




"""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       SCREAMING_SNAKE_CASE__					:


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


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
										    lowerCAmelCase					)

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

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

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


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				MaskaFormerConfig(
										    hidden_size=self.hidden_dim						,						)
										lowerCAmelCase  =				self.num_queries
										lowerCAmelCase  =				self.num_labels

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

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


						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				self.prepare_config_and_inputs()
										lowerCAmelCase  =				{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
										return config, inputs_dict


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				output.encoder_hidden_states
										lowerCAmelCase  =				output.pixel_decoder_hidden_states
										lowerCAmelCase  =				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   __lowercase (							self				:			Any						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]=False					):
										with torch.no_grad():
														lowerCAmelCase  =				MaskaFormerModel(config=lowerCAmelCase					)
														model.to(lowerCAmelCase					)
														model.eval()

														lowerCAmelCase  =				model(pixel_values=lowerCAmelCase						,						pixel_mask=lowerCAmelCase					)
														lowerCAmelCase  =				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   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				MaskaFormerForUniversalSegmentation(config=lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()

										def comm_check_on_output(lowerCAmelCase				:			Union[str, Any]					):
														# 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  =				model(pixel_values=lowerCAmelCase						,						pixel_mask=lowerCAmelCase					)
														lowerCAmelCase  =				model(lowerCAmelCase					)

														comm_check_on_output(lowerCAmelCase					)

														lowerCAmelCase  =				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       SCREAMING_SNAKE_CASE__					(					_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   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				MaskaFormerModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										self.config_tester.run_common_tests()


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase						,						**lowerCAmelCase						,						output_hidden_states=lowerCAmelCase					)


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				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   __lowercase (							self				:			List[str]					):
										pass


						@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""					)
						def   __lowercase (							self				:			Union[str, Any]					):
										pass


						@unittest.skip(reason="""Mask2Former is not a generative model"""					)
						def   __lowercase (							self				:			str					):
										pass


						@unittest.skip(reason="""Mask2Former does not use token embeddings"""					)
						def   __lowercase (							self				:			Dict					):
										pass


						@require_torch_multi_gpu
						@unittest.skip(
						    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""					)
						def   __lowercase (							self				:			List[Any]					):
										pass


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


						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase  =				["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,						lowerCAmelCase					)


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


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				(self.model_tester.min_size,) * 2
										lowerCAmelCase  =				{
										    """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(),
										}
										lowerCAmelCase  =				self.model_tester.get_config()

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


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase						,						**lowerCAmelCase						,						output_hidden_states=lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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


						def   __lowercase (							self				:			List[Any]					):
										if not self.model_tester.is_training:
														return

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

										lowerCAmelCase  =				model_class(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.train()

										lowerCAmelCase  =				model(lowerCAmelCase						,						mask_labels=lowerCAmelCase						,						class_labels=lowerCAmelCase					).loss
										loss.backward()




						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.all_model_classes[1]
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										lowerCAmelCase  =				True
										lowerCAmelCase  =				True

										lowerCAmelCase  =				model_class(lowerCAmelCase					).to(lowerCAmelCase					)
										model.train()

										lowerCAmelCase  =				model(lowerCAmelCase						,						mask_labels=lowerCAmelCase						,						class_labels=lowerCAmelCase					)

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

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

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

										lowerCAmelCase  =				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					)





a      =							1e-4





def 							lowercase					()     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image






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


						@cached_property
						def   __lowercase (							self				:			Dict					):
										return "facebook/mask2former-swin-small-coco-instance"


						@cached_property
						def   __lowercase (							self				:			int					):
										return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints					) if is_vision_available() else None


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase					)
										lowerCAmelCase  =				self.default_image_processor
										lowerCAmelCase  =				prepare_img()
										lowerCAmelCase  =				image_processor(lowerCAmelCase						,						return_tensors="""pt"""					).to(lowerCAmelCase					)
										lowerCAmelCase  =				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():
														lowerCAmelCase  =				model(**lowerCAmelCase					)

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

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

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


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase					).eval()
										lowerCAmelCase  =				self.default_image_processor
										lowerCAmelCase  =				prepare_img()
										lowerCAmelCase  =				image_processor(lowerCAmelCase						,						return_tensors="""pt"""					).to(lowerCAmelCase					)
										lowerCAmelCase  =				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():
														lowerCAmelCase  =				model(**lowerCAmelCase					)
										# masks_queries_logits
										lowerCAmelCase  =				outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape						,						(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					)
										lowerCAmelCase  =				[
										    [-8.7839, -9.0056, -8.8121],
										    [-7.4104, -7.0313, -6.5401],
										    [-6.6105, -6.3427, -6.4675],
										]
										lowerCAmelCase  =				torch.tensor(lowerCAmelCase					).to(lowerCAmelCase					)
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,						lowerCAmelCase						,						atol=lowerCAmelCase					)					)
										# class_queries_logits
										lowerCAmelCase  =				outputs.class_queries_logits
										self.assertEqual(class_queries_logits.shape						,						(1, model.config.num_queries, model.config.num_labels + 1)					)
										lowerCAmelCase  =				torch.tensor(
										    [
										        [1.8324, -8.0835, -4.1922],
										        [0.8450, -9.0050, -3.6053],
										        [0.3045, -7.7293, -3.0275],
										    ]					).to(lowerCAmelCase					)
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,						lowerCAmelCase						,						atol=lowerCAmelCase					)					)




						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(lowerCAmelCase					).eval()
										lowerCAmelCase  =				self.default_image_processor

										lowerCAmelCase  =				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  =				inputs["""pixel_values"""].to(lowerCAmelCase					)
										lowerCAmelCase  =				[el.to(lowerCAmelCase					) for el in inputs["""mask_labels"""]]
										lowerCAmelCase  =				[el.to(lowerCAmelCase					) for el in inputs["""class_labels"""]]

										with torch.no_grad():
														lowerCAmelCase  =				model(**lowerCAmelCase					)

										self.assertTrue(outputs.loss is not None					)











"""simple docstring"""







from __future__ import annotations





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if (direction == 1 and array[indexa] > array[indexa]) or (
				    direction == 0 and array[indexa] < array[indexa]
				):
								lowerCAmelCase ,						lowerCAmelCase  =				array[indexa], array[indexa]





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if length > 1:
								lowerCAmelCase  =				int(length / 2 )
								for i in range(snake_case__      ,       low + middle ):
												comp_and_swap(snake_case__      ,       snake_case__      ,       i + middle      ,       snake_case__ )
								bitonic_merge(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )
								bitonic_merge(snake_case__      ,       low + middle      ,       snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if length > 1:
								lowerCAmelCase  =				int(length / 2 )
								bitonic_sort(snake_case__      ,       snake_case__      ,       snake_case__      ,       1 )
								bitonic_sort(snake_case__      ,       low + middle      ,       snake_case__      ,       0 )
								bitonic_merge(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		a      =							input('Enter numbers separated by a comma:\n').strip()
		a      =							[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=', ')





