




'''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 						a				:


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


				def   A_ (		self    :					int					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
									    lowercase_					)

									snake_case_									=				torch.ones([self.batch_size, self.min_size, self.max_size]       ,				device=lowercase_					)

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

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


				def   A_ (		self    :					int					):
									snake_case_									=				MaskaFormerConfig(
									    hidden_size=self.hidden_dim       ,				)
									snake_case_									=				self.num_queries
									snake_case_									=				self.num_labels

									snake_case_									=				[1, 1, 1, 1]
									snake_case_									=				self.num_channels

									snake_case_									=				64
									snake_case_									=				128
									snake_case_									=				self.hidden_dim
									snake_case_									=				self.hidden_dim
									snake_case_									=				self.hidden_dim
									return config


				def   A_ (		self    :					Optional[Any]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.prepare_config_and_inputs()
									snake_case_									=				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
									return config, inputs_dict


				def   A_ (		self    :					Any       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[Any]					):
									snake_case_									=				output.encoder_hidden_states
									snake_case_									=				output.pixel_decoder_hidden_states
									snake_case_									=				output.transformer_decoder_hidden_states

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


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Dict       ,				lowercase_    :					Dict       ,				lowercase_    :					List[Any]=False					):
									with torch.no_grad():
														snake_case_									=				MaskaFormerModel(config=lowercase_					)
														model.to(lowercase_					)
														model.eval()

														snake_case_									=				model(pixel_values=lowercase_       ,				pixel_mask=lowercase_					)
														snake_case_									=				model(lowercase_       ,				output_hidden_states=lowercase_					)

									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(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					int       ,				lowercase_    :					Dict					):
									snake_case_									=				MaskaFormerForUniversalSegmentation(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()

									def comm_check_on_output(lowercase_    :					Tuple					):
														# 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():
														snake_case_									=				model(pixel_values=lowercase_       ,				pixel_mask=lowercase_					)
														snake_case_									=				model(lowercase_					)

														comm_check_on_output(lowercase_					)

														snake_case_									=				model(
														    pixel_values=lowercase_       ,				pixel_mask=lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					)

									comm_check_on_output(lowercase_					)

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







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				snake_case_      =		{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				MaskaFormerModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_					)


				def   A_ (		self    :					int					):
									self.config_tester.run_common_tests()


				def   A_ (		self    :					str					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(lowercase_       ,				**lowercase_       ,				output_hidden_states=lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase_					)


				@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''					)
				def   A_ (		self    :					Optional[Any]					):
									pass


				@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''					)
				def   A_ (		self    :					Optional[Any]					):
									pass


				@unittest.skip(reason='''Mask2Former is not a generative model'''					)
				def   A_ (		self    :					Dict					):
									pass


				@unittest.skip(reason='''Mask2Former does not use token embeddings'''					)
				def   A_ (		self    :					Union[str, Any]					):
									pass


				@require_torch_multi_gpu
				@unittest.skip(
				    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''					)
				def   A_ (		self    :					str					):
									pass


				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
				def   A_ (		self    :					List[Any]					):
									pass


				def   A_ (		self    :					Tuple					):
									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(lowercase_					)
														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]       ,				lowercase_					)


				@slow
				def   A_ (		self    :					Any					):
									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														snake_case_									=				MaskaFormerModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)


				def   A_ (		self    :					List[str]					):
									snake_case_									=				(self.model_tester.min_size,) * 2
									snake_case_									=				{
									    '''pixel_values''': torch.randn((2, 3, *size)       ,				device=lowercase_					),
									    '''mask_labels''': torch.randn((2, 10, *size)       ,				device=lowercase_					),
									    '''class_labels''': torch.zeros(2       ,				10       ,				device=lowercase_					).long(),
									}
									snake_case_									=				self.model_tester.get_config()

									snake_case_									=				MaskaFormerForUniversalSegmentation(lowercase_					).to(lowercase_					)
									snake_case_									=				model(**lowercase_					)
									self.assertTrue(outputs.loss is not None					)


				def   A_ (		self    :					Dict					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(lowercase_       ,				**lowercase_       ,				output_hidden_states=lowercase_					)


				def   A_ (		self    :					Union[str, Any]					):
									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(lowercase_					).to(lowercase_					)
														snake_case_									=				model(**lowercase_       ,				output_attentions=lowercase_					)
														self.assertTrue(outputs.attentions is not None					)


				def   A_ (		self    :					Optional[int]					):
									if not self.model_tester.is_training:
														return

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

									snake_case_									=				model_class(lowercase_					)
									model.to(lowercase_					)
									model.train()

									snake_case_									=				model(lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					).loss
									loss.backward()


				def   A_ (		self    :					List[str]					):
									snake_case_									=				self.all_model_classes[1]
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs()
									snake_case_									=				True
									snake_case_									=				True

									snake_case_									=				model_class(lowercase_					).to(lowercase_					)
									model.train()

									snake_case_									=				model(lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					)

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

									snake_case_									=				outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									snake_case_									=				outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

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

									outputs.loss.backward(retain_graph=lowercase_					)

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







a  :				int									=  1E-4


def 		__magic_name__    (	)		->					Dict:

					'''simple docstring'''

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






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


				@cached_property
				def   A_ (		self    :					List[Any]					):
									return "facebook/mask2former-swin-small-coco-instance"


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


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(lowercase_					)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				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(lowercase_       ,				(1, 3, 384, 384)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									snake_case_									=				torch.tensor(
									    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)

									snake_case_									=				torch.tensor(
									    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)

									snake_case_									=				torch.tensor(
									    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowercase_					).eval()
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				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(lowercase_       ,				(1, 3, 384, 384)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)
									# masks_queries_logits
									snake_case_									=				outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape       ,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					)
									snake_case_									=				[
									    [-8.7839, -9.0056, -8.8121],
									    [-7.4104, -7.0313, -6.5401],
									    [-6.6105, -6.3427, -6.4675],
									]
									snake_case_									=				torch.tensor(lowercase_					).to(lowercase_					)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)
									# class_queries_logits
									snake_case_									=				outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape       ,				(1, model.config.num_queries, model.config.num_labels + 1)					)
									snake_case_									=				torch.tensor(
									    [
									        [1.8324, -8.0835, -4.1922],
									        [0.8450, -9.0050, -3.6053],
									        [0.3045, -7.7293, -3.0275],
									    ]					).to(lowercase_					)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowercase_					).eval()
									snake_case_									=				self.default_image_processor

									snake_case_									=				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'''       ,				)

									snake_case_									=				inputs['''pixel_values'''].to(lowercase_					)
									snake_case_									=				[el.to(lowercase_					) for el in inputs['''mask_labels''']]
									snake_case_									=				[el.to(lowercase_					) for el in inputs['''class_labels''']]

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									self.assertTrue(outputs.loss is not None					)



from math import ceil


def     __lowerCAmelCase			(			a__ = 1001  )       ->  int:
				__a										=    1

				for i in range(1						,    int(ceil(n / 2.0  )  )  ):
								__a										=    2 * i + 1
								__a										=    2 * i
								__a										=    total + 4 * odd**2 - 6 * even

				return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															A     :	List[Any]    = int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')