






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   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=2    ,		_UpperCamelCase=True    ,		_UpperCamelCase=False    ,		_UpperCamelCase=1_0    ,		_UpperCamelCase=3    ,		_UpperCamelCase=3_2 * 4    ,		_UpperCamelCase=3_2 * 6    ,		_UpperCamelCase=4    ,		_UpperCamelCase=3_2    ,		)     ->							Optional[int]:
							UpperCAmelCase_   :				List[str]						=						parent
							UpperCAmelCase_   :				List[str]						=						batch_size
							UpperCAmelCase_   :				Optional[int]						=						is_training
							UpperCAmelCase_   :				Optional[int]						=						use_auxiliary_loss
							UpperCAmelCase_   :				List[str]						=						num_queries
							UpperCAmelCase_   :				Union[str, Any]						=						num_channels
							UpperCAmelCase_   :				int						=						min_size
							UpperCAmelCase_   :				List[Any]						=						max_size
							UpperCAmelCase_   :				Optional[Any]						=						num_labels
							UpperCAmelCase_   :				List[Any]						=						mask_feature_size



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
							    _a       )

							UpperCAmelCase_   :				str						=						torch.ones([self.batch_size, self.min_size, self.max_size]    ,		device=_a       )

							UpperCAmelCase_   :				Any						=						(
							    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,		device=_a       ) > 0.5
							).float()
							UpperCAmelCase_   :				Optional[int]						=						(torch.rand((self.batch_size, self.num_labels)    ,		device=_a       ) > 0.5).long()

							UpperCAmelCase_   :				int						=						self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return MaskFormerConfig.from_backbone_and_decoder_configs(
							    backbone_config=SwinConfig(
							        depths=[1, 1, 1, 1]    ,		)    ,		decoder_config=DetrConfig(
							        decoder_ffn_dim=1_2_8    ,		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  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						self.prepare_config_and_inputs()
							UpperCAmelCase_   :				Optional[int]						=						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
							return config, inputs_dict



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				Dict						=						output.encoder_hidden_states
							UpperCAmelCase_   :				str						=						output.pixel_decoder_hidden_states
							UpperCAmelCase_   :				Optional[Any]						=						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_config.decoder_layers       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=False       )     ->							List[str]:


							with torch.no_grad():
												UpperCAmelCase_   :				Dict						=						MaskFormerModel(config=_a       )
												model.to(_a       )
												model.eval()

												UpperCAmelCase_   :				Union[str, Any]						=						model(pixel_values=_a    ,		pixel_mask=_a       )
												UpperCAmelCase_   :				int						=						model(_a    ,		output_hidden_states=_a       )
							# 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(_a    ,		_a       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[int]						=						MaskFormerForInstanceSegmentation(config=_a       )
							model.to(_a       )
							model.eval()

							def comm_check_on_output(_UpperCamelCase       ):
												# 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_   :				Optional[Any]						=						model(pixel_values=_a    ,		pixel_mask=_a       )
												UpperCAmelCase_   :				str						=						model(_a       )

												comm_check_on_output(_a       )

												UpperCAmelCase_   :				List[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				(UpperCamelCase_			,  UpperCamelCase_			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
		_snake_case   :   Union[str, Any]							=  (
		    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
		    if is_torch_available()
		    else {}
		)

		_snake_case   :   int							=  False
		_snake_case   :   Any							=  False
		_snake_case   :   Any							=  False
		_snake_case   :   Any							=  False



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						MaskFormerModelTester(self       )
							UpperCAmelCase_   :				Tuple						=						ConfigTester(self    ,		config_class=_a    ,		has_text_modality=_a       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							self.config_tester.run_common_tests()



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Tuple						=						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(_a    ,		**_a    ,		output_hidden_states=_a       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				str						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_a       )



		@unittest.skip(reason='MaskFormer does not use inputs_embeds'       )
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							pass



		@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'       )
		def  __UpperCAmelCase      (							self       )     ->							Any:
							pass



		@unittest.skip(reason='MaskFormer is not a generative model'       )
		def  __UpperCAmelCase      (							self       )     ->							int:
							pass



		@unittest.skip(reason='MaskFormer does not use token embeddings'       )
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							pass



		@require_torch_multi_gpu
		@unittest.skip(
		    reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'       )
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							pass



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def  __UpperCAmelCase      (							self       )     ->							Any:
							pass



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Optional[int]						=						self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCAmelCase_   :				Optional[Any]						=						["""pixel_values"""]
												self.assertListEqual(arg_names[:1]    ,		_a       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							str:


							for model_name in ["facebook/maskformer-swin-small-coco"]:
												UpperCAmelCase_   :				Optional[int]						=						MaskFormerModel.from_pretrained(_a       )
												self.assertIsNotNone(_a       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				List[str]						=						(self.model_tester.min_size,) * 2
							UpperCAmelCase_   :				List[str]						=						{
							    """pixel_values""": torch.randn((2, 3, *size)    ,		device=_a       ),
							    """mask_labels""": torch.randn((2, 1_0, *size)    ,		device=_a       ),
							    """class_labels""": torch.zeros(2    ,		1_0    ,		device=_a       ).long(),
							}

							UpperCAmelCase_   :				Dict						=						MaskFormerForInstanceSegmentation(MaskFormerConfig()       ).to(_a       )
							UpperCAmelCase_   :				Optional[int]						=						model(**_a       )
							self.assertTrue(outputs.loss is not None       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				Tuple						=						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(_a    ,		**_a    ,		output_hidden_states=_a       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				List[str]						=						self.model_tester.prepare_config_and_inputs_for_common()

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



		def  __UpperCAmelCase      (							self       )     ->							List[str]:


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

							UpperCAmelCase_   :				Optional[Any]						=						model_class(_a       )
							model.to(_a       )
							model.train()

							UpperCAmelCase_   :				List[str]						=						model(_a    ,		mask_labels=_a    ,		class_labels=_a       ).loss
							loss.backward()



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				List[Any]						=						self.all_model_classes[1]
							UpperCAmelCase_   :				Optional[Any]						=						self.model_tester.prepare_config_and_inputs()
							UpperCAmelCase_   :				Optional[int]						=						True
							UpperCAmelCase_   :				Any						=						True

							UpperCAmelCase_   :				List[str]						=						model_class(_a       )
							model.to(_a       )
							model.train()

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

							UpperCAmelCase_   :				Dict						=						outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

							UpperCAmelCase_   :				str						=						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_   :				List[Any]						=						outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							UpperCAmelCase_   :				Dict						=						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       )





__UpperCAmelCase     							=						1E-4




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






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







		'''simple docstring'''



		@cached_property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return (
							    MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'       )
							    if is_vision_available()
							    else None
							)



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Union[str, Any]						=						MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'       ).to(_a       )
							UpperCAmelCase_   :				int						=						self.default_image_processor
							UpperCAmelCase_   :				List[str]						=						prepare_img()
							UpperCAmelCase_   :				Union[str, Any]						=						image_processor(_a    ,		return_tensors='pt'       ).to(_a       )
							UpperCAmelCase_   :				Dict						=						inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
							# check size
							self.assertEqual(_a    ,		(1, 3, 8_0_0, 1_0_8_8)       )

							with torch.no_grad():
												UpperCAmelCase_   :				str						=						model(**_a       )

							UpperCAmelCase_   :				Any						=						torch.tensor(
							    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]       ).to(_a       )
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,		_a    ,		atol=_a       )       )

							UpperCAmelCase_   :				Any						=						torch.tensor(
							    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]       ).to(_a       )
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,		_a    ,		atol=_a       )       )

							UpperCAmelCase_   :				Union[str, Any]						=						torch.tensor(
							    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]       ).to(_a       )
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,		_a    ,		atol=_a       )       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Tuple						=						(
							    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'       )
							    .to(_a       )
							    .eval()
							)
							UpperCAmelCase_   :				List[str]						=						self.default_image_processor
							UpperCAmelCase_   :				Optional[int]						=						prepare_img()
							UpperCAmelCase_   :				List[Any]						=						image_processor(_a    ,		return_tensors='pt'       ).to(_a       )
							UpperCAmelCase_   :				Tuple						=						inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
							# check size
							self.assertEqual(_a    ,		(1, 3, 8_0_0, 1_0_8_8)       )

							with torch.no_grad():
												UpperCAmelCase_   :				str						=						model(**_a       )
							# masks_queries_logits
							UpperCAmelCase_   :				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						=						[
							    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
							    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
							    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
							]
							UpperCAmelCase_   :				Optional[int]						=						torch.tensor(_a       ).to(_a       )
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,		_a    ,		atol=_a       )       )
							# class_queries_logits
							UpperCAmelCase_   :				Tuple						=						outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape    ,		(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
							UpperCAmelCase_   :				List[str]						=						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(_a       )
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,		_a    ,		atol=_a       )       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				List[Any]						=						(
							    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'       )
							    .to(_a       )
							    .eval()
							)
							UpperCAmelCase_   :				Any						=						self.default_image_processor
							UpperCAmelCase_   :				Tuple						=						prepare_img()
							UpperCAmelCase_   :				int						=						image_processor(_a    ,		return_tensors='pt'       ).to(_a       )
							UpperCAmelCase_   :				Any						=						inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
							# check size
							self.assertEqual(_a    ,		(1, 3, 8_0_0, 1_0_8_8)       )

							with torch.no_grad():
												UpperCAmelCase_   :				str						=						model(**_a       )
							# masks_queries_logits
							UpperCAmelCase_   :				Union[str, 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						=						[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
							UpperCAmelCase_   :				Tuple						=						torch.tensor(_a       ).to(_a       )
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,		_a    ,		atol=_a       )       )
							# class_queries_logits
							UpperCAmelCase_   :				Optional[int]						=						outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape    ,		(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
							UpperCAmelCase_   :				Dict						=						torch.tensor(
							    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]       ).to(_a       )
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,		_a    ,		atol=_a       )       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						(
							    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'       )
							    .to(_a       )
							    .eval()
							)
							UpperCAmelCase_   :				List[Any]						=						self.default_image_processor

							UpperCAmelCase_   :				Optional[Any]						=						image_processor(
							    [np.zeros((3, 8_0_0, 1_3_3_3)       ), np.zeros((3, 8_0_0, 1_3_3_3)       )]    ,		segmentation_maps=[np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       ), np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       )]    ,		return_tensors='pt'    ,		)

							UpperCAmelCase_   :				Union[str, Any]						=						inputs["""pixel_values"""].to(_a       )
							UpperCAmelCase_   :				Tuple						=						[el.to(_a       ) for el in inputs["""mask_labels"""]]
							UpperCAmelCase_   :				Tuple						=						[el.to(_a       ) for el in inputs["""class_labels"""]]

							with torch.no_grad():
												UpperCAmelCase_   :				Optional[int]						=						model(**_a       )

							self.assertTrue(outputs.loss is not None       )














import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__UpperCAmelCase     							=						'Usage of script: script_name <size_of_canvas:int>'

__UpperCAmelCase     							=						[0] * 100 + [1] * 10
random.shuffle(choice)




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						[[False for i in range(__snake_case  )] for j in range(__snake_case  )]
					return canvas




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					for i, row in enumerate(__snake_case  ):
										for j, _ in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Tuple						=						bool(random.getrandbits(1  )  )




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						np.array(__snake_case  )
					UpperCAmelCase_   :				Any						=						np.array(create_canvas(current_canvas.shape[0]  )  )
					for r, row in enumerate(__snake_case  ):
										for c, pt in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						__judge_point(
															    __snake_case					,	current_canvas[r - 1 : r + 2, c - 1 : c + 2]  )

					UpperCAmelCase_   :				List[Any]						=						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					UpperCAmelCase_   :				list[list[bool]]						=						current_canvas.tolist()
					return return_canvas




def 					lowercase__				(					__snake_case   :							bool					,	__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						0
					UpperCAmelCase_   :				List[Any]						=						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					UpperCAmelCase_   :				List[Any]						=						pt
					if pt:
										if alive < 2:
															UpperCAmelCase_   :				str						=						False
										elif alive == 2 or alive == 3:
															UpperCAmelCase_   :				int						=						True
										elif alive > 3:
															UpperCAmelCase_   :				List[Any]						=						False
					else:
										if alive == 3:
															UpperCAmelCase_   :				int						=						True

					return state


if __name__ == "__main__":
		if len(sys.argv) != 2:
				raise Exception(usage_doc)

		__UpperCAmelCase     							=						int(sys.argv[1])
		# main working structure of this module.
		__UpperCAmelCase     							=						create_canvas(canvas_size)
		seed(c)
		__UpperCAmelCase							,       __UpperCAmelCase     							=						plt.subplots()
		fig.show()
		__UpperCAmelCase     							=						ListedColormap(['w', 'k'])
		try:
				while True:
						__UpperCAmelCase     							=						run(c)
						ax.matshow(c, cmap=cmap)
						fig.canvas.draw()
						ax.cla()
		except KeyboardInterrupt:
				# do nothing.
				pass






