
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        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Any						,	snake_case_ :       str						,	snake_case_ :       Dict=2						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       Tuple=False						,	snake_case_ :       Union[str, Any]=10						,	snake_case_ :       int=3						,	snake_case_ :       Dict=32 * 4						,	snake_case_ :       str=32 * 6						,	snake_case_ :       Optional[int]=4						,	snake_case_ :       Tuple=32						,	):
							UpperCamelCase_:							Tuple	=						parent
							UpperCamelCase_:							Any	=						batch_size
							UpperCamelCase_:							str	=						is_training
							UpperCamelCase_:							str	=						use_auxiliary_loss
							UpperCamelCase_:							Tuple	=						num_queries
							UpperCamelCase_:							int	=						num_channels
							UpperCamelCase_:							Union[str, Any]	=						min_size
							UpperCamelCase_:							Dict	=						max_size
							UpperCamelCase_:							Dict	=						num_labels
							UpperCamelCase_:							Any	=						mask_feature_size

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							str	=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
							    snake_case_      )

							UpperCamelCase_:							Optional[int]	=						torch.ones([self.batch_size, self.min_size, self.max_size]						,	device=snake_case_      )

							UpperCamelCase_:							Tuple	=						(
							    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,	device=snake_case_      ) > 0.5
							).float()
							UpperCamelCase_:							Dict	=						(torch.rand((self.batch_size, self.num_labels)						,	device=snake_case_      ) > 0.5).long()

							UpperCamelCase_:							Any	=						self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							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_:							List[str]	=						self.prepare_config_and_inputs()
							UpperCamelCase_:							int	=						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
							return config, inputs_dict

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       List[str]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						output.encoder_hidden_states
							UpperCamelCase_:							Any	=						output.pixel_decoder_hidden_states
							UpperCamelCase_:							Optional[int]	=						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 :       Optional[int]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       int						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Union[str, Any]=False      ):
							with torch.no_grad():
											UpperCamelCase_:							int	=						MaskFormerModel(config=snake_case_      )
											model.to(snake_case_      )
											model.eval()

											UpperCamelCase_:							str	=						model(pixel_values=snake_case_						,	pixel_mask=snake_case_      )
											UpperCamelCase_:							Any	=						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 :       List[str]						,	snake_case_ :       Tuple						,	snake_case_ :       Any						,	snake_case_ :       List[Any]						,	snake_case_ :       List[str]						,	snake_case_ :       Any      ):
							UpperCamelCase_:							List[Any]	=						MaskFormerForInstanceSegmentation(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()

							def comm_check_on_output(snake_case_ :       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():
											UpperCamelCase_:							Any	=						model(pixel_values=snake_case_						,	pixel_mask=snake_case_      )
											UpperCamelCase_:							int	=						model(snake_case_      )

											comm_check_on_output(snake_case_      )

											UpperCamelCase_:							Optional[int]	=						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        _UpperCamelCase				(					_A     ,       _A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						int					      = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
			__UpperCamelCase      :						Optional[int]					      = (
			    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
			    if is_torch_available()
			    else {}
			)

			__UpperCamelCase      :						Tuple					      = False
			__UpperCamelCase      :						str					      = False
			__UpperCamelCase      :						str					      = False
			__UpperCamelCase      :						List[str]					      = False

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[Any]	=						MaskFormerModelTester(self      )
							UpperCamelCase_:							Optional[Any]	=						ConfigTester(self						,	config_class=snake_case_						,	has_text_modality=snake_case_      )

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

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_,   UpperCamelCase_:							str	=						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_:							int	=						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 :       Dict      ):
							pass

			@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""      )
			def       lowerCAmelCase__	(       self :       Tuple      ):
							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 :       int      ):
							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[str]      ):
							pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""      )
			def       lowerCAmelCase__	(       self :       int      ):
							pass

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

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

											UpperCamelCase_:							Optional[Any]	=						["""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_:							int	=						MaskFormerModel.from_pretrained(snake_case_      )
											self.assertIsNotNone(snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							int	=						(self.model_tester.min_size,) * 2
							UpperCamelCase_:							Union[str, Any]	=						{
							    """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_:							Dict	=						MaskFormerForInstanceSegmentation(MaskFormerConfig()      ).to(snake_case_      )
							UpperCamelCase_:							List[Any]	=						model(**snake_case_      )
							self.assertTrue(outputs.loss is not None      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_,   UpperCamelCase_:							Tuple	=						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_,   UpperCamelCase_:							int	=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:							Optional[Any]	=						model_class(snake_case_      ).to(snake_case_      )
											UpperCamelCase_:							str	=						model(**snake_case_						,	output_attentions=snake_case_      )
											self.assertTrue(outputs.attentions is not None      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							if not self.model_tester.is_training:
											return
							# only MaskFormerForInstanceSegmentation has the loss
							UpperCamelCase_:							Optional[Any]	=						self.all_model_classes[1]
							UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Tuple	=						self.model_tester.prepare_config_and_inputs()

							UpperCamelCase_:							Any	=						model_class(snake_case_      )
							model.to(snake_case_      )
							model.train()

							UpperCamelCase_:							Optional[Any]	=						model(snake_case_						,	mask_labels=snake_case_						,	class_labels=snake_case_      ).loss
							loss.backward()




			def       lowerCAmelCase__	(       self :       List[str]      ):
							# only MaskFormerForInstanceSegmentation has the loss
							UpperCamelCase_:							int	=						self.all_model_classes[1]
							UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Any	=						self.model_tester.prepare_config_and_inputs()
							UpperCamelCase_:							Any	=						True
							UpperCamelCase_:							List[str]	=						True

							UpperCamelCase_:							Optional[int]	=						model_class(snake_case_      )
							model.to(snake_case_      )
							model.train()

							UpperCamelCase_:							Optional[Any]	=						model(snake_case_						,	mask_labels=snake_case_						,	class_labels=snake_case_      )

							UpperCamelCase_:							List[Any]	=						outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

							UpperCamelCase_:							int	=						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_:							str	=						outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							UpperCamelCase_:							Tuple	=						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      )




lowerCamelCase_						: List[str]         =  1E-4







def        A__					(						)  ->	Tuple:
				UpperCamelCase_:							List[str]	=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
				return image





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






			'''simple docstring'''

			@cached_property
			def       lowerCAmelCase__	(       self :       int      ):
							return (
							    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
							    if is_vision_available()
							    else None
							)

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""      ).to(snake_case_      )
							UpperCamelCase_:							Any	=						self.default_image_processor
							UpperCamelCase_:							Any	=						prepare_img()
							UpperCamelCase_:							Any	=						image_processor(snake_case_						,	return_tensors="""pt"""      ).to(snake_case_      )
							UpperCamelCase_:							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(snake_case_						,	(1, 3, 800, 1088)      )

							with torch.no_grad():
											UpperCamelCase_:							Optional[Any]	=						model(**snake_case_      )

							UpperCamelCase_:							Dict	=						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_:							Tuple	=						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_:							Dict	=						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 :       Any      ):
							UpperCamelCase_:							str	=						(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
							    .to(snake_case_      )
							    .eval()
							)
							UpperCamelCase_:							Tuple	=						self.default_image_processor
							UpperCamelCase_:							Tuple	=						prepare_img()
							UpperCamelCase_:							Dict	=						image_processor(snake_case_						,	return_tensors="""pt"""      ).to(snake_case_      )
							UpperCamelCase_:							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(snake_case_						,	(1, 3, 800, 1088)      )

							with torch.no_grad():
											UpperCamelCase_:							int	=						model(**snake_case_      )
							# masks_queries_logits
							UpperCamelCase_:							List[Any]	=						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_:							str	=						[
							    [-1.373_7124, -1.772_4937, -1.936_4233],
							    [-1.597_7281, -1.986_7939, -2.152_3695],
							    [-1.579_5398, -1.926_9832, -2.09_3942],
							]
							UpperCamelCase_:							List[str]	=						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_:							Optional[Any]	=						outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape						,	(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
							UpperCamelCase_:							Union[str, Any]	=						torch.tensor(
							    [
							        [1.6_512e00, -5.2_572e00, -3.3_519e00],
							        [3.6_169e-02, -5.9_025e00, -2.9_313e00],
							        [1.0_766e-04, -7.7_630e00, -5.1_263e00],
							    ]      ).to(snake_case_      )
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,	snake_case_						,	atol=snake_case_      )      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Dict	=						(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""      )
							    .to(snake_case_      )
							    .eval()
							)
							UpperCamelCase_:							str	=						self.default_image_processor
							UpperCamelCase_:							List[Any]	=						prepare_img()
							UpperCamelCase_:							Union[str, Any]	=						image_processor(snake_case_						,	return_tensors="""pt"""      ).to(snake_case_      )
							UpperCamelCase_:							Tuple	=						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_:							Optional[Any]	=						model(**snake_case_      )
							# masks_queries_logits
							UpperCamelCase_:							Optional[Any]	=						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_:							Tuple	=						[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
							UpperCamelCase_:							Dict	=						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_:							Any	=						outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape						,	(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
							UpperCamelCase_:							List[Any]	=						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 :       Optional[int]      ):
							UpperCamelCase_:							Dict	=						(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
							    .to(snake_case_      )
							    .eval()
							)
							UpperCamelCase_:							Optional[Any]	=						self.default_image_processor

							UpperCamelCase_:							Any	=						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_:							Any	=						inputs["""pixel_values"""].to(snake_case_      )
							UpperCamelCase_:							Any	=						[el.to(snake_case_      ) for el in inputs["""mask_labels"""]]
							UpperCamelCase_:							Union[str, Any]	=						[el.to(snake_case_      ) for el in inputs["""class_labels"""]]

							with torch.no_grad():
											UpperCamelCase_:							str	=						model(**snake_case_      )

							self.assertTrue(outputs.loss is not None      )








def        A__					(						lowerCamelCase = 50						)  ->	int:
				UpperCamelCase_:							List[Any]	=						[[0] * 3 for _ in range(length + 1						)]

				for row_length in range(length + 1						):
								for tile_length in range(2     ,    5						):
												for tile_start in range(row_length - tile_length + 1						):
																different_colour_ways_number[row_length][tile_length - 2] += (
																    different_colour_ways_number[row_length - tile_start - tile_length][
																        tile_length - 2
																    ]
																    + 1
																)

				return sum(different_colour_ways_number[length]						)


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






