


'''simple docstring'''



import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
	from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor






class  UpperCamelCase__:




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Tuple=13   ,					lowerCAmelCase    :				Tuple=32   ,					lowerCAmelCase    :				int=2   ,					lowerCAmelCase    :				Union[str, Any]=3   ,					lowerCAmelCase    :				Optional[int]=16   ,					lowerCAmelCase    :				str=[1, 2, 1]   ,					lowerCAmelCase    :				int=[2, 2, 4]   ,					lowerCAmelCase    :				List[str]=2   ,					lowerCAmelCase    :				Dict=2.0   ,					lowerCAmelCase    :				List[str]=True   ,					lowerCAmelCase    :				List[Any]=0.0   ,					lowerCAmelCase    :				List[str]=0.0   ,					lowerCAmelCase    :				List[str]=0.1   ,					lowerCAmelCase    :				Optional[int]="gelu"   ,					lowerCAmelCase    :				Tuple=False   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				str=0.02   ,					lowerCAmelCase    :				Union[str, Any]=1E-5   ,					lowerCAmelCase    :				Tuple=True   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				List[Any]=True   ,					lowerCAmelCase    :				Union[str, Any]=10   ,					lowerCAmelCase    :				Optional[Any]=8   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			embed_dim
										UpperCAmelCase         =			depths
										UpperCAmelCase         =			num_heads
										UpperCAmelCase         =			window_size
										UpperCAmelCase         =			mlp_ratio
										UpperCAmelCase         =			qkv_bias
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			drop_path_rate
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			use_absolute_embeddings
										UpperCAmelCase         =			patch_norm
										UpperCAmelCase         =			layer_norm_eps
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			scope
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			type_sequence_label_size
										UpperCAmelCase         =			encoder_stride




				def   a__(	self    :				str   )->  str:

										"""simple docstring"""




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

										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.type_sequence_label_size   )

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values, labels




				def   a__(	self    :				Optional[int]   )->  int:

										"""simple docstring"""




										return SwinvaConfig(
										    image_size=self.image_size   ,					patch_size=self.patch_size   ,					num_channels=self.num_channels   ,					embed_dim=self.embed_dim   ,					depths=self.depths   ,					num_heads=self.num_heads   ,					window_size=self.window_size   ,					mlp_ratio=self.mlp_ratio   ,					qkv_bias=self.qkv_bias   ,					hidden_dropout_prob=self.hidden_dropout_prob   ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,					drop_path_rate=self.drop_path_rate   ,					hidden_act=self.hidden_act   ,					use_absolute_embeddings=self.use_absolute_embeddings   ,					path_norm=self.patch_norm   ,					layer_norm_eps=self.layer_norm_eps   ,					initializer_range=self.initializer_range   ,					encoder_stride=self.encoder_stride   ,					)




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				List[Any]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			SwinvaModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )

										UpperCAmelCase         =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
										UpperCAmelCase         =			int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, expected_seq_len, expected_dim)   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			SwinvaForMaskedImageModeling(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(
										    result.logits.shape   ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

										# test greyscale images
										UpperCAmelCase         =			1
										UpperCAmelCase         =			SwinvaForMaskedImageModeling(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()

										UpperCAmelCase         =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, 1, self.image_size, self.image_size)   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Dict   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.type_sequence_label_size
										UpperCAmelCase         =			SwinvaForImageClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.type_sequence_label_size)   )





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

										"""simple docstring"""




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







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   List[str]							= (
				    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
				)
				__magic_name__       :   List[str]							= (
				    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__       :   Optional[int]							= False
				__magic_name__       :   List[Any]							= False
				__magic_name__       :   List[str]							= False
				__magic_name__       :   List[str]							= False




				def   a__(	self    :				Dict   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			SwinvaModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					embed_dim=37   )




				def   a__(	self    :				Optional[int]   )->  Tuple:

										"""simple docstring"""




										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




				def   a__(	self    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




				@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''   )
				def   a__(	self    :				Tuple   )->  Union[str, Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Swinv2 does not use inputs_embeds'''   )
				def   a__(	self    :				Any   )->  Any:

										"""simple docstring"""




										pass




				def   a__(	self    :				str   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

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




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase         =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




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

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																UpperCAmelCase         =			False
																UpperCAmelCase         =			True
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																model.to(lowerCAmelCase   )
																model.eval()
																with torch.no_grad():
																						UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )
																UpperCAmelCase         =			outputs.attentions
																UpperCAmelCase         =			len(self.model_tester.depths   )
																self.assertEqual(len(lowerCAmelCase   )   ,					lowerCAmelCase   )

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																UpperCAmelCase         =			True
																UpperCAmelCase         =			config.window_size**2
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																model.to(lowerCAmelCase   )
																model.eval()
																with torch.no_grad():
																						UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )
																UpperCAmelCase         =			outputs.attentions
																self.assertEqual(len(lowerCAmelCase   )   ,					lowerCAmelCase   )

																self.assertListEqual(
																    list(attentions[0].shape[-3:]   )   ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,					)
																UpperCAmelCase         =			len(lowerCAmelCase   )

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

																if hasattr(self.model_tester   ,					'''num_hidden_states_types'''   ):
																						UpperCAmelCase         =			self.model_tester.num_hidden_states_types
																else:
																						# also another +1 for reshaped_hidden_states
																						UpperCAmelCase         =			2
																self.assertEqual(out_len + added_hidden_states   ,					len(lowerCAmelCase   )   )

																UpperCAmelCase         =			outputs.attentions

																self.assertEqual(len(lowerCAmelCase   )   ,					lowerCAmelCase   )

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]   )   ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,					)




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Any   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			model_class(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()

										with torch.no_grad():
																UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase         =			outputs.hidden_states

										UpperCAmelCase         =			getattr(
										    self.model_tester   ,					'''expected_num_hidden_layers'''   ,					len(self.model_tester.depths   ) + 1   )
										self.assertEqual(len(lowerCAmelCase   )   ,					lowerCAmelCase   )

										# Swinv2 has a different seq_length
										UpperCAmelCase         =			(
										    config.patch_size
										    if isinstance(config.patch_size   ,					collections.abc.Iterable   )
										    else (config.patch_size, config.patch_size)
										)

										UpperCAmelCase         =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]   )   ,					[num_patches, self.model_tester.embed_dim]   ,					)

										UpperCAmelCase         =			outputs.reshaped_hidden_states
										self.assertEqual(len(lowerCAmelCase   )   ,					lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			reshaped_hidden_states[0].shape
										UpperCAmelCase         =			(
										    reshaped_hidden_states[0].view(lowerCAmelCase   ,					lowerCAmelCase   ,					height * width   ).permute(0   ,					2   ,					1   )
										)
										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:]   )   ,					[num_patches, self.model_tester.embed_dim]   ,					)




				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										UpperCAmelCase         =			(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size   ,					collections.abc.Iterable   )
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																self.check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

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

																self.check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )




				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			3

										UpperCAmelCase         =			(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size   ,					collections.abc.Iterable   )
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)
										UpperCAmelCase         =			(
										    config.patch_size
										    if isinstance(config.patch_size   ,					collections.abc.Iterable   )
										    else (config.patch_size, config.patch_size)
										)

										UpperCAmelCase         =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										UpperCAmelCase         =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																self.check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					(padded_height, padded_width)   )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																UpperCAmelCase         =			True
																self.check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					(padded_height, padded_width)   )




				def   a__(	self    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




				@slow
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			SwinvaModel.from_pretrained(lowerCAmelCase   )
																self.assertIsNotNone(lowerCAmelCase   )





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

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										UpperCAmelCase         =			_config_zero_init(lowerCAmelCase   )
										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(config=lowerCAmelCase   )
																for name, param in model.named_parameters():
																						if "embeddings" not in name and "logit_scale" not in name and 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"""   ,					)







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




				@cached_property
				def   a__(	self    :				List[Any]   )->  str:

										"""simple docstring"""




										return (
										    AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''   )
										    if is_vision_available()
										    else None
										)





				@slow
				def   a__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''   ).to(
										    lowerCAmelCase   )
										UpperCAmelCase         =			self.default_image_processor

										UpperCAmelCase         =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			torch.Size((1, 1000)   )
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )
										UpperCAmelCase         =			torch.tensor([-0.3947, -0.4306, 0.0026]   ).to(lowerCAmelCase   )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,					lowerCAmelCase   ,					atol=1E-4   )   )









'''simple docstring'''



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.generation import DisjunctiveConstraint






@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 4], [1, 2, 3, 4]]
										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )
										self.assertTrue(isinstance(dc.token_ids   ,					lowerCAmelCase   )   )

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )




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

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2], [1, 2, 3, 4]]

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint(lowerCAmelCase   )  # fails here




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(3   )
										UpperCAmelCase         =			stepped is True and completed is True and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 3]   )





				def   a__(	self    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(4   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2, 4]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(5   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

										dc.reset()

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 3   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 2   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(5   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.remaining() == 0   )
										self.assertTrue(dc.current_seq == [1, 2, 5]   )





