

'''simple docstring'''





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

							if is_vision_available():
														from transformers import MaskaFormerImageProcessor

if is_vision_available():
							from PIL import Image





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		Any		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Any=2		,				UpperCAmelCase__    :		Union[str, Any]=True		,				UpperCAmelCase__    :		List[Any]=False		,				UpperCAmelCase__    :		Any=10		,				UpperCAmelCase__    :		List[Any]=3		,				UpperCAmelCase__    :		int=32 * 8		,				UpperCAmelCase__    :		Union[str, Any]=32 * 8		,				UpperCAmelCase__    :		int=4		,				UpperCAmelCase__    :		Any=64		,				):
										'''simple docstring'''
										lowercase   :       List[Any]								=parent
										lowercase   :       Optional[int]								=batch_size
										lowercase   :       Optional[int]								=is_training
										lowercase   :       str								=use_auxiliary_loss
										lowercase   :       int								=num_queries
										lowercase   :       Dict								=num_channels
										lowercase   :       str								=min_size
										lowercase   :       Optional[int]								=max_size
										lowercase   :       Any								=num_labels
										lowercase   :       Any								=hidden_dim
										lowercase   :       List[Any]								=hidden_dim







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
										    UpperCAmelCase__      )

										lowercase   :       str								=torch.ones([self.batch_size, self.min_size, self.max_size]		,				device=UpperCAmelCase__      )

										lowercase   :       List[str]								=(
										    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,				device=UpperCAmelCase__      ) > 0.5
										).float()
										lowercase   :       Any								=(torch.rand((self.batch_size, self.num_labels)		,				device=UpperCAmelCase__      ) > 0.5).long()

										lowercase   :       Tuple								=self.get_config()
										return config, pixel_values, pixel_mask, mask_labels, class_labels







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=MaskaFormerConfig(
										    hidden_size=self.hidden_dim		,				)
										lowercase   :       List[str]								=self.num_queries
										lowercase   :       Tuple								=self.num_labels

										lowercase   :       Tuple								=[1, 1, 1, 1]
										lowercase   :       Tuple								=self.num_channels

										lowercase   :       Any								=64
										lowercase   :       List[Any]								=128
										lowercase   :       Union[str, Any]								=self.hidden_dim
										lowercase   :       str								=self.hidden_dim
										lowercase   :       Dict								=self.hidden_dim
										return config







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase   :       int								=self.prepare_config_and_inputs()
										lowercase   :       List[str]								={'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
										return config, inputs_dict







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       str								=output.encoder_hidden_states
										lowercase   :       Optional[Any]								=output.pixel_decoder_hidden_states
										lowercase   :       List[Any]								=output.transformer_decoder_hidden_states

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







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		int=False      ):
										'''simple docstring'''
										with torch.no_grad():
																lowercase   :       int								=MaskaFormerModel(config=UpperCAmelCase__      )
																model.to(UpperCAmelCase__      )
																model.eval()

																lowercase   :       List[Any]								=model(pixel_values=UpperCAmelCase__		,				pixel_mask=UpperCAmelCase__      )
																lowercase   :       Dict								=model(UpperCAmelCase__		,				output_hidden_states=UpperCAmelCase__      )

										self.parent.assertEqual(
										    output.transformer_decoder_last_hidden_state.shape		,				(self.batch_size, self.num_queries, self.hidden_dim)		,				)
										# 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(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       int								=MaskaFormerForUniversalSegmentation(config=UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()

										def comm_check_on_output(UpperCAmelCase__    :		Union[str, 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():
																lowercase   :       Optional[Any]								=model(pixel_values=UpperCAmelCase__		,				pixel_mask=UpperCAmelCase__      )
																lowercase   :       List[Any]								=model(UpperCAmelCase__      )

																comm_check_on_output(UpperCAmelCase__      )

																lowercase   :       str								=model(
																    pixel_values=UpperCAmelCase__		,				pixel_mask=UpperCAmelCase__		,				mask_labels=UpperCAmelCase__		,				class_labels=UpperCAmelCase__      )

										comm_check_on_output(UpperCAmelCase__      )

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





@require_torch
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				lowerCamelCase_							=						{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=MaskaFormerModelTester(self      )
										lowercase   :       Optional[Any]								=ConfigTester(self		,				config_class=UpperCAmelCase__		,				has_text_modality=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(UpperCAmelCase__		,				**UpperCAmelCase__		,				output_hidden_states=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCAmelCase__      )







				@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''      )
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										pass







				@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''      )
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										pass







				@unittest.skip(reason='''Mask2Former is not a generative model'''      )
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										pass







				@unittest.skip(reason='''Mask2Former does not use token embeddings'''      )
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										pass







				@require_torch_multi_gpu
				@unittest.skip(
				    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''      )
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										pass







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







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Any								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase   :       Tuple								=model_class(UpperCAmelCase__      )
																lowercase   :       int								=inspect.signature(model.forward      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowercase   :       List[str]								=[*signature.parameters.keys()]

																lowercase   :       Tuple								=['''pixel_values''']
																self.assertListEqual(arg_names[:1]		,				UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																lowercase   :       Dict								=MaskaFormerModel.from_pretrained(UpperCAmelCase__      )
																self.assertIsNotNone(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       int								=(self.model_tester.min_size,) * 2
										lowercase   :       int								={
										    '''pixel_values''': torch.randn((2, 3, *size)		,				device=UpperCAmelCase__      ),
										    '''mask_labels''': torch.randn((2, 10, *size)		,				device=UpperCAmelCase__      ),
										    '''class_labels''': torch.zeros(2		,				10		,				device=UpperCAmelCase__      ).long(),
										}
										lowercase   :       Tuple								=self.model_tester.get_config()

										lowercase   :       List[Any]								=MaskaFormerForUniversalSegmentation(UpperCAmelCase__      ).to(UpperCAmelCase__      )
										lowercase   :       List[str]								=model(**UpperCAmelCase__      )
										self.assertTrue(outputs.loss is not None      )







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(UpperCAmelCase__		,				**UpperCAmelCase__		,				output_hidden_states=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       int								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase   :       Dict								=model_class(UpperCAmelCase__      ).to(UpperCAmelCase__      )
																lowercase   :       Any								=model(**UpperCAmelCase__		,				output_attentions=UpperCAmelCase__      )
																self.assertTrue(outputs.attentions is not None      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										if not self.model_tester.is_training:
																return

										lowercase   :       Optional[Any]								=self.all_model_classes[1]
										lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase   :       Dict								=self.model_tester.prepare_config_and_inputs()

										lowercase   :       str								=model_class(UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.train()

										lowercase   :       Union[str, Any]								=model(UpperCAmelCase__		,				mask_labels=UpperCAmelCase__		,				class_labels=UpperCAmelCase__      ).loss
										loss.backward()







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.all_model_classes[1]
										lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase   :       Dict								=self.model_tester.prepare_config_and_inputs()
										lowercase   :       str								=True
										lowercase   :       Tuple								=True

										lowercase   :       Union[str, Any]								=model_class(UpperCAmelCase__      ).to(UpperCAmelCase__      )
										model.train()

										lowercase   :       int								=model(UpperCAmelCase__		,				mask_labels=UpperCAmelCase__		,				class_labels=UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=outputs.encoder_hidden_states[0]
										encoder_hidden_states.retain_grad()

										lowercase   :       int								=outputs.pixel_decoder_hidden_states[0]
										pixel_decoder_hidden_states.retain_grad()

										lowercase   :       Optional[Any]								=outputs.transformer_decoder_hidden_states[0]
										transformer_decoder_hidden_states.retain_grad()

										lowercase   :       Dict								=outputs.attentions[0]
										attentions.retain_grad()

										outputs.loss.backward(retain_graph=UpperCAmelCase__      )

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



UpperCamelCase_							=					1E-4






def 							_lowerCAmelCase (      )							->     List[str]:
						lowercase   :       List[Any]								=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
						return image







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







				@cached_property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return "facebook/mask2former-swin-small-coco-instance"







				@cached_property
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       int								=MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(UpperCAmelCase__      )
										lowercase   :       Dict								=self.default_image_processor
										lowercase   :       Optional[int]								=prepare_img()
										lowercase   :       Any								=image_processor(UpperCAmelCase__		,				return_tensors='''pt'''      ).to(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=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(UpperCAmelCase__		,				(1, 3, 384, 384)      )

										with torch.no_grad():
																lowercase   :       Union[str, Any]								=model(**UpperCAmelCase__      )

										lowercase   :       List[Any]								=torch.tensor(
										    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]      ).to(UpperCAmelCase__      )
										self.assertTrue(
										    torch.allclose(
										        outputs.encoder_last_hidden_state[0, 0, :3, :3]		,				UpperCAmelCase__		,				atol=UpperCAmelCase__      )      )

										lowercase   :       Optional[int]								=torch.tensor(
										    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]      ).to(UpperCAmelCase__      )
										self.assertTrue(
										    torch.allclose(
										        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,				UpperCAmelCase__		,				atol=UpperCAmelCase__      )      )

										lowercase   :       int								=torch.tensor(
										    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]      ).to(UpperCAmelCase__      )
										self.assertTrue(
										    torch.allclose(
										        outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,				UpperCAmelCase__		,				atol=UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(UpperCAmelCase__      ).eval()
										lowercase   :       str								=self.default_image_processor
										lowercase   :       str								=prepare_img()
										lowercase   :       Tuple								=image_processor(UpperCAmelCase__		,				return_tensors='''pt'''      ).to(UpperCAmelCase__      )
										lowercase   :       List[str]								=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(UpperCAmelCase__		,				(1, 3, 384, 384)      )

										with torch.no_grad():
																lowercase   :       Optional[Any]								=model(**UpperCAmelCase__      )
										# masks_queries_logits
										lowercase   :       List[Any]								=outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape		,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
										lowercase   :       List[Any]								=[
										    [-8.78_39, -9.00_56, -8.81_21],
										    [-7.41_04, -7.03_13, -6.54_01],
										    [-6.61_05, -6.34_27, -6.46_75],
										]
										lowercase   :       Union[str, Any]								=torch.tensor(UpperCAmelCase__      ).to(UpperCAmelCase__      )
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,				UpperCAmelCase__		,				atol=UpperCAmelCase__      )      )
										# class_queries_logits
										lowercase   :       Dict								=outputs.class_queries_logits
										self.assertEqual(class_queries_logits.shape		,				(1, model.config.num_queries, model.config.num_labels + 1)      )
										lowercase   :       Optional[Any]								=torch.tensor(
										    [
										        [1.83_24, -8.08_35, -4.19_22],
										        [0.84_50, -9.00_50, -3.60_53],
										        [0.30_45, -7.72_93, -3.02_75],
										    ]      ).to(UpperCAmelCase__      )
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,				UpperCAmelCase__		,				atol=UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[str]								=MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(UpperCAmelCase__      ).eval()
										lowercase   :       int								=self.default_image_processor

										lowercase   :       Tuple								=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'''		,				)

										lowercase   :       Tuple								=inputs['''pixel_values'''].to(UpperCAmelCase__      )
										lowercase   :       Dict								=[el.to(UpperCAmelCase__      ) for el in inputs['''mask_labels''']]
										lowercase   :       Union[str, Any]								=[el.to(UpperCAmelCase__      ) for el in inputs['''class_labels''']]

										with torch.no_grad():
																lowercase   :       List[str]								=model(**UpperCAmelCase__      )

										self.assertTrue(outputs.loss is not None      )








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int = 1000000	)							->     int:
						lowercase   :       Dict								=set(range(3      ,   __magic_name__      ,   2	)	)
						primes.add(2	)
						for p in range(3      ,   __magic_name__      ,   2	):
												if p not in primes:
																		continue
												primes.difference_update(set(range(p * p      ,   __magic_name__      ,   __magic_name__	)	)	)

						lowercase   :       List[Any]								=[float(__magic_name__	) for n in range(limit + 1	)]

						for p in primes:
												for n in range(__magic_name__      ,   limit + 1      ,   __magic_name__	):
																		phi[n] *= 1 - 1 / p

						return int(sum(phi[2:]	)	)


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





