




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_MAPPING,
					    BeitForImageClassification,
					    BeitForMaskedImageModeling,
					    BeitForSemanticSegmentation,
					    BeitModel,
					)
					from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					import PIL
					from PIL import Image

					from transformers import BeitImageProcessor







class        __lowerCAmelCase					:

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__=100     ,		lowerCamelCase__=13     ,		lowerCamelCase__=30     ,		lowerCamelCase__=2     ,		lowerCamelCase__=3     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=32     ,		lowerCamelCase__=4     ,		lowerCamelCase__=4     ,		lowerCamelCase__=37     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=10     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=3     ,		lowerCamelCase__=None     ,		lowerCamelCase__=[0, 1, 2, 3]     ,		)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       parent
							__lowerCamelCase									=       100
							__lowerCamelCase									=       batch_size
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       is_training
							__lowerCamelCase									=       use_labels
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       type_sequence_label_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       scope
							__lowerCamelCase									=       out_indices
							__lowerCamelCase									=       num_labels

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							__lowerCamelCase									=       (image_size // patch_size) ** 2
							__lowerCamelCase									=       num_patches + 1


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

							__lowerCamelCase									=       self.get_config()

							return config, pixel_values, labels, pixel_labels


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							return BeitConfig(
							    vocab_size=self.vocab_size     ,		image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		is_decoder=lowerCamelCase__     ,		initializer_range=self.initializer_range     ,		out_indices=self.out_indices     ,		)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       BeitModel(config=lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()
							__lowerCamelCase									=       model(lowerCamelCase__				)
							self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       BeitForMaskedImageModeling(config=lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()
							__lowerCamelCase									=       model(lowerCamelCase__				)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length - 1, self.vocab_size)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.type_sequence_label_size
							__lowerCamelCase									=       BeitForImageClassification(lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()
							__lowerCamelCase									=       model(lowerCamelCase__     ,		labels=lowerCamelCase__				)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)				)

							# test greyscale images
							__lowerCamelCase									=       1
							__lowerCamelCase									=       BeitForImageClassification(lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()

							__lowerCamelCase									=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
							__lowerCamelCase									=       model(lowerCamelCase__     ,		labels=lowerCamelCase__				)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       self.num_labels
							__lowerCamelCase									=       BeitForSemanticSegmentation(lowerCamelCase__				)
							model.to(lowerCamelCase__				)
							model.eval()
							__lowerCamelCase									=       model(lowerCamelCase__				)
							self.parent.assertEqual(
							    result.logits.shape     ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
							__lowerCamelCase									=       model(lowerCamelCase__     ,		labels=lowerCamelCase__				)
							self.parent.assertEqual(
							    result.logits.shape     ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)






			def  lowercase_       ( self				)       -> Tuple:



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







@require_torch
class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			(
			    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			snake_case_  						=			(
			    {
			        '''feature-extraction''': BeitModel,
			        '''image-classification''': BeitForImageClassification,
			        '''image-segmentation''': BeitForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			snake_case_  						=			False
			snake_case_  						=			False
			snake_case_  						=			False


			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       BeitModelTester(self				)
							__lowerCamelCase									=       ConfigTester(self     ,		config_class=lowerCamelCase__     ,		has_text_modality=lowerCamelCase__     ,		hidden_size=37				)


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			@unittest.skip(reason='BEiT does not use inputs_embeds'				)
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase									=       model_class(lowerCamelCase__				)
											self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)				)
											__lowerCamelCase									=       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase__     ,		nn.Linear				)				)


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase									=       model_class(lowerCamelCase__				)
											__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]     ,		lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__				)


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Any:



							'''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:
											# we don't test BeitForMaskedImageModeling
											if model_class in [*get_values(lowerCamelCase__				), BeitForMaskedImageModeling]:
															continue

											__lowerCamelCase									=       model_class(lowerCamelCase__				)
											model.to(lowerCamelCase__				)
											model.train()
											__lowerCamelCase									=       self._prepare_for_class(lowerCamelCase__     ,		lowerCamelCase__     ,		return_labels=lowerCamelCase__				)
											__lowerCamelCase									=       model(**lowerCamelCase__				).loss
											loss.backward()


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
											return

							__lowerCamelCase									=       False
							__lowerCamelCase									=       True

							for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if (
											    model_class in [*get_values(lowerCamelCase__				), BeitForMaskedImageModeling]
											    or not model_class.supports_gradient_checkpointing
											):
															continue

											__lowerCamelCase									=       model_class(lowerCamelCase__				)
											model.gradient_checkpointing_enable()
											model.to(lowerCamelCase__				)
											model.train()
											__lowerCamelCase									=       self._prepare_for_class(lowerCamelCase__     ,		lowerCamelCase__     ,		return_labels=lowerCamelCase__				)
											__lowerCamelCase									=       model(**lowerCamelCase__				).loss
											loss.backward()


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs_for_common()

							__lowerCamelCase									=       _config_zero_init(lowerCamelCase__				)
							for model_class in self.all_model_classes:
											__lowerCamelCase									=       model_class(config=lowerCamelCase__				)
											for name, param in model.named_parameters():
															# we skip lambda parameters as these require special initial values
															# determined by config.layer_scale_init_value
															if "lambda" in name:
																			continue
															if param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1e9).round() / 1e9).item()     ,		[0.0, 1.0]     ,		msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     ,		)






			@slow
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase									=       BeitModel.from_pretrained(lowerCamelCase__				)
											self.assertIsNotNone(lowerCamelCase__				)







def      lowerCamelCase_						(			)	->			Optional[Any]:





				"""simple docstring"""


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







@require_torch
@require_vision
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			@cached_property
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'				) if is_vision_available() else None


			@slow
			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'				).to(lowerCamelCase__				)

							__lowerCamelCase									=       self.default_image_processor
							__lowerCamelCase									=       prepare_img()
							__lowerCamelCase									=       image_processor(images=lowerCamelCase__     ,		return_tensors='pt'				).pixel_values.to(lowerCamelCase__				)

							# prepare bool_masked_pos
							__lowerCamelCase									=       torch.ones((1, 196)     ,		dtype=torch.bool				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase									=       model(pixel_values=lowerCamelCase__     ,		bool_masked_pos=lowerCamelCase__				)
							__lowerCamelCase									=       outputs.logits

							# verify the logits
							__lowerCamelCase									=       torch.Size((1, 196, 8_192)				)
							self.assertEqual(logits.shape     ,		lowerCamelCase__				)

							__lowerCamelCase									=       torch.tensor(
							    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]				).to(lowerCamelCase__				)

							self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,		lowerCamelCase__     ,		atol=1e-2				)				)


			@slow
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'				).to(lowerCamelCase__				)

							__lowerCamelCase									=       self.default_image_processor
							__lowerCamelCase									=       prepare_img()
							__lowerCamelCase									=       image_processor(images=lowerCamelCase__     ,		return_tensors='pt'				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase									=       model(**lowerCamelCase__				)
							__lowerCamelCase									=       outputs.logits

							# verify the logits
							__lowerCamelCase									=       torch.Size((1, 1_000)				)
							self.assertEqual(logits.shape     ,		lowerCamelCase__				)

							__lowerCamelCase									=       torch.tensor([-1.23_85, -1.09_87, -1.01_08]				).to(lowerCamelCase__				)

							self.assertTrue(torch.allclose(logits[0, :3]     ,		lowerCamelCase__     ,		atol=1e-4				)				)

							__lowerCamelCase									=       281
							self.assertEqual(logits.argmax(-1				).item()     ,		lowerCamelCase__				)


			@slow
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'				).to(
							    lowerCamelCase__				)

							__lowerCamelCase									=       self.default_image_processor
							__lowerCamelCase									=       prepare_img()
							__lowerCamelCase									=       image_processor(images=lowerCamelCase__     ,		return_tensors='pt'				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase									=       model(**lowerCamelCase__				)
							__lowerCamelCase									=       outputs.logits

							# verify the logits
							__lowerCamelCase									=       torch.Size((1, 21_841)				)
							self.assertEqual(logits.shape     ,		lowerCamelCase__				)

							__lowerCamelCase									=       torch.tensor([1.68_81, -0.27_87, 0.59_01]				).to(lowerCamelCase__				)

							self.assertTrue(torch.allclose(logits[0, :3]     ,		lowerCamelCase__     ,		atol=1e-4				)				)

							__lowerCamelCase									=       2_396
							self.assertEqual(logits.argmax(-1				).item()     ,		lowerCamelCase__				)


			@slow
			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'				)
							__lowerCamelCase									=       model.to(lowerCamelCase__				)

							__lowerCamelCase									=       BeitImageProcessor(do_resize=lowerCamelCase__     ,		size=640     ,		do_center_crop=lowerCamelCase__				)

							__lowerCamelCase									=       load_dataset('hf-internal-testing/fixtures_ade20k'     ,		split='test'				)
							__lowerCamelCase									=       Image.open(ds[0]['file']				)
							__lowerCamelCase									=       image_processor(images=lowerCamelCase__     ,		return_tensors='pt'				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase									=       model(**lowerCamelCase__				)
							__lowerCamelCase									=       outputs.logits

							# verify the logits
							__lowerCamelCase									=       torch.Size((1, 150, 160, 160)				)
							self.assertEqual(logits.shape     ,		lowerCamelCase__				)

							__lowerCamelCase									=       version.parse(PIL.__version__				) < version.parse('9.0.0'				)

							if is_pillow_less_than_a:
											__lowerCamelCase									=       torch.tensor(
											    [
											        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
											        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
											        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
											    ]     ,		device=lowerCamelCase__     ,		)
							else:
											__lowerCamelCase									=       torch.tensor(
											    [
											        [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
											        [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
											        [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
											    ]     ,		device=lowerCamelCase__     ,		)

							self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,		lowerCamelCase__     ,		atol=1e-4				)				)






			@slow
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'				)
							__lowerCamelCase									=       model.to(lowerCamelCase__				)

							__lowerCamelCase									=       BeitImageProcessor(do_resize=lowerCamelCase__     ,		size=640     ,		do_center_crop=lowerCamelCase__				)

							__lowerCamelCase									=       load_dataset('hf-internal-testing/fixtures_ade20k'     ,		split='test'				)
							__lowerCamelCase									=       Image.open(ds[0]['file']				)
							__lowerCamelCase									=       image_processor(images=lowerCamelCase__     ,		return_tensors='pt'				).to(lowerCamelCase__				)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase									=       model(**lowerCamelCase__				)

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

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

							__lowerCamelCase									=       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__				)
							__lowerCamelCase									=       torch.Size((160, 160)				)
							self.assertEqual(segmentation[0].shape     ,		lowerCamelCase__				)








def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: int )	->			list:





				"""simple docstring"""


				__lowerCamelCase									=       word.split()

				def justify(UpperCamelCase__							: list					,	UpperCamelCase__							: int					,	UpperCamelCase__							: int ) -> str:
								__lowerCamelCase									=       max_width - width
								__lowerCamelCase									=       len(UpperCamelCase__ )
								if len(UpperCamelCase__ ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowerCamelCase									=       words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowerCamelCase									=       spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowerCamelCase									=       (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(UpperCamelCase__ ):
																num_spaces_between_words_list[i] += 1
												__lowerCamelCase									=       []
												for i in range(UpperCamelCase__ ):
																# add the word
																aligned_words_list.append(line[i] )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1] )
												# join the aligned words list to form a justified line
												return "".join(UpperCamelCase__ )

				__lowerCamelCase									=       []
				__lowerCamelCase									=       []
				__lowerCamelCase									=       0
				for word in words:
								if width + len(UpperCamelCase__ ) + len(UpperCamelCase__ ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(UpperCamelCase__ )
												width += len(UpperCamelCase__ )
								else:
												# justify the line and add it to result
												answer.append(justify(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ ) )
												# reset new line and new width
												__lowerCamelCase			,		__lowerCamelCase									=       [word], len(UpperCamelCase__ )
				__lowerCamelCase									=       max_width - width - len(UpperCamelCase__ )
				answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
				return answer


if __name__ == "__main__":
					from doctest import testmod

					testmod()


