


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

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

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

												a__					=					self.get_config()
												return config, pixel_values, pixel_mask, mask_labels, class_labels
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					MaskaFormerConfig(
												    hidden_size=self.hidden_dim      ,)
												a__					=					self.num_queries
												a__					=					self.num_labels

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

												a__					=					64
												a__					=					1_28
												a__					=					self.hidden_dim
												a__					=					self.hidden_dim
												a__					=					self.hidden_dim
												return config
					def 			lowerCamelCase__(				self       :Any				) ->   List[Any]:
												a__				, a__				, a__				, a__				, a__					=					self.prepare_config_and_inputs()
												a__					=					{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
												return config, inputs_dict
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :List[Any]      ,__snake_case       :int				) ->   Union[str, Any]:
												a__					=					output.encoder_hidden_states
												a__					=					output.pixel_decoder_hidden_states
												a__					=					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 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Any      ,__snake_case       :List[Any]      ,__snake_case       :Dict=False				) ->   List[str]:
												with torch.no_grad():
																			a__					=					MaskaFormerModel(config=_lowerCamelCase				)
																			model.to(_lowerCamelCase				)
																			model.eval()

																			a__					=					model(pixel_values=_lowerCamelCase      ,pixel_mask=_lowerCamelCase				)
																			a__					=					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 			lowerCamelCase__(				self       :int      ,__snake_case       :Dict      ,__snake_case       :List[Any]      ,__snake_case       :Any      ,__snake_case       :str      ,__snake_case       :Union[str, Any]				) ->   Optional[int]:
												a__					=					MaskaFormerForUniversalSegmentation(config=_lowerCamelCase				)
												model.to(_lowerCamelCase				)
												model.eval()

												def comm_check_on_output(__snake_case       :List[str]				):
																			# 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():
																			a__					=					model(pixel_values=_lowerCamelCase      ,pixel_mask=_lowerCamelCase				)
																			a__					=					model(_lowerCamelCase				)

																			comm_check_on_output(_lowerCamelCase				)

																			a__					=					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 	snake_case_       (A_     ,						A_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Optional[int]   				=      (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
					UpperCAmelCase__ :   int   				=      {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

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

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

																			a__					=					['pixel_values']
																			self.assertListEqual(arg_names[:1]      ,_lowerCamelCase				)
					@slow
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[str]:
												for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																			a__					=					MaskaFormerModel.from_pretrained(_lowerCamelCase				)
																			self.assertIsNotNone(_lowerCamelCase				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[int]:
												a__					=					(self.model_tester.min_size,) * 2
												a__					=					{
												    '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(),
												}
												a__					=					self.model_tester.get_config()

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

												for model_class in self.all_model_classes:
																			a__					=					model_class(_lowerCamelCase				).to(_lowerCamelCase				)
																			a__					=					model(**_lowerCamelCase      ,output_attentions=_lowerCamelCase				)
																			self.assertTrue(outputs.attentions is not None				)
					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												if not self.model_tester.is_training:
																			return

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

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

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



					def 			lowerCamelCase__(				self       :Optional[int]				) ->   int:
												a__					=					self.all_model_classes[1]
												a__				, a__				, a__				, a__				, a__					=					self.model_tester.prepare_config_and_inputs()
												a__					=					True
												a__					=					True

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

												a__					=					model(_lowerCamelCase      ,mask_labels=_lowerCamelCase      ,class_labels=_lowerCamelCase				)

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

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

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

												a__					=					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				)





snake_case							:	str					      =				1e-4






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







@require_vision
@slow
class 	snake_case_       (unittest.TestCase				):
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[Any]:
												return "facebook/mask2former-swin-small-coco-instance"
					@cached_property
					def 			lowerCamelCase__(				self       :int				) ->   Tuple:
												return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None
					def 			lowerCamelCase__(				self       :Any				) ->   Dict:
												a__					=					MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(_lowerCamelCase				)
												a__					=					self.default_image_processor
												a__					=					prepare_img()
												a__					=					image_processor(_lowerCamelCase      ,return_tensors='pt'				).to(_lowerCamelCase				)
												a__					=					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, 3_84, 3_84)				)

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

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

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

												a__					=					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(_lowerCamelCase				)
												self.assertTrue(
												    torch.allclose(
												        outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,_lowerCamelCase      ,atol=_lowerCamelCase				)				)
					def 			lowerCamelCase__(				self       :str				) ->   List[str]:
												a__					=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(_lowerCamelCase				).eval()
												a__					=					self.default_image_processor
												a__					=					prepare_img()
												a__					=					image_processor(_lowerCamelCase      ,return_tensors='pt'				).to(_lowerCamelCase				)
												a__					=					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, 3_84, 3_84)				)

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



					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Optional[int]:
												a__					=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(_lowerCamelCase				).eval()
												a__					=					self.default_image_processor

												a__					=					image_processor(
												    [np.zeros((3, 8_00, 13_33)				), np.zeros((3, 8_00, 13_33)				)]      ,segmentation_maps=[np.zeros((3_84, 3_84)				).astype(np.floataa				), np.zeros((3_84, 3_84)				).astype(np.floataa				)]      ,return_tensors='pt'      ,)

												a__					=					inputs['pixel_values'].to(_lowerCamelCase				)
												a__					=					[el.to(_lowerCamelCase				) for el in inputs['mask_labels']]
												a__					=					[el.to(_lowerCamelCase				) for el in inputs['class_labels']]

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

												self.assertTrue(outputs.loss is not None				)








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if (direction == 1 and array[indexa] > array[indexa]) or (
							    direction == 0 and array[indexa] < array[indexa]
							):
														a__				, a__					=					array[indexa], array[indexa]






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														for i in range(__lowerCAmelCase ,   low + middle       ):
																					comp_and_swap(__lowerCAmelCase ,   __lowerCAmelCase ,   i + middle ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   __lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														bitonic_sort(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   1       )
														bitonic_sort(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   0       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				input('''Enter numbers separated by a comma:\n''').strip()
						snake_case							:	Optional[int]					      =				[int(item.strip()) for item in user_input.split(''',''')]

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print('''\nSorted array in ascending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print('''Sorted array in descending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')




