



"""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 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=2	,       snake_case=True	,       snake_case=False	,       snake_case=10	,       snake_case=3	,       snake_case=32 * 4	,       snake_case=32 * 6	,       snake_case=4	,       snake_case=32	,       )			->							Optional[Any]:
									_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        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
									    snake_case						)

									_UpperCAmelCase							 =		torch.ones([self.batch_size, self.min_size, self.max_size]	,       device=snake_case						)

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

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






							def        lowerCamelCase_   (  self						)			->							Dict:
									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        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		self.prepare_config_and_inputs()
									_UpperCAmelCase							 =		{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
									return config, inputs_dict






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		output.encoder_hidden_states
									_UpperCAmelCase							 =		output.pixel_decoder_hidden_states
									_UpperCAmelCase							 =		output.transformer_decoder_hidden_states

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case=False						)			->							int:
									with torch.no_grad():
											_UpperCAmelCase							 =		MaskFormerModel(config=snake_case						)
											model.to(snake_case						)
											model.eval()

											_UpperCAmelCase							 =		model(pixel_values=snake_case	,       pixel_mask=snake_case						)
											_UpperCAmelCase							 =		model(snake_case	,       output_hidden_states=snake_case						)
									# 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(snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		MaskFormerForInstanceSegmentation(config=snake_case						)
									model.to(snake_case						)
									model.eval()

									def comm_check_on_output(snake_case						):
											# 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=snake_case	,       pixel_mask=snake_case						)
											_UpperCAmelCase							 =		model(snake_case						)

											comm_check_on_output(snake_case						)

											_UpperCAmelCase							 =		model(
											    pixel_values=snake_case	,       pixel_mask=snake_case	,       mask_labels=snake_case	,       class_labels=snake_case						)

									comm_check_on_output(snake_case						)

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







@require_torch
class 			lowercase__			(      A,					A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							_UpperCAmelCase      				=					(
							    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		MaskFormerModelTester(self						)
									_UpperCAmelCase							 =		ConfigTester(self	,       config_class=snake_case	,       has_text_modality=snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									self.config_tester.run_common_tests()






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(snake_case	,       **snake_case	,       output_hidden_states=snake_case						)






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case						)






							@unittest.skip(reason='MaskFormer does not use inputs_embeds'						)
							def        lowerCamelCase_   (  self						)			->							str:
									pass






							@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'						)
							def        lowerCamelCase_   (  self						)			->							str:
									pass






							@unittest.skip(reason='MaskFormer is not a generative model'						)
							def        lowerCamelCase_   (  self						)			->							Any:
									pass






							@unittest.skip(reason='MaskFormer does not use token embeddings'						)
							def        lowerCamelCase_   (  self						)			->							Tuple:
									pass






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






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






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_UpperCAmelCase							 =		model_class(snake_case						)
											_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]	,       snake_case						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									for model_name in ["facebook/maskformer-swin-small-coco"]:
											_UpperCAmelCase							 =		MaskFormerModel.from_pretrained(snake_case						)
											self.assertIsNotNone(snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		(self.model_tester.min_size,) * 2
									_UpperCAmelCase							 =		{
									    'pixel_values': torch.randn((2, 3, *size)	,       device=snake_case						),
									    'mask_labels': torch.randn((2, 10, *size)	,       device=snake_case						),
									    'class_labels': torch.zeros(2	,       10	,       device=snake_case						).long(),
									}

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






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(snake_case	,       **snake_case	,       output_hidden_states=snake_case						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

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






							def        lowerCamelCase_   (  self						)			->							Any:
									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(snake_case						)
									model.to(snake_case						)
									model.train()

									_UpperCAmelCase							 =		model(snake_case	,       mask_labels=snake_case	,       class_labels=snake_case						).loss
									loss.backward()






							def        lowerCamelCase_   (  self						)			->							Dict:
									# only MaskFormerForInstanceSegmentation has the loss
									_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(snake_case						)
									model.to(snake_case						)
									model.train()

									_UpperCAmelCase							 =		model(snake_case	,       mask_labels=snake_case	,       class_labels=snake_case						)

									_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=snake_case						)

									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 	UpperCAmelCase     (		):

		'''simple docstring'''


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






@require_vision
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							@cached_property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return (
									    MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'						)
									    if is_vision_available()
									    else None
									)






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'						).to(snake_case						)
									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(snake_case	,       return_tensors='pt'						).to(snake_case						)
									_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(snake_case	,       (1, 3, 800, 1088)						)

									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)

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

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

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






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'						)
									    .to(snake_case						)
									    .eval()
									)
									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(snake_case	,       return_tensors='pt'						).to(snake_case						)
									_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(snake_case	,       (1, 3, 800, 1088)						)

									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)
									# 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(snake_case						).to(snake_case						)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,       snake_case	,       atol=snake_case						)						)
									# 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(
									    [
									        [1.65_12E00, -5.25_72E00, -3.35_19E00],
									        [3.61_69E-02, -5.90_25E00, -2.93_13E00],
									        [1.07_66E-04, -7.76_30E00, -5.12_63E00],
									    ]						).to(snake_case						)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,       snake_case	,       atol=snake_case						)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'						)
									    .to(snake_case						)
									    .eval()
									)
									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(snake_case	,       return_tensors='pt'						).to(snake_case						)
									_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(snake_case	,       (1, 3, 800, 1088)						)

									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)
									# 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(snake_case						).to(snake_case						)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,       snake_case	,       atol=snake_case						)						)
									# 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(snake_case						)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,       snake_case	,       atol=snake_case						)						)






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'						)
									    .to(snake_case						)
									    .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(snake_case						)
									_UpperCAmelCase							 =		[el.to(snake_case						) for el in inputs['mask_labels']]
									_UpperCAmelCase							 =		[el.to(snake_case						) for el in inputs['class_labels']]

									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)

									self.assertTrue(outputs.loss is not None						)






"""simple docstring"""





def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[[0 for _ in range(A       )] for _ in range(m + 1       )]
		for i in range(m + 1       ):
				_UpperCAmelCase							 =		1

		for n in range(m + 1       ):
				for k in range(1     ,				A       ):
						memo[n][k] += memo[n][k - 1]
						if n - k > 0:
								memo[n][k] += memo[n - k - 1][k]

		return memo[m][m - 1]


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										try:
															lowercase  							=					int(input('''Enter a number: ''').strip())
															print(partition(n))
										except ValueError:
															print('''Please enter a number.''')
					else:
										try:
															lowercase  							=					int(sys.argv[1])
															print(partition(n))
										except ValueError:
															print('''Please pass a number.''')


