




"""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 lowerCAmelCase__			:






			'''simple docstring'''



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

							_lowerCamelCase  :    List[Any]					=					torch.ones([self.batch_size, self.min_size, self.max_size]						,   device=_a						)

							_lowerCamelCase  :    Any					=					(
							    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,   device=_a						) > 0.5
							).float()
							_lowerCamelCase  :    List[str]					=					(torch.rand((self.batch_size, self.num_labels)						,   device=_a						) > 0.5).long()

							_lowerCamelCase  :    int					=					self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					MaskaFormerConfig(
							    hidden_size=self.hidden_dim						,   )
							_lowerCamelCase  :    Optional[Any]					=					self.num_queries
							_lowerCamelCase  :    Any					=					self.num_labels

							_lowerCamelCase  :    Dict					=					[1, 1, 1, 1]
							_lowerCamelCase  :    str					=					self.num_channels

							_lowerCamelCase  :    List[Any]					=					64
							_lowerCamelCase  :    Optional[Any]					=					128
							_lowerCamelCase  :    List[str]					=					self.hidden_dim
							_lowerCamelCase  :    str					=					self.hidden_dim
							_lowerCamelCase  :    List[Any]					=					self.hidden_dim
							return config
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					self.prepare_config_and_inputs()
							_lowerCamelCase  :    int					=					{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
							return config, inputs_dict
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    int					=					output.encoder_hidden_states
							_lowerCamelCase  :    Tuple					=					output.pixel_decoder_hidden_states
							_lowerCamelCase  :    str					=					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       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=False						):




							with torch.no_grad():
											_lowerCamelCase  :    Optional[Any]					=					MaskaFormerModel(config=_a						)
											model.to(_a						)
											model.eval()

											_lowerCamelCase  :    Dict					=					model(pixel_values=_a						,   pixel_mask=_a						)
											_lowerCamelCase  :    str					=					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       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Dict					=					MaskaFormerForUniversalSegmentation(config=_a						)
							model.to(_a						)
							model.eval()

							def comm_check_on_output(lowercase						):
											# 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  :    Union[str, Any]					=					model(pixel_values=_a						,   pixel_mask=_a						)
											_lowerCamelCase  :    Dict					=					model(_a						)

											comm_check_on_output(_a						)

											_lowerCamelCase  :    Union[str, Any]					=					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__			(  __SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			lowerCamelCase__   =    {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

			lowerCamelCase__   =    False
			lowerCamelCase__   =    False
			lowerCamelCase__   =    False
			lowerCamelCase__   =    False
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					MaskaFormerModelTester(self						)
							_lowerCamelCase  :    Dict					=					ConfigTester(self						,   config_class=_a						,   has_text_modality=_a						)
			def       A_					(						self						):
							self.config_tester.run_common_tests()
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(_a						,   **_a						,   output_hidden_states=_a						)
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					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       A_					(						self						):
							pass
			@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'						)
			def       A_					(						self						):
							pass
			@unittest.skip(reason='Mask2Former is not a generative model'						)
			def       A_					(						self						):
							pass
			@unittest.skip(reason='Mask2Former does not use token embeddings'						)
			def       A_					(						self						):
							pass
			@require_torch_multi_gpu
			@unittest.skip(
			    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'						)
			def       A_					(						self						):
							pass
			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.model_tester.prepare_config_and_inputs_for_common()

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

											_lowerCamelCase  :    Optional[int]					=					["""pixel_values"""]
											self.assertListEqual(arg_names[:1]						,   _a						)
			@slow
			def       A_					(						self						):




							for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											_lowerCamelCase  :    str					=					MaskaFormerModel.from_pretrained(_a						)
											self.assertIsNotNone(_a						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					(self.model_tester.min_size,) * 2
							_lowerCamelCase  :    Any					=					{
							    """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(),
							}
							_lowerCamelCase  :    Optional[Any]					=					self.model_tester.get_config()

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

							for model_class in self.all_model_classes:
											_lowerCamelCase  :    Dict					=					model_class(_a						).to(_a						)
											_lowerCamelCase  :    Optional[int]					=					model(**_a						,   output_attentions=_a						)
											self.assertTrue(outputs.attentions is not None						)
			def       A_					(						self						):




							if not self.model_tester.is_training:
											return

							_lowerCamelCase  :    List[Any]					=					self.all_model_classes[1]
							_lowerCamelCase  :    int					=					self.model_tester.prepare_config_and_inputs()

							_lowerCamelCase  :    Tuple					=					model_class(_a						)
							model.to(_a						)
							model.train()

							_lowerCamelCase  :    Dict					=					model(_a						,   mask_labels=_a						,   class_labels=_a						).loss
							loss.backward()






			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					self.all_model_classes[1]
							_lowerCamelCase  :    List[str]					=					self.model_tester.prepare_config_and_inputs()
							_lowerCamelCase  :    int					=					True
							_lowerCamelCase  :    Tuple					=					True

							_lowerCamelCase  :    Union[str, Any]					=					model_class(_a						).to(_a						)
							model.train()

							_lowerCamelCase  :    Dict					=					model(_a						,   mask_labels=_a						,   class_labels=_a						)

							_lowerCamelCase  :    Optional[int]					=					outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

							_lowerCamelCase  :    List[Any]					=					outputs.pixel_decoder_hidden_states[0]
							pixel_decoder_hidden_states.retain_grad()

							_lowerCamelCase  :    int					=					outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							_lowerCamelCase  :    List[str]					=					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						)

lowercase__  		=		1E-4







def   _snake_case    (       ):
				_lowerCamelCase  :    List[str]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
				return image






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






			'''simple docstring'''



			@cached_property
			def       A_					(						self						):
							return "facebook/mask2former-swin-small-coco-instance"
			@cached_property
			def       A_					(						self						):
							return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(_a						)
							_lowerCamelCase  :    Dict					=					self.default_image_processor
							_lowerCamelCase  :    Optional[int]					=					prepare_img()
							_lowerCamelCase  :    Union[str, Any]					=					image_processor(_a						,   return_tensors='pt'						).to(_a						)
							_lowerCamelCase  :    str					=					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():
											_lowerCamelCase  :    Union[str, Any]					=					model(**_a						)

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

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

							_lowerCamelCase  :    str					=					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(_a						)
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,   _a						,   atol=_a						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(_a						).eval()
							_lowerCamelCase  :    str					=					self.default_image_processor
							_lowerCamelCase  :    str					=					prepare_img()
							_lowerCamelCase  :    str					=					image_processor(_a						,   return_tensors='pt'						).to(_a						)
							_lowerCamelCase  :    Optional[Any]					=					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():
											_lowerCamelCase  :    str					=					model(**_a						)
							# masks_queries_logits
							_lowerCamelCase  :    Dict					=					outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape						,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
							_lowerCamelCase  :    List[str]					=					[
							    [-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],
							]
							_lowerCamelCase  :    Any					=					torch.tensor(_a						).to(_a						)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,   _a						,   atol=_a						)						)
							# class_queries_logits
							_lowerCamelCase  :    List[str]					=					outputs.class_queries_logits
							self.assertEqual(class_queries_logits.shape						,   (1, model.config.num_queries, model.config.num_labels + 1)						)
							_lowerCamelCase  :    Dict					=					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(_a						)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,   _a						,   atol=_a						)						)






			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(_a						).eval()
							_lowerCamelCase  :    Optional[int]					=					self.default_image_processor

							_lowerCamelCase  :    Tuple					=					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  :    Optional[int]					=					inputs["""pixel_values"""].to(_a						)
							_lowerCamelCase  :    int					=					[el.to(_a						) for el in inputs["""mask_labels"""]]
							_lowerCamelCase  :    Any					=					[el.to(_a						) for el in inputs["""class_labels"""]]

							with torch.no_grad():
											_lowerCamelCase  :    Tuple					=					model(**_a						)

							self.assertTrue(outputs.loss is not None						)





"""simple docstring"""






def   _snake_case    (       lowercase__ = 1000	):
				_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					1, 1
				_lowerCamelCase  :    int					=					[]
				for i in range(1							,					n + 1	):
								_lowerCamelCase  :    Tuple					=					prev_numerator + 2 * prev_denominator
								_lowerCamelCase  :    str					=					prev_numerator + prev_denominator
								if len(str(lowercase__	)	) > len(str(lowercase__	)	):
												result.append(lowercase__	)
								_lowerCamelCase  :    Dict					=					numerator
								_lowerCamelCase  :    Optional[int]					=					denominator

				return len(lowercase__	)


if __name__ == "__main__":
			print(F"{solution() = }")