





'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

   if is_vision_available():
      from transformers import MaskFormerImageProcessor

if is_vision_available():
   from PIL import Image
class     __lowerCAmelCase					:






       """simple docstring"""





       def __init__(							self       :		int	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Optional[Any]=2	,			lowerCAmelCase__       :		List[str]=True	,			lowerCAmelCase__       :		Tuple=False	,			lowerCAmelCase__       :		Optional[Any]=10	,			lowerCAmelCase__       :		Any=3	,			lowerCAmelCase__       :		str=32 * 4	,			lowerCAmelCase__       :		Union[str, Any]=32 * 6	,			lowerCAmelCase__       :		Union[str, Any]=4	,			lowerCAmelCase__       :		Optional[Any]=32	,			)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	is_training
            _UpperCamelCase       =	use_auxiliary_loss
            _UpperCamelCase       =	num_queries
            _UpperCamelCase       =	num_channels
            _UpperCamelCase       =	min_size
            _UpperCamelCase       =	max_size
            _UpperCamelCase       =	num_labels
            _UpperCamelCase       =	mask_feature_size





       def snake_case__						(							self       :		str					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
                lowerCAmelCase__					)

            _UpperCamelCase       =	torch.ones([self.batch_size, self.min_size, self.max_size]	,			device=lowerCAmelCase__					)

            _UpperCamelCase       =	(
                torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,			device=lowerCAmelCase__					) > 0.5
            ).float()
            _UpperCamelCase       =	(torch.rand((self.batch_size, self.num_labels)	,			device=lowerCAmelCase__					) > 0.5).long()

            _UpperCamelCase       =	self.get_config()
            return config, pixel_values, pixel_mask, mask_labels, class_labels





       def snake_case__						(							self       :		Tuple					)   ->					Dict:

            '''simple docstring'''




            return MaskFormerConfig.from_backbone_and_decoder_configs(
                backbone_config=SwinConfig(
                    depths=[1, 1, 1, 1]	,			)	,			decoder_config=DetrConfig(
                    decoder_ffn_dim=128	,			num_queries=self.num_queries	,			decoder_attention_heads=2	,			d_model=self.mask_feature_size	,			)	,			mask_feature_size=self.mask_feature_size	,			fpn_feature_size=self.mask_feature_size	,			num_channels=self.num_channels	,			num_labels=self.num_labels	,			)





       def snake_case__						(							self       :		str					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase       =	{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
            return config, inputs_dict





       def snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	output.encoder_hidden_states
            _UpperCamelCase       =	output.pixel_decoder_hidden_states
            _UpperCamelCase       =	output.transformer_decoder_hidden_states

            self.parent.assertTrue(len(lowerCAmelCase__					)	,			len(config.backbone_config.depths					)					)
            self.parent.assertTrue(len(lowerCAmelCase__					)	,			len(config.backbone_config.depths					)					)
            self.parent.assertTrue(len(lowerCAmelCase__					)	,			config.decoder_config.decoder_layers					)





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Union[str, Any]=False					)   ->					Dict:

            '''simple docstring'''




            with torch.no_grad():
                 _UpperCamelCase       =	MaskFormerModel(config=lowerCAmelCase__					)
                 model.to(lowerCAmelCase__					)
                 model.eval()

                 _UpperCamelCase       =	model(pixel_values=lowerCAmelCase__	,			pixel_mask=lowerCAmelCase__					)
                 _UpperCamelCase       =	model(lowerCAmelCase__	,			output_hidden_states=lowerCAmelCase__					)
            # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
            # encoder and pixel decoder
            self.parent.assertEqual(
                output.transformer_decoder_last_hidden_state.shape	,			(self.batch_size, self.num_queries, self.mask_feature_size)	,			)
            # let's ensure the other two hidden state exists
            self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
            self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

            if output_hidden_states:
                 self.check_output_hidden_state(lowerCAmelCase__	,			lowerCAmelCase__					)







       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Any					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	MaskFormerForInstanceSegmentation(config=lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.eval()

            def comm_check_on_output(lowerCAmelCase__       :		Any					):
                 # let's still check that all the required stuff is there
                 self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None					)
                 self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None					)
                 self.parent.assertTrue(result.encoder_last_hidden_state is not None					)
                 # okay, now we need to check the logits shape
                 # due to the encoder compression, masks have a //4 spatial size
                 self.parent.assertEqual(
                     result.masks_queries_logits.shape	,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,			)
                 # + 1 for null class
                 self.parent.assertEqual(
                     result.class_queries_logits.shape	,			(self.batch_size, self.num_queries, self.num_labels + 1)					)

            with torch.no_grad():
                 _UpperCamelCase       =	model(pixel_values=lowerCAmelCase__	,			pixel_mask=lowerCAmelCase__					)
                 _UpperCamelCase       =	model(lowerCAmelCase__					)

                 comm_check_on_output(lowerCAmelCase__					)

                 _UpperCamelCase       =	model(
                     pixel_values=lowerCAmelCase__	,			pixel_mask=lowerCAmelCase__	,			mask_labels=lowerCAmelCase__	,			class_labels=lowerCAmelCase__					)

            comm_check_on_output(lowerCAmelCase__					)

            self.parent.assertTrue(result.loss is not None					)
            self.parent.assertEqual(result.loss.shape	,			torch.Size([1]					)					)

@require_torch
class     __lowerCAmelCase					(   __magic_name__						,						__magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[Any]						    =   (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
       _snake_case     :					List[str]						    =   (
           {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
           if is_torch_available()
           else {}
       )

       _snake_case     :					int						    =   False
       _snake_case     :					Dict						    =   False
       _snake_case     :					Dict						    =   False
       _snake_case     :					Union[str, Any]						    =   False





       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	MaskFormerModelTester(self					)
            _UpperCamelCase       =	ConfigTester(self	,			config_class=lowerCAmelCase__	,			has_text_modality=lowerCAmelCase__					)





       def snake_case__						(							self       :		Union[str, Any]					)   ->					Dict:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__						(							self       :		Union[str, Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskformer_model(lowerCAmelCase__	,			**lowerCAmelCase__	,			output_hidden_states=lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCAmelCase__					)





       @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''					)
       def snake_case__						(							self       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''					)
       def snake_case__						(							self       :		Union[str, Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MaskFormer is not a generative model'''					)
       def snake_case__						(							self       :		str					)   ->					Optional[int]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MaskFormer does not use token embeddings'''					)
       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[int]:

            '''simple docstring'''




            pass





       @require_torch_multi_gpu
       @unittest.skip(
           reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''					)
       def snake_case__						(							self       :		Union[str, Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            pass





       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
       def snake_case__						(							self       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            pass





       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase       =	['''pixel_values''']
                 self.assertListEqual(arg_names[:1]	,			lowerCAmelCase__					)





       @slow
       def snake_case__						(							self       :		Any					)   ->					List[Any]:

            '''simple docstring'''




            for model_name in ["facebook/maskformer-swin-small-coco"]:
                 _UpperCamelCase       =	MaskFormerModel.from_pretrained(lowerCAmelCase__					)
                 self.assertIsNotNone(lowerCAmelCase__					)





       def snake_case__						(							self       :		str					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	(self.model_tester.min_size,) * 2
            _UpperCamelCase       =	{
                '''pixel_values''': torch.randn((2, 3, *size)	,			device=lowerCAmelCase__					),
                '''mask_labels''': torch.randn((2, 10, *size)	,			device=lowerCAmelCase__					),
                '''class_labels''': torch.zeros(2	,			10	,			device=lowerCAmelCase__					).long(),
            }

            _UpperCamelCase       =	MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	model(**lowerCAmelCase__					)
            self.assertTrue(outputs.loss is not None					)





       def snake_case__						(							self       :		Tuple					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskformer_model(lowerCAmelCase__	,			**lowerCAmelCase__	,			output_hidden_states=lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase       =	model_class(lowerCAmelCase__					).to(lowerCAmelCase__					)
                 _UpperCamelCase       =	model(**lowerCAmelCase__	,			output_attentions=lowerCAmelCase__					)
                 self.assertTrue(outputs.attentions is not None					)





       def snake_case__						(							self       :		List[Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            if not self.model_tester.is_training:
                 return
            # only MaskFormerForInstanceSegmentation has the loss
            _UpperCamelCase       =	self.all_model_classes[1]
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()

            _UpperCamelCase       =	model_class(lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.train()

            _UpperCamelCase       =	model(lowerCAmelCase__	,			mask_labels=lowerCAmelCase__	,			class_labels=lowerCAmelCase__					).loss
            loss.backward()







       def snake_case__						(							self       :		Optional[int]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	self.all_model_classes[1]
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True

            _UpperCamelCase       =	model_class(lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.train()

            _UpperCamelCase       =	model(lowerCAmelCase__	,			mask_labels=lowerCAmelCase__	,			class_labels=lowerCAmelCase__					)

            _UpperCamelCase       =	outputs.encoder_hidden_states[0]
            encoder_hidden_states.retain_grad()

            _UpperCamelCase       =	outputs.pixel_decoder_hidden_states[0]
            pixel_decoder_hidden_states.retain_grad()
            # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
            _UpperCamelCase       =	outputs.transformer_decoder_hidden_states[0]
            transformer_decoder_hidden_states.retain_grad()

            _UpperCamelCase       =	outputs.attentions[0]
            attentions.retain_grad()

            outputs.loss.backward(retain_graph=lowerCAmelCase__					)

            self.assertIsNotNone(encoder_hidden_states.grad					)
            self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
            self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
            self.assertIsNotNone(attentions.grad					)

lowercase__						:      Any	=    1E-4







def  a__    (    )		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image




@require_vision
@slow
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       @cached_property
       def snake_case__						(							self       :		Optional[Any]					)   ->					Tuple:

            '''simple docstring'''




            return (
                MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
                if is_vision_available()
                else None
            )





       def snake_case__						(							self       :		Optional[int]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	self.default_image_processor
            _UpperCamelCase       =	prepare_img()
            _UpperCamelCase       =	image_processor(lowerCAmelCase__	,			return_tensors='''pt'''					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	inputs['''pixel_values'''].shape
            # check size is divisible by 32
            self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
            # check size
            self.assertEqual(lowerCAmelCase__	,			(1, 3, 800, 1088)					)

            with torch.no_grad():
                 _UpperCamelCase       =	model(**lowerCAmelCase__					)

            _UpperCamelCase       =	torch.tensor(
                [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]					).to(lowerCAmelCase__					)
            self.assertTrue(
                torch.allclose(
                    outputs.encoder_last_hidden_state[0, 0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)

            _UpperCamelCase       =	torch.tensor(
                [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]					).to(lowerCAmelCase__					)
            self.assertTrue(
                torch.allclose(
                    outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)

            _UpperCamelCase       =	torch.tensor(
                [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]					).to(lowerCAmelCase__					)
            self.assertTrue(
                torch.allclose(
                    outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	(
                MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
                .to(lowerCAmelCase__					)
                .eval()
            )
            _UpperCamelCase       =	self.default_image_processor
            _UpperCamelCase       =	prepare_img()
            _UpperCamelCase       =	image_processor(lowerCAmelCase__	,			return_tensors='''pt'''					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	inputs['''pixel_values'''].shape
            # check size is divisible by 32
            self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
            # check size
            self.assertEqual(lowerCAmelCase__	,			(1, 3, 800, 1088)					)

            with torch.no_grad():
                 _UpperCamelCase       =	model(**lowerCAmelCase__					)
            # masks_queries_logits
            _UpperCamelCase       =	outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,			)
            _UpperCamelCase       =	[
                [-1.3737124, -1.7724937, -1.9364233],
                [-1.5977281, -1.9867939, -2.1523695],
                [-1.5795398, -1.9269832, -2.093942],
            ]
            _UpperCamelCase       =	torch.tensor(lowerCAmelCase__					).to(lowerCAmelCase__					)
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)
            # class_queries_logits
            _UpperCamelCase       =	outputs.class_queries_logits
            self.assertEqual(
                class_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
            _UpperCamelCase       =	torch.tensor(
                [
                    [1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
                    [3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
                    [1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
                ]					).to(lowerCAmelCase__					)
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Union[str, Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	(
                MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''					)
                .to(lowerCAmelCase__					)
                .eval()
            )
            _UpperCamelCase       =	self.default_image_processor
            _UpperCamelCase       =	prepare_img()
            _UpperCamelCase       =	image_processor(lowerCAmelCase__	,			return_tensors='''pt'''					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	inputs['''pixel_values'''].shape
            # check size is divisible by 32
            self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
            # check size
            self.assertEqual(lowerCAmelCase__	,			(1, 3, 800, 1088)					)

            with torch.no_grad():
                 _UpperCamelCase       =	model(**lowerCAmelCase__					)
            # masks_queries_logits
            _UpperCamelCase       =	outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,			)
            _UpperCamelCase       =	[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
            _UpperCamelCase       =	torch.tensor(lowerCAmelCase__					).to(lowerCAmelCase__					)
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)
            # class_queries_logits
            _UpperCamelCase       =	outputs.class_queries_logits
            self.assertEqual(
                class_queries_logits.shape	,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
            _UpperCamelCase       =	torch.tensor(
                [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]					).to(lowerCAmelCase__					)
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)







       def snake_case__						(							self       :		Any					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	(
                MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
                .to(lowerCAmelCase__					)
                .eval()
            )
            _UpperCamelCase       =	self.default_image_processor

            _UpperCamelCase       =	image_processor(
                [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]	,			segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]	,			return_tensors='''pt'''	,			)

            _UpperCamelCase       =	inputs['''pixel_values'''].to(lowerCAmelCase__					)
            _UpperCamelCase       =	[el.to(lowerCAmelCase__					) for el in inputs['''mask_labels''']]
            _UpperCamelCase       =	[el.to(lowerCAmelCase__					) for el in inputs['''class_labels''']]

            with torch.no_grad():
                 _UpperCamelCase       =	model(**lowerCAmelCase__					)

            self.assertTrue(outputs.loss is not None					)










'''simple docstring'''

from math import isclose, sqrt







def  a__    (    lowercase :					float, lowercase :					float, lowercase :					float				)		->		tuple[float, float, float]:





     """simple docstring"""

     _UpperCamelCase       =	point_y / 4 / point_x
     _UpperCamelCase       =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
     _UpperCamelCase       =	(1 - normal_gradient * normal_gradient) / (
         1 + normal_gradient * normal_gradient
     )
     _UpperCamelCase       =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

     # to find the next point, solve the simultaeneous equations:
     # y^2 + 4x^2 = 100
     # y - b = m * (x - a)
     # ==> A x^2 + B x + C = 0
     _UpperCamelCase       =	outgoing_gradient**2 + 4
     _UpperCamelCase       =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
     _UpperCamelCase       =	(point_y - outgoing_gradient * point_x) ** 2 - 100

     _UpperCamelCase       =	(
         -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)
     _UpperCamelCase       =	(
         -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)

     # two solutions, one of which is our input point
     _UpperCamelCase       =	x_minus if isclose(lowercase, lowercase				) else x_plus
     _UpperCamelCase       =	point_y + outgoing_gradient * (next_x - point_x)

     return next_x, next_y, outgoing_gradient







def  a__    (    lowercase :					float = 1.4, lowercase :					float = -9.6				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	0
     _UpperCamelCase       =	first_x_coord
     _UpperCamelCase       =	first_y_coord
     _UpperCamelCase       =	(1_0.1 - point_y) / (0.0 - point_x)

     while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	next_point(lowercase, lowercase, lowercase				)
          num_reflections += 1

     return num_reflections


if __name__ == "__main__":
   print(F"""{solution() = }""")



