





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,       A,       A=2,       A=True,       A=False,       A=10,       A=3,       A=32 * 8,       A=32 * 8,       A=4,       A=64,       ):





												"""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              =						hidden_dim
												_UpperCamelCase              =						hidden_dim

					def 	_UpperCamelCase     (      self      ):





												"""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 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase              =						MaskaFormerConfig(
												    hidden_size=self.hidden_dim,       )
												_UpperCamelCase              =						self.num_queries
												_UpperCamelCase              =						self.num_labels

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

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

					def 	_UpperCamelCase     (      self      ):





												"""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 	_UpperCamelCase     (      self,       A,       A      ):





												"""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_layers      )

					def 	_UpperCamelCase     (      self,       A,       A,       A,       A=False      ):





												"""simple docstring"""

												with torch.no_grad():
																			_UpperCamelCase              =						MaskaFormerModel(config=lowerCamelCase_      )
																			model.to(lowerCamelCase_      )
																			model.eval()

																			_UpperCamelCase              =						model(pixel_values=lowerCamelCase_,       pixel_mask=lowerCamelCase_      )
																			_UpperCamelCase              =						model(lowerCamelCase_,       output_hidden_states=lowerCamelCase_      )

												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(lowerCamelCase_,       lowerCamelCase_      )



					def 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A      ):





												"""simple docstring"""

												_UpperCamelCase              =						MaskaFormerForUniversalSegmentation(config=lowerCamelCase_      )
												model.to(lowerCamelCase_      )
												model.eval()

												def comm_check_on_output(A      ):
																			# 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  __A(						a__						,			a__						,			unittest.TestCase ):
					__A	  =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
					__A	  =       {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

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

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase              =						MaskaFormerModelTester(self      )
												_UpperCamelCase              =						ConfigTester(self,       config_class=lowerCamelCase_,       has_text_modality=lowerCamelCase_      )

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												self.config_tester.run_common_tests()

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase       ,		_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_,       **lowerCamelCase_,       output_hidden_states=lowerCamelCase_      )

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase_      )

					@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''      )
					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												pass

					@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''      )
					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												pass

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





												"""simple docstring"""

												pass

					@unittest.skip(reason='''Mask2Former does not use token embeddings'''      )
					def 	_UpperCamelCase     (      self      ):





												"""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 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												pass

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





												"""simple docstring"""

												pass

					def 	_UpperCamelCase     (      self      ):





												"""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 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																			_UpperCamelCase              =						MaskaFormerModel.from_pretrained(lowerCamelCase_      )
																			self.assertIsNotNone(lowerCamelCase_      )

					def 	_UpperCamelCase     (      self      ):





												"""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              =						self.model_tester.get_config()

												_UpperCamelCase              =						MaskaFormerForUniversalSegmentation(lowerCamelCase_      ).to(lowerCamelCase_      )
												_UpperCamelCase              =						model(**lowerCamelCase_      )
												self.assertTrue(outputs.loss is not None      )

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase       ,		_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_,       **lowerCamelCase_,       output_hidden_states=lowerCamelCase_      )

					def 	_UpperCamelCase     (      self      ):





												"""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 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												if not self.model_tester.is_training:
																			return

												_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 	_UpperCamelCase     (      self      ):





												"""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_      ).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()

												_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    : List[str]    	=  1e-4


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

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

					@cached_property
					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												return "facebook/mask2former-swin-small-coco-instance"

					@cached_property
					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase              =						MaskaFormerModel.from_pretrained(self.model_checkpoints      ).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, 384, 384)      )

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

												_UpperCamelCase              =						torch.tensor(
												    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]      ).to(lowerCamelCase_      )
												self.assertTrue(
												    torch.allclose(
												        outputs.encoder_last_hidden_state[0, 0, :3, :3],       lowerCamelCase_,       atol=lowerCamelCase_      )      )

												_UpperCamelCase              =						torch.tensor(
												    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]      ).to(lowerCamelCase_      )
												self.assertTrue(
												    torch.allclose(
												        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],       lowerCamelCase_,       atol=lowerCamelCase_      )      )

												_UpperCamelCase              =						torch.tensor(
												    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]      ).to(lowerCamelCase_      )
												self.assertTrue(
												    torch.allclose(
												        outputs.transformer_decoder_last_hidden_state[0, :3, :3],       lowerCamelCase_,       atol=lowerCamelCase_      )      )

					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase              =						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).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, 384, 384)      )

												with torch.no_grad():
																			_UpperCamelCase              =						model(**lowerCamelCase_      )
												# masks_queries_logits
												_UpperCamelCase              =						outputs.masks_queries_logits
												self.assertEqual(
												    masks_queries_logits.shape,       (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
												_UpperCamelCase              =						[
												    [-8.7_839, -9.0_056, -8.8_121],
												    [-7.4_104, -7.0_313, -6.5_401],
												    [-6.6_105, -6.3_427, -6.4_675],
												]
												_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.num_queries, model.config.num_labels + 1)      )
												_UpperCamelCase              =						torch.tensor(
												    [
												        [1.8_324, -8.0_835, -4.1_922],
												        [0.8_450, -9.0_050, -3.6_053],
												        [0.3_045, -7.7_293, -3.0_275],
												    ]      ).to(lowerCamelCase_      )
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],       lowerCamelCase_,       atol=lowerCamelCase_      )      )



					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												_UpperCamelCase              =						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).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      )













from math import factorial


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 20					):
       _UpperCamelCase              =						2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
       # 2, 3,...
       _UpperCamelCase              =						n // 2

       return int(factorial(lowerCAmelCase					) / (factorial(lowerCAmelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
       import sys

       if len(sys.argv) == 1:
              print(solution(20))
       else:
              try:
                     lowercase    : Dict    	=  int(sys.argv[1])
                     print(solution(n))
              except ValueError:
                     print("""Invalid entry - please enter a number.""")






