




"""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 lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=2    , lowercase=True    , lowercase=False    , lowercase=10    , lowercase=3    , lowercase=32 * 8    , lowercase=32 * 8    , lowercase=4    , lowercase=64    , ):
							_lowerCamelCase	:     Optional[Any]       =       parent
							_lowerCamelCase	:     List[Any]       =       batch_size
							_lowerCamelCase	:     Optional[Any]       =       is_training
							_lowerCamelCase	:     str       =       use_auxiliary_loss
							_lowerCamelCase	:     Dict       =       num_queries
							_lowerCamelCase	:     List[str]       =       num_channels
							_lowerCamelCase	:     Dict       =       min_size
							_lowerCamelCase	:     str       =       max_size
							_lowerCamelCase	:     Union[str, Any]       =       num_labels
							_lowerCamelCase	:     Any       =       hidden_dim
							_lowerCamelCase	:     List[str]       =       hidden_dim
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
							    a__    )

							_lowerCamelCase	:     Optional[Any]       =       torch.ones([self.batch_size, self.min_size, self.max_size]    , device=a__    )

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

							_lowerCamelCase	:     Dict       =       self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       MaskaFormerConfig(
							    hidden_size=self.hidden_dim    , )
							_lowerCamelCase	:     Any       =       self.num_queries
							_lowerCamelCase	:     Optional[int]       =       self.num_labels

							_lowerCamelCase	:     int       =       [1, 1, 1, 1]
							_lowerCamelCase	:     Dict       =       self.num_channels

							_lowerCamelCase	:     List[str]       =       64
							_lowerCamelCase	:     Tuple       =       128
							_lowerCamelCase	:     Optional[Any]       =       self.hidden_dim
							_lowerCamelCase	:     List[str]       =       self.hidden_dim
							_lowerCamelCase	:     Tuple       =       self.hidden_dim
							return config
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Optional[Any]       =       self.prepare_config_and_inputs()
							_lowerCamelCase	:     List[str]       =       {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
							return config, inputs_dict
			def    A_       (    self    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Tuple       =       output.encoder_hidden_states
							_lowerCamelCase	:     str       =       output.pixel_decoder_hidden_states
							_lowerCamelCase	:     Dict       =       output.transformer_decoder_hidden_states

							self.parent.assertTrue(len(a__    )    , len(config.backbone_config.depths    )    )
							self.parent.assertTrue(len(a__    )    , len(config.backbone_config.depths    )    )
							self.parent.assertTrue(len(a__    )    , config.decoder_layers    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase=False    ):
							with torch.no_grad():
											_lowerCamelCase	:     Any       =       MaskaFormerModel(config=a__    )
											model.to(a__    )
											model.eval()

											_lowerCamelCase	:     Optional[int]       =       model(pixel_values=a__    , pixel_mask=a__    )
											_lowerCamelCase	:     Optional[Any]       =       model(a__    , output_hidden_states=a__    )

							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(a__    , a__    )






			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       MaskaFormerForUniversalSegmentation(config=a__    )
							model.to(a__    )
							model.eval()

							def comm_check_on_output(lowercase    ):
											# 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():
											_lowerCamelCase	:     int       =       model(pixel_values=a__    , pixel_mask=a__    )
											_lowerCamelCase	:     str       =       model(a__    )

											comm_check_on_output(a__    )

											_lowerCamelCase	:     List[str]       =       model(
											    pixel_values=a__    , pixel_mask=a__    , mask_labels=a__    , class_labels=a__    )

							comm_check_on_output(a__    )

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






@require_torch
class lowerCAmelCase__      (	A__,      A__,      unittest.TestCase		):






			'''simple docstring'''



			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    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       MaskaFormerModelTester(self    )
							_lowerCamelCase	:     str       =       ConfigTester(self    , config_class=a__    , has_text_modality=a__    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(a__    , **a__    , output_hidden_states=a__    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*a__    )
			@unittest.skip(reason='Mask2Former does not use inputs_embeds'    )
			def    A_       (    self    ):
							pass
			@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'    )
			def    A_       (    self    ):
							pass
			@unittest.skip(reason='Mask2Former is not a generative model'    )
			def    A_       (    self    ):
							pass
			@unittest.skip(reason='Mask2Former does not use token embeddings'    )
			def    A_       (    self    ):
							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    ):
							pass
			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Dict       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase	:     List[Any]       =       model_class(a__    )
											_lowerCamelCase	:     Dict       =       inspect.signature(model.forward    )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_lowerCamelCase	:     int       =       [*signature.parameters.keys()]

											_lowerCamelCase	:     Tuple       =       ['pixel_values']
											self.assertListEqual(arg_names[:1]    , a__    )
			@slow
			def    A_       (    self    ):
							for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											_lowerCamelCase	:     List[Any]       =       MaskaFormerModel.from_pretrained(a__    )
											self.assertIsNotNone(a__    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       (self.model_tester.min_size,) * 2
							_lowerCamelCase	:     Optional[Any]       =       {
							    'pixel_values': torch.randn((2, 3, *size)    , device=a__    ),
							    'mask_labels': torch.randn((2, 10, *size)    , device=a__    ),
							    'class_labels': torch.zeros(2    , 10    , device=a__    ).long(),
							}
							_lowerCamelCase	:     Optional[int]       =       self.model_tester.get_config()

							_lowerCamelCase	:     Tuple       =       MaskaFormerForUniversalSegmentation(a__    ).to(a__    )
							_lowerCamelCase	:     Optional[int]       =       model(**a__    )
							self.assertTrue(outputs.loss is not None    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(a__    , **a__    , output_hidden_states=a__    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase	:     Optional[Any]       =       model_class(a__    ).to(a__    )
											_lowerCamelCase	:     Optional[int]       =       model(**a__    , output_attentions=a__    )
											self.assertTrue(outputs.attentions is not None    )
			def    A_       (    self    ):
							if not self.model_tester.is_training:
											return

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

							_lowerCamelCase	:     List[Any]       =       model_class(a__    )
							model.to(a__    )
							model.train()

							_lowerCamelCase	:     Any       =       model(a__    , mask_labels=a__    , class_labels=a__    ).loss
							loss.backward()






			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.all_model_classes[1]
							_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     int       =       self.model_tester.prepare_config_and_inputs()
							_lowerCamelCase	:     Tuple       =       True
							_lowerCamelCase	:     int       =       True

							_lowerCamelCase	:     Any       =       model_class(a__    ).to(a__    )
							model.train()

							_lowerCamelCase	:     List[str]       =       model(a__    , mask_labels=a__    , class_labels=a__    )

							_lowerCamelCase	:     Tuple       =       outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

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

							_lowerCamelCase	:     List[str]       =       outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							_lowerCamelCase	:     Tuple       =       outputs.attentions[0]
							attentions.retain_grad()

							outputs.loss.backward(retain_graph=a__    )

							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__			=		1E-4







def 	_snake_case     (							):
				_lowerCamelCase	:     Optional[int]       =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
				return image






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






			'''simple docstring'''



			@cached_property
			def    A_       (    self    ):
							return "facebook/mask2former-swin-small-coco-instance"
			@cached_property
			def    A_       (    self    ):
							return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints    ) if is_vision_available() else None
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       MaskaFormerModel.from_pretrained(self.model_checkpoints    ).to(a__    )
							_lowerCamelCase	:     Optional[Any]       =       self.default_image_processor
							_lowerCamelCase	:     Tuple       =       prepare_img()
							_lowerCamelCase	:     int       =       image_processor(a__    , return_tensors='pt'    ).to(a__    )
							_lowerCamelCase	:     Optional[int]       =       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(a__    , (1, 3, 384, 384)    )

							with torch.no_grad():
											_lowerCamelCase	:     Dict       =       model(**a__    )

							_lowerCamelCase	:     Union[str, 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(a__    )
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3]    , a__    , atol=a__    )    )

							_lowerCamelCase	:     Dict       =       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(a__    )
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    , a__    , atol=a__    )    )

							_lowerCamelCase	:     Dict       =       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(a__    )
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    , a__    , atol=a__    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(a__    ).eval()
							_lowerCamelCase	:     Optional[Any]       =       self.default_image_processor
							_lowerCamelCase	:     int       =       prepare_img()
							_lowerCamelCase	:     Optional[int]       =       image_processor(a__    , return_tensors='pt'    ).to(a__    )
							_lowerCamelCase	:     List[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(a__    , (1, 3, 384, 384)    )

							with torch.no_grad():
											_lowerCamelCase	:     Dict       =       model(**a__    )
							# masks_queries_logits
							_lowerCamelCase	:     int       =       outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape    , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    )
							_lowerCamelCase	:     int       =       [
							    [-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],
							]
							_lowerCamelCase	:     List[str]       =       torch.tensor(a__    ).to(a__    )
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    , a__    , atol=a__    )    )
							# class_queries_logits
							_lowerCamelCase	:     Any       =       outputs.class_queries_logits
							self.assertEqual(class_queries_logits.shape    , (1, model.config.num_queries, model.config.num_labels + 1)    )
							_lowerCamelCase	:     str       =       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(a__    )
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    , a__    , atol=a__    )    )






			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(a__    ).eval()
							_lowerCamelCase	:     Tuple       =       self.default_image_processor

							_lowerCamelCase	:     Dict       =       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'    , )

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

							with torch.no_grad():
											_lowerCamelCase	:     Tuple       =       model(**a__    )

							self.assertTrue(outputs.loss is not None    )




def 		lowerCAmelCase__     (       UpperCamelCase_					:							int						)->	int:
    A__					=       1
    for i in range(1			,							num + 1						):
        fact *= i
    return fact



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int						)->	int:
    A__					=       0
    while number > 0:
        A__					=       number % 1_0
        sum_of_digits += last_digit
        A__					=       number // 1_0  # Removing the last_digit from the given number
    return sum_of_digits



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int = 1_0_0						)->	int:
    A__					=       factorial(UpperCamelCase_						)
    A__					=       split_and_add(UpperCamelCase_						)
    return result


if __name__ == "__main__":
   print(solution(int(input("Enter the Number: ").strip())))
