


"""simple docstring"""



import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor







class    __a						( __lowerCAmelCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.config_class(**self.inputs_dict   )
					self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,	'''hidden_sizes'''   )   )
					self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,	'''num_attention_heads'''   )   )
					self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,	'''num_encoder_blocks'''   )   )







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase=13      ,	UpperCAmelCase=64      ,	UpperCAmelCase=3      ,	UpperCAmelCase=4      ,	UpperCAmelCase=[2, 2, 2, 2]      ,	UpperCAmelCase=[8, 4, 2, 1]      ,	UpperCAmelCase=[16, 32, 64, 128]      ,	UpperCAmelCase=[1, 4, 8, 16]      ,	UpperCAmelCase=[1, 2, 4, 8]      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase="gelu"      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=3      ,	UpperCAmelCase=None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			parent
					lowerCAmelCase_      							=			batch_size
					lowerCAmelCase_      							=			image_size
					lowerCAmelCase_      							=			num_channels
					lowerCAmelCase_      							=			num_encoder_blocks
					lowerCAmelCase_      							=			sr_ratios
					lowerCAmelCase_      							=			depths
					lowerCAmelCase_      							=			hidden_sizes
					lowerCAmelCase_      							=			downsampling_rates
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			is_training
					lowerCAmelCase_      							=			use_labels
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_probs_dropout_prob
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			num_labels
					lowerCAmelCase_      							=			scope







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					lowerCAmelCase_      							=			None
					if self.use_labels:
						lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.image_size, self.image_size]      ,	self.num_labels   )

					lowerCAmelCase_      							=			self.get_config()
					return config, pixel_values, labels







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return SegformerConfig(
					    image_size=self.image_size      ,	num_channels=self.num_channels      ,	num_encoder_blocks=self.num_encoder_blocks      ,	depths=self.depths      ,	hidden_sizes=self.hidden_sizes      ,	num_attention_heads=self.num_attention_heads      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	initializer_range=self.initializer_range      ,	)







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SegformerModel(config=__SCREAMING_SNAKE_CASE   )
					model.to(__SCREAMING_SNAKE_CASE   )
					model.eval()
					lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			lowerCAmelCase_      							=			self.image_size // (self.downsampling_rates[-1] * 2)
					self.parent.assertEqual(
					    result.last_hidden_state.shape      ,	(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.num_labels
					lowerCAmelCase_      							=			SegformerForSemanticSegmentation(__SCREAMING_SNAKE_CASE   )
					model.to(__SCREAMING_SNAKE_CASE   )
					model.eval()
					lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE   )
					self.parent.assertEqual(
					    result.logits.shape      ,	(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
					lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE      ,	labels=__SCREAMING_SNAKE_CASE   )
					self.parent.assertEqual(
					    result.logits.shape      ,	(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
					self.parent.assertGreater(result.loss      ,	0.0   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			1
					lowerCAmelCase_      							=			SegformerForSemanticSegmentation(config=__SCREAMING_SNAKE_CASE   )
					model.to(__SCREAMING_SNAKE_CASE   )
					model.eval()
					lowerCAmelCase_      							=			torch.randint(0      ,	1      ,	(self.batch_size, self.image_size, self.image_size)   ).to(__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE      ,	labels=__SCREAMING_SNAKE_CASE   )
					self.parent.assertGreater(result.loss      ,	0.0   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.prepare_config_and_inputs()
					lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			config_and_inputs
					lowerCAmelCase_      							=			{'''pixel_values''': pixel_values}
					return config, inputs_dict







@require_torch
class    __a						( __lowerCAmelCase					,			__lowerCAmelCase					,			unittest.TestCase							):
				lowerCamelCase     :       List[str]			       =(
				    (
				        SegformerModel,
				        SegformerForSemanticSegmentation,
				        SegformerForImageClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCamelCase     :       Union[str, Any]			       =(
				    {
				        '''feature-extraction''': SegformerModel,
				        '''image-classification''': SegformerForImageClassification,
				        '''image-segmentation''': SegformerForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				lowerCamelCase     :       Tuple			       =True
				lowerCamelCase     :       Union[str, Any]			       =False
				lowerCamelCase     :       Any			       =False
				lowerCamelCase     :       int			       =False







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SegformerModelTester(self   )
					lowerCAmelCase_      							=			SegformerConfigTester(self      ,	config_class=__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					self.config_tester.run_common_tests()







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_binary_image_segmentation(*__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_segmentation(*__SCREAMING_SNAKE_CASE   )







				@unittest.skip('''SegFormer does not use inputs_embeds'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







				@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()

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

						lowerCAmelCase_      							=			['''pixel_values''']
						self.assertListEqual(arg_names[:1]      ,	__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase_      							=			True

					for model_class in self.all_model_classes:
						lowerCAmelCase_      							=			True
						lowerCAmelCase_      							=			False
						lowerCAmelCase_      							=			True
						lowerCAmelCase_      							=			model_class(__SCREAMING_SNAKE_CASE   )
						model.to(__SCREAMING_SNAKE_CASE   )
						model.eval()
						with torch.no_grad():
							lowerCAmelCase_      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )   )
						lowerCAmelCase_      							=			outputs.attentions

						lowerCAmelCase_      							=			sum(self.model_tester.depths   )
						self.assertEqual(len(__SCREAMING_SNAKE_CASE   )      ,	__SCREAMING_SNAKE_CASE   )

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						lowerCAmelCase_      							=			True
						lowerCAmelCase_      							=			model_class(__SCREAMING_SNAKE_CASE   )
						model.to(__SCREAMING_SNAKE_CASE   )
						model.eval()
						with torch.no_grad():
							lowerCAmelCase_      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )   )
						lowerCAmelCase_      							=			outputs.attentions

						self.assertEqual(len(__SCREAMING_SNAKE_CASE   )      ,	__SCREAMING_SNAKE_CASE   )

						# verify the first attentions (first block, first layer)
						lowerCAmelCase_      							=			(self.model_tester.image_size // 4) ** 2
						lowerCAmelCase_      							=			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(attentions[0].shape[-3:]   )      ,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,	)

						# verify the last attentions (last block, last layer)
						lowerCAmelCase_      							=			(self.model_tester.image_size // 32) ** 2
						lowerCAmelCase_      							=			(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
						self.assertListEqual(
						    list(attentions[-1].shape[-3:]   )      ,	[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]      ,	)
						lowerCAmelCase_      							=			len(__SCREAMING_SNAKE_CASE   )

						# Check attention is always last and order is fine
						lowerCAmelCase_      							=			True
						lowerCAmelCase_      							=			True
						lowerCAmelCase_      							=			model_class(__SCREAMING_SNAKE_CASE   )
						model.to(__SCREAMING_SNAKE_CASE   )
						model.eval()
						with torch.no_grad():
							lowerCAmelCase_      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )   )

						self.assertEqual(out_len + 1      ,	len(__SCREAMING_SNAKE_CASE   )   )

						lowerCAmelCase_      							=			outputs.attentions

						self.assertEqual(len(__SCREAMING_SNAKE_CASE   )      ,	__SCREAMING_SNAKE_CASE   )
						# verify the first attentions (first block, first layer)
						lowerCAmelCase_      							=			(self.model_tester.image_size // 4) ** 2
						lowerCAmelCase_      							=			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]   )      ,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					def check_hidden_states_output(UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):
						lowerCAmelCase_      							=			model_class(__SCREAMING_SNAKE_CASE   )
						model.to(__SCREAMING_SNAKE_CASE   )
						model.eval()

						with torch.no_grad():
							lowerCAmelCase_      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )   )

						lowerCAmelCase_      							=			outputs.hidden_states

						lowerCAmelCase_      							=			self.model_tester.num_encoder_blocks
						self.assertEqual(len(__SCREAMING_SNAKE_CASE   )      ,	__SCREAMING_SNAKE_CASE   )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]   )      ,	[
						        self.model_tester.hidden_sizes[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]      ,	)

					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowerCAmelCase_      							=			True
						check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						lowerCAmelCase_      							=			True

						check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if not self.model_tester.is_training:
						return

					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase_      							=			True

					for model_class in self.all_model_classes:
						if model_class in get_values(__SCREAMING_SNAKE_CASE   ):
							continue

						lowerCAmelCase_      							=			model_class(__SCREAMING_SNAKE_CASE   )
						model.to(__SCREAMING_SNAKE_CASE   )
						model.train()
						lowerCAmelCase_      							=			self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE      ,	return_labels=__SCREAMING_SNAKE_CASE   )
						lowerCAmelCase_      							=			model(**__SCREAMING_SNAKE_CASE   ).loss
						loss.backward()







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

					'''simple docstring'''






					pass







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCAmelCase_      							=			SegformerModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
						self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )




def      UpperCAmelCase     (       )						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
	return image







@require_torch
class    __a						( unittest.TestCase							):







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SegformerImageProcessor(
					    image_scale=(512, 512)      ,	keep_ratio=__SCREAMING_SNAKE_CASE      ,	align=__SCREAMING_SNAKE_CASE      ,	do_random_crop=__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''   ).to(
					    __SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			prepare_img()
					lowerCAmelCase_      							=			image_processor(images=__SCREAMING_SNAKE_CASE      ,	return_tensors='''pt'''   )
					lowerCAmelCase_      							=			encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE   )

					with torch.no_grad():
						lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			torch.Size((1, model.config.num_labels, 128, 128)   )
					self.assertEqual(outputs.logits.shape      ,	__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			torch.tensor(
					    [
					        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
					        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
					        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
					    ]   ).to(__SCREAMING_SNAKE_CASE   )
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,	__SCREAMING_SNAKE_CASE      ,	atol=1E-4   )   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SegformerImageProcessor(
					    image_scale=(512, 512)      ,	keep_ratio=__SCREAMING_SNAKE_CASE      ,	align=__SCREAMING_SNAKE_CASE      ,	do_random_crop=__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			SegformerForSemanticSegmentation.from_pretrained(
					    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''   ).to(__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			prepare_img()
					lowerCAmelCase_      							=			image_processor(images=__SCREAMING_SNAKE_CASE      ,	return_tensors='''pt'''   )
					lowerCAmelCase_      							=			encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE   )

					with torch.no_grad():
						lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			torch.Size((1, model.config.num_labels, 128, 128)   )
					self.assertEqual(outputs.logits.shape      ,	__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			torch.tensor(
					    [
					        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
					        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
					        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
					    ]   ).to(__SCREAMING_SNAKE_CASE   )
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,	__SCREAMING_SNAKE_CASE      ,	atol=1E-1   )   )







				@slow
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SegformerImageProcessor(
					    image_scale=(512, 512)      ,	keep_ratio=__SCREAMING_SNAKE_CASE      ,	align=__SCREAMING_SNAKE_CASE      ,	do_random_crop=__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''   ).to(
					    __SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			prepare_img()
					lowerCAmelCase_      							=			image_processor(images=__SCREAMING_SNAKE_CASE      ,	return_tensors='''pt'''   )
					lowerCAmelCase_      							=			encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE   )

					with torch.no_grad():
						lowerCAmelCase_      							=			model(__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			outputs.logits.detach().cpu()

					lowerCAmelCase_      							=			image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE      ,	target_sizes=[(500, 300)]   )
					lowerCAmelCase_      							=			torch.Size((500, 300)   )
					self.assertEqual(segmentation[0].shape      ,	__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			torch.Size((128, 128)   )
					self.assertEqual(segmentation[0].shape      ,	__SCREAMING_SNAKE_CASE   )





'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     if divisor % 5 == 0 or divisor % 2 == 0:
          return 0
     __snake_case            =	1
     __snake_case            =	1
     while repunit:
          __snake_case            =	(10 * repunit + 1) % divisor
          repunit_index += 1
     return repunit_index







def       _UpperCamelCase			(_lowerCamelCase				:  int = 1_00_00_00						)-> int:




     '''simple docstring'''





     __snake_case            =	limit - 1
     if divisor % 2 == 0:
          divisor += 1
     while least_divisible_repunit(_lowerCamelCase						) <= limit:
          divisor += 2
     return divisor


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






