlp_ratio=self.mlp_ratio ,				qkv_bias=self.qkv_bias ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				drop_path_rate=self.drop_path_rate ,				hidden_act=self.hidden_act ,				use_absolute_embeddings=self.use_absolute_embeddings ,				path_norm=self.patch_norm ,				layer_norm_eps=self.layer_norm_eps ,				initializer_range=self.initializer_range ,				encoder_stride=self.encoder_stride ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= SwinvaModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)

				snake_case_      		= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
				snake_case_      		= int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, expected_seq_len, expected_dim)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= SwinvaForMaskedImageModeling(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.logits.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= SwinvaForMaskedImageModeling(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()

				snake_case_      		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, 1, self.image_size, self.image_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.type_sequence_label_size
				snake_case_      		= SwinvaForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				snake_case_		,       snake_case_		,       snake_case_      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
			)
			__A						  =  (
			    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= SwinvaModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				embed_dim=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='Swinv2 does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= True

				for model_class in self.all_model_classes:
					snake_case_      		= True
					snake_case_      		= False
					snake_case_      		= True
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()
					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)
					snake_case_      		= outputs.attentions
					snake_case_      		= len(self.model_tester.depths							)
					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					snake_case_      		= True
					snake_case_      		= config.window_size**2
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()
					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)
					snake_case_      		= outputs.attentions
					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

					self.assertListEqual(
					    list(attentions[0].shape[-3:]							) ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,				)
					snake_case_      		= len(__UpperCamelCase							)

					# Check attention is always last and order is fine
					snake_case_      		= True
					snake_case_      		= True
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()
					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

					if hasattr(self.model_tester ,				'num_hidden_states_types'							):
						snake_case_      		= self.model_tester.num_hidden_states_types
					else:
						# also another +1 for reshaped_hidden_states
						snake_case_      		= 2
					self.assertEqual(out_len + added_hidden_states ,				len(__UpperCamelCase							)							)

					snake_case_      		= outputs.attentions

					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

					self.assertListEqual(
					    list(self_attentions[0].shape[-3:]							) ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= model_class(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()

				with torch.no_grad():
					snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

				snake_case_      		= outputs.hidden_states

				snake_case_      		= getattr(
				    self.model_tester ,				'expected_num_hidden_layers' ,				len(self.model_tester.depths							) + 1							)
				self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

				# Swinv2 has a different seq_length
				snake_case_      		= (
				    config.patch_size
				    if isinstance(config.patch_size ,				collections.abc.Iterable							)
				    else (config.patch_size, config.patch_size)
				)

				snake_case_      		= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]							) ,				[num_patches, self.model_tester.embed_dim] ,				)

				snake_case_      		= outputs.reshaped_hidden_states
				self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

				snake_case_		,       snake_case_		,       snake_case_		,       snake_case_      		= reshaped_hidden_states[0].shape
				snake_case_      		= (
				    reshaped_hidden_states[0].view(__UpperCamelCase ,				__UpperCamelCase ,				height * width							).permute(0 ,				2 ,				1							)
				)
				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-2:]							) ,				[num_patches, self.model_tester.embed_dim] ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				snake_case_      		= (
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size ,				collections.abc.Iterable							)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)

				for model_class in self.all_model_classes:
					snake_case_      		= True
					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

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

					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= 3

				snake_case_      		= (
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size ,				collections.abc.Iterable							)
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)
				snake_case_      		= (
				    config.patch_size
				    if isinstance(config.patch_size ,				collections.abc.Iterable							)
				    else (config.patch_size, config.patch_size)
				)

				snake_case_      		= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				snake_case_      		= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
					snake_case_      		= True
					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				(padded_height, padded_width)							)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					snake_case_      		= True
					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				(padded_height, padded_width)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= SwinvaModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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







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






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return (
				    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'							)
				    if is_vision_available()
				    else None
				)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'							).to(
				    __UpperCamelCase							)
				snake_case_      		= self.default_image_processor

				snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)
				snake_case_      		= torch.tensor([-0.3947, -0.4306, 0.0026]							).to(__UpperCamelCase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)
