



"""simple docstring"""

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 SCREAMING_SNAKE_CASE__					:



						def __init__(self						,     _lowercase						,     _lowercase=2						,     _lowercase=True						,     _lowercase=False						,     _lowercase=10						,     _lowercase=3						,     _lowercase=32 * 4						,     _lowercase=32 * 6						,     _lowercase=4						,     _lowercase=32						,     ):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       parent
													__a		:		Union[str, Any]									=       batch_size
													__a		:		List[str]									=       is_training
													__a		:		Optional[Any]									=       use_auxiliary_loss
													__a		:		List[str]									=       num_queries
													__a		:		Tuple									=       num_channels
													__a		:		Any									=       min_size
													__a		:		int									=       max_size
													__a		:		str									=       num_labels
													__a		:		str									=       mask_feature_size



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
													    _lowercase							)

													__a		:		Dict									=       torch.ones([self.batch_size, self.min_size, self.max_size]						,     device=_lowercase							)

													__a		:		str									=       (
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,     device=_lowercase							) > 0.5
													).float()
													__a		:		Dict									=       (torch.rand((self.batch_size, self.num_labels)						,     device=_lowercase							) > 0.5).long()

													__a		:		Optional[Any]									=       self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													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							):





													'''simple docstring'''

													__a		:		List[str]									=       self.prepare_config_and_inputs()
													__a		:		Dict									=       {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
													return config, inputs_dict



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[int]									=       output.encoder_hidden_states
													__a		:		Optional[Any]									=       output.pixel_decoder_hidden_states
													__a		:		Optional[Any]									=       output.transformer_decoder_hidden_states

													self.parent.assertTrue(len(_lowercase							)						,     len(config.backbone_config.depths							)							)
													self.parent.assertTrue(len(_lowercase							)						,     len(config.backbone_config.depths							)							)
													self.parent.assertTrue(len(_lowercase							)						,     config.decoder_config.decoder_layers							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=False							):





													'''simple docstring'''

													with torch.no_grad():
																				__a		:		Any									=       MaskFormerModel(config=_lowercase							)
																				model.to(_lowercase							)
																				model.eval()

																				__a		:		Dict									=       model(pixel_values=_lowercase						,     pixel_mask=_lowercase							)
																				__a		:		str									=       model(_lowercase						,     output_hidden_states=_lowercase							)
													# 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(_lowercase						,     _lowercase							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       MaskFormerForInstanceSegmentation(config=_lowercase							)
													model.to(_lowercase							)
													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():
																				__a		:		Optional[int]									=       model(pixel_values=_lowercase						,     pixel_mask=_lowercase							)
																				__a		:		List[Any]									=       model(_lowercase							)

																				comm_check_on_output(_lowercase							)

																				__a		:		Dict									=       model(
																				    pixel_values=_lowercase						,     pixel_mask=_lowercase						,     mask_labels=_lowercase						,     class_labels=_lowercase							)

													comm_check_on_output(_lowercase							)

													self.parent.assertTrue(result.loss is not None							)
													self.parent.assertEqual(result.loss.shape						,     torch.Size([1]							)							)







@require_torch
class SCREAMING_SNAKE_CASE__					(       __snake_case     ,					__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
						_lowerCAmelCase							=					(
						    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       MaskFormerModelTester(self							)
													__a		:		int									=       ConfigTester(self						,     config_class=_lowercase						,     has_text_modality=_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.config_tester.run_common_tests()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskformer_model(_lowercase						,     **_lowercase						,     output_hidden_states=_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowercase							)



						@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""MaskFormer is not a generative model"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""MaskFormer does not use token embeddings"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													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							):





													'''simple docstring'''

													pass



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





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()

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

																				__a		:		Tuple									=       ["""pixel_values"""]
																				self.assertListEqual(arg_names[:1]						,     _lowercase							)



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for model_name in ["facebook/maskformer-swin-small-coco"]:
																				__a		:		str									=       MaskFormerModel.from_pretrained(_lowercase							)
																				self.assertIsNotNone(_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       (self.model_tester.min_size,) * 2
													__a		:		Optional[int]									=       {
													    """pixel_values""": torch.randn((2, 3, *size)						,     device=_lowercase							),
													    """mask_labels""": torch.randn((2, 10, *size)						,     device=_lowercase							),
													    """class_labels""": torch.zeros(2						,     10						,     device=_lowercase							).long(),
													}

													__a		:		Union[str, Any]									=       MaskFormerForInstanceSegmentation(MaskFormerConfig()							).to(_lowercase							)
													__a		:		Optional[Any]									=       model(**_lowercase							)
													self.assertTrue(outputs.loss is not None							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskformer_model(_lowercase						,     **_lowercase						,     output_hidden_states=_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													if not self.model_tester.is_training:
																				return
													# only MaskFormerForInstanceSegmentation has the loss
													__a		:		Union[str, Any]									=       self.all_model_classes[1]
													__a		:		List[Any]									=       self.model_tester.prepare_config_and_inputs()

													__a		:		Tuple									=       model_class(_lowercase							)
													model.to(_lowercase							)
													model.train()

													__a		:		Optional[Any]									=       model(_lowercase						,     mask_labels=_lowercase						,     class_labels=_lowercase							).loss
													loss.backward()





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       self.all_model_classes[1]
													__a		:		Tuple									=       self.model_tester.prepare_config_and_inputs()
													__a		:		Optional[int]									=       True
													__a		:		Dict									=       True

													__a		:		Union[str, Any]									=       model_class(_lowercase							)
													model.to(_lowercase							)
													model.train()

													__a		:		Optional[int]									=       model(_lowercase						,     mask_labels=_lowercase						,     class_labels=_lowercase							)

													__a		:		str									=       outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													__a		:		List[Any]									=       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
													__a		:		Tuple									=       outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													__a		:		Tuple									=       outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=_lowercase							)

													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       __magic_name__    (   ):
							__a		:		Optional[Any]									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
							return image







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



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return (
													    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
													    if is_vision_available()
													    else None
													)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""							).to(_lowercase							)
													__a		:		int									=       self.default_image_processor
													__a		:		List[Any]									=       prepare_img()
													__a		:		Any									=       image_processor(_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)
													__a		:		Dict									=       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(_lowercase						,     (1, 3, 800, 1088)							)

													with torch.no_grad():
																				__a		:		List[Any]									=       model(**_lowercase							)

													__a		:		Dict									=       torch.tensor(
													    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]							).to(_lowercase							)
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)

													__a		:		int									=       torch.tensor(
													    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]							).to(_lowercase							)
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)

													__a		:		List[Any]									=       torch.tensor(
													    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]							).to(_lowercase							)
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       (
													    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
													    .to(_lowercase							)
													    .eval()
													)
													__a		:		int									=       self.default_image_processor
													__a		:		Optional[Any]									=       prepare_img()
													__a		:		Optional[Any]									=       image_processor(_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)
													__a		:		Union[str, 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(_lowercase						,     (1, 3, 800, 1088)							)

													with torch.no_grad():
																				__a		:		Optional[int]									=       model(**_lowercase							)
													# masks_queries_logits
													__a		:		Optional[int]									=       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)						,     )
													__a		:		Dict									=       [
													    [-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],
													]
													__a		:		Dict									=       torch.tensor(_lowercase							).to(_lowercase							)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)
													# class_queries_logits
													__a		:		Union[str, Any]									=       outputs.class_queries_logits
													self.assertEqual(
													    class_queries_logits.shape						,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
													__a		:		List[str]									=       torch.tensor(
													    [
													        [1.6512e00, -5.2572e00, -3.3519e00],
													        [3.6169e-02, -5.9025e00, -2.9313e00],
													        [1.0766e-04, -7.7630e00, -5.1263e00],
													    ]							).to(_lowercase							)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       (
													    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""							)
													    .to(_lowercase							)
													    .eval()
													)
													__a		:		List[str]									=       self.default_image_processor
													__a		:		Tuple									=       prepare_img()
													__a		:		int									=       image_processor(_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)
													__a		:		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(_lowercase						,     (1, 3, 800, 1088)							)

													with torch.no_grad():
																				__a		:		Tuple									=       model(**_lowercase							)
													# masks_queries_logits
													__a		:		Optional[int]									=       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)						,     )
													__a		:		Optional[int]									=       [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
													__a		:		Dict									=       torch.tensor(_lowercase							).to(_lowercase							)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)
													# class_queries_logits
													__a		:		int									=       outputs.class_queries_logits
													self.assertEqual(
													    class_queries_logits.shape						,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
													__a		:		Optional[Any]									=       torch.tensor(
													    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]							).to(_lowercase							)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,     _lowercase						,     atol=_lowercase							)							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[Any]									=       (
													    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
													    .to(_lowercase							)
													    .eval()
													)
													__a		:		Optional[Any]									=       self.default_image_processor

													__a		:		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"""						,     )

													__a		:		Optional[int]									=       inputs["""pixel_values"""].to(_lowercase							)
													__a		:		List[Any]									=       [el.to(_lowercase							) for el in inputs["""mask_labels"""]]
													__a		:		Optional[int]									=       [el.to(_lowercase							) for el in inputs["""class_labels"""]]

													with torch.no_grad():
																				__a		:		Union[str, Any]									=       model(**_lowercase							)

													self.assertTrue(outputs.loss is not None							)






"""simple docstring"""

import os





def       __magic_name__    (   _lowerCamelCase				: Dict					):
							__a		:		List[str]									=       len(grid[0]					)
							__a		:		int									=       len(_lowerCamelCase					)

							__a		:		Tuple									=       0
							__a		:		List[Any]									=       0
							__a		:		List[str]									=       0

							# Check vertically, horizontally, diagonally at the same time (only works
							# for nxn grid)
							for i in range(_lowerCamelCase					):
														for j in range(n_rows - 3					):
																					__a		:		List[Any]									=       grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
																					__a		:		Tuple									=       grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

																					# Left-to-right diagonal (\) product
																					if i < n_columns - 3:
																												__a		:		List[Any]									=       (
																												    grid[i][j]
																												    * grid[i + 1][j + 1]
																												    * grid[i + 2][j + 2]
																												    * grid[i + 3][j + 3]
																												)

																					# Right-to-left diagonal(/) product
																					if i > 2:
																												__a		:		List[Any]									=       (
																												    grid[i][j]
																												    * grid[i - 1][j + 1]
																												    * grid[i - 2][j + 2]
																												    * grid[i - 3][j + 3]
																												)

																					__a		:		str									=       max(
																					    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					if max_product > largest:
																												__a		:		Optional[Any]									=       max_product

							return largest





def       __magic_name__    (   ):
							__a		:		Tuple									=       []
							with open(os.path.dirname(_lowerCamelCase					) + """/grid.txt"""					) as file:
														for line in file:
																					grid.append(line.strip("""\n"""					).split(""" """					)					)

							__a		:		Tuple									=       [[int(_lowerCamelCase					) for i in grid[j]] for j in range(len(_lowerCamelCase					)					)]

							return largest_product(_lowerCamelCase					)


if __name__ == "__main__":
	print(solution())

