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_     :







							def __init__(   self      :     Union[str, Any] ,			_A      :     List[Any] ,			_A      :     List[Any]=2 ,			_A      :     Tuple=True ,			_A      :     Any=False ,			_A      :     Dict=10 ,			_A      :     Optional[int]=3 ,			_A      :     Union[str, Any]=32 * 8 ,			_A      :     Optional[Any]=32 * 8 ,			_A      :     List[str]=4 ,			_A      :     Dict=64 ,			):
												_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      :     List[Any]		):
												_UpperCamelCase			     =     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
												    _A		)

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

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

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







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

																	_UpperCamelCase			     =     model(pixel_values=_A ,			pixel_mask=_A		)
																	_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     Any ,			_A      :     Any ,			_A      :     Any ,			_A      :     Tuple		):
												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation(config=_A		)
												model.to(_A		)
												model.eval()

												def comm_check_on_output(_A      :     List[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():
																	_UpperCamelCase			     =     model(pixel_values=_A ,			pixel_mask=_A		)
																	_UpperCamelCase			     =     model(_A		)

																	comm_check_on_output(_A		)

																	_UpperCamelCase			     =     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_     (       __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							UpperCAmelCase				   =     {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     MaskaFormerModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			has_text_modality=_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskaformer_model(_A ,			**_A ,			output_hidden_states=_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Dict		):
												pass







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







							@unittest.skip(reason='''Mask2Former is not a generative model'''		)
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												pass







							@unittest.skip(reason='''Mask2Former does not use token embeddings'''		)
							def  UpperCamelCase_						(   self      :     List[str]		):
												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      :     Tuple		):
												pass







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







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase			     =     model_class(_A		)
																	_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] ,			_A		)







							@slow
							def  UpperCamelCase_						(   self      :     int		):
												for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																	_UpperCamelCase			     =     MaskaFormerModel.from_pretrained(_A		)
																	self.assertIsNotNone(_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     (self.model_tester.min_size,) * 2
												_UpperCamelCase			     =     {
												    '''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(),
												}
												_UpperCamelCase			     =     self.model_tester.get_config()

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







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskaformer_model(_A ,			**_A ,			output_hidden_states=_A		)







							def  UpperCamelCase_						(   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(_A		).to(_A		)
																	_UpperCamelCase			     =     model(**_A ,			output_attentions=_A		)
																	self.assertTrue(outputs.attentions is not None		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												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(_A		)
												model.to(_A		)
												model.train()

												_UpperCamelCase			     =     model(_A ,			mask_labels=_A ,			class_labels=_A		).loss
												loss.backward()







							def  UpperCamelCase_						(   self      :     Tuple		):
												_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(_A		).to(_A		)
												model.train()

												_UpperCamelCase			     =     model(_A ,			mask_labels=_A ,			class_labels=_A		)

												_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=_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		)




_lowerCAmelCase       =		1E-4
def 					_snake_case	(  ):
					_UpperCamelCase			     =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
					return image







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







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







							@cached_property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(_A		)
												_UpperCamelCase			     =     self.default_image_processor
												_UpperCamelCase			     =     prepare_img()
												_UpperCamelCase			     =     image_processor(_A ,			return_tensors='''pt'''		).to(_A		)
												_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(_A ,			(1, 3, 384, 384)		)

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

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

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

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







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_A		).eval()
												_UpperCamelCase			     =     self.default_image_processor
												_UpperCamelCase			     =     prepare_img()
												_UpperCamelCase			     =     image_processor(_A ,			return_tensors='''pt'''		).to(_A		)
												_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(_A ,			(1, 3, 384, 384)		)

												with torch.no_grad():
																	_UpperCamelCase			     =     model(**_A		)
												# 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			     =     [
												    [-8.7839, -9.0056, -8.8121],
												    [-7.4104, -7.0313, -6.5401],
												    [-6.6105, -6.3427, -6.4675],
												]
												_UpperCamelCase			     =     torch.tensor(_A		).to(_A		)
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,			_A ,			atol=_A		)		)
												# 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(
												    [
												        [1.8324, -8.0835, -4.1922],
												        [0.8450, -9.0050, -3.6053],
												        [0.3045, -7.7293, -3.0275],
												    ]		).to(_A		)
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,			_A ,			atol=_A		)		)







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_A		).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(_A		)
												_UpperCamelCase			     =     [el.to(_A		) for el in inputs['''mask_labels''']]
												_UpperCamelCase			     =     [el.to(_A		) for el in inputs['''class_labels''']]

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

												self.assertTrue(outputs.loss is not None		)








def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if not head:
										return True
					# split the list to two parts
					__a      ,			__a					:						Any      =					head.next, head
					while fast and fast.next:
										__a					:						Optional[int]      =					fast.next.next
										__a					:						Optional[int]      =					slow.next
					__a					:						Optional[int]      =					slow.next
					__a					:						Tuple      =					None  # Don't forget here! But forget still works!
					# reverse the second part
					__a					:						Any      =					None
					while second:
										__a					:						int      =					second.next
										__a					:						int      =					node
										__a					:						Union[str, Any]      =					second
										__a					:						Union[str, Any]      =					nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										__a					:						int      =					node.next
										__a					:						Dict      =					head.next
					return True







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					if not head or not head.next:
										return True
					__a					:						Optional[int]      =					{}
					__a					:						Optional[int]      =					0
					while head:
										if head.val in d:
															d[head.val].append(__A      )
										else:
															__a					:						Dict      =					[pos]
										__a					:						Dict      =					head.next
										pos += 1
					__a					:						Dict      =					pos - 1
					__a					:						Optional[int]      =					0
					for v in d.values():
										if len(__A      ) % 2 != 0:
															middle += 1
										else:
															__a					:						Dict      =					0
															for i in range(0				,       len(__A      )      ):
																				if v[i] + v[len(__A      ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True





