





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 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=13     ,					_A=32     ,					_A=2     ,					_A=3     ,					_A=16     ,					_A=[1, 2, 1]     ,					_A=[2, 2, 4]     ,					_A=2     ,					_A=2.0     ,					_A=True     ,					_A=0.0     ,					_A=0.0     ,					_A=0.1     ,					_A="gelu"     ,					_A=False     ,					_A=True     ,					_A=0.0_2     ,					_A=1e-5     ,					_A=True     ,					_A=None     ,					_A=True     ,					_A=10     ,					_A=8     ,					):
									__A	:						Optional[Any]       =				parent
									__A	:						Optional[Any]       =				batch_size
									__A	:						Optional[Any]       =				image_size
									__A	:						Optional[int]       =				patch_size
									__A	:						Optional[int]       =				num_channels
									__A	:						List[Any]       =				embed_dim
									__A	:						Union[str, Any]       =				depths
									__A	:						Tuple       =				num_heads
									__A	:						Union[str, Any]       =				window_size
									__A	:						str       =				mlp_ratio
									__A	:						int       =				qkv_bias
									__A	:						str       =				hidden_dropout_prob
									__A	:						Optional[int]       =				attention_probs_dropout_prob
									__A	:						int       =				drop_path_rate
									__A	:						Any       =				hidden_act
									__A	:						List[str]       =				use_absolute_embeddings
									__A	:						List[Any]       =				patch_norm
									__A	:						Tuple       =				layer_norm_eps
									__A	:						int       =				initializer_range
									__A	:						Optional[int]       =				is_training
									__A	:						List[str]       =				scope
									__A	:						Union[str, Any]       =				use_labels
									__A	:						Tuple       =				type_sequence_label_size
									__A	:						Tuple       =				encoder_stride



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									__A	:						Optional[Any]       =				None
									if self.use_labels:
														__A	:						int       =				ids_tensor([self.batch_size]     ,					self.type_sequence_label_size     )

									__A	:						Tuple       =				self.get_config()

									return config, pixel_values, labels



				def 					UpperCAmelCase_		(			self     ):
									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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Union[str, Any]       =				SwinvaModel(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						int       =				model(_A     )

									__A	:						Any       =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
									__A	:						Optional[Any]       =				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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						str       =				SwinvaForMaskedImageModeling(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						List[str]       =				model(_A     )
									self.parent.assertEqual(
									    result.logits.shape     ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

									# test greyscale images
									__A	:						List[Any]       =				1
									__A	:						Union[str, Any]       =				SwinvaForMaskedImageModeling(_A     )
									model.to(_A     )
									model.eval()

									__A	:						Tuple       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									__A	:						List[str]       =				model(_A     )
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, 1, self.image_size, self.image_size)     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						List[Any]       =				self.type_sequence_label_size
									__A	:						List[str]       =				SwinvaForImageClassification(_A     )
									model.to(_A     )
									model.eval()
									__A	:						Optional[int]       =				model(_A     ,					labels=_A     )
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)     )





				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				self.prepare_config_and_inputs()
									__A    ,				__A    ,				__A	:						List[Any]       =				config_and_inputs
									__A	:						Optional[Any]       =				{'pixel_values': pixel_values}
									return config, inputs_dict





@require_torch
class 						_A(					snake_case__				, snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Any					    =	(
				    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
				)
				UpperCamelCase    :  Dict					    =	(
				    {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase    :  str					    =	False
				UpperCamelCase    :  List[Any]					    =	False
				UpperCamelCase    :  Any					    =	False
				UpperCamelCase    :  List[str]					    =	False



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				SwinvaModelTester(self     )
									__A	:						str       =				ConfigTester(self     ,					config_class=_A     ,					embed_dim=37     )



				def 					UpperCAmelCase_		(			self     ):
									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 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A     )



				@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				@unittest.skip(reason='Swinv2 does not use inputs_embeds'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						str       =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														__A	:						Optional[Any]       =				model_class(_A     )
														self.assertIsInstance(model.get_input_embeddings()     ,					(nn.Module)     )
														__A	:						Dict       =				model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_A     ,					nn.Linear     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						Any       =				self.model_tester.prepare_config_and_inputs_for_common()

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

														__A	:						List[Any]       =				['pixel_values']
														self.assertListEqual(arg_names[:1]     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						Dict       =				self.model_tester.prepare_config_and_inputs_for_common()
									__A	:						Dict       =				True

									for model_class in self.all_model_classes:
														__A	:						Dict       =				True
														__A	:						Optional[int]       =				False
														__A	:						Union[str, Any]       =				True
														__A	:						Any       =				model_class(_A     )
														model.to(_A     )
														model.eval()
														with torch.no_grad():
																			__A	:						Optional[Any]       =				model(**self._prepare_for_class(_A     ,					_A     )     )
														__A	:						Any       =				outputs.attentions
														__A	:						Union[str, Any]       =				len(self.model_tester.depths     )
														self.assertEqual(len(_A     )     ,					_A     )

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

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

														# Check attention is always last and order is fine
														__A	:						int       =				True
														__A	:						Union[str, Any]       =				True
														__A	:						Dict       =				model_class(_A     )
														model.to(_A     )
														model.eval()
														with torch.no_grad():
																			__A	:						Any       =				model(**self._prepare_for_class(_A     ,					_A     )     )

														if hasattr(self.model_tester     ,					'num_hidden_states_types'     ):
																			__A	:						Optional[Any]       =				self.model_tester.num_hidden_states_types
														else:
																			# also another +1 for reshaped_hidden_states
																			__A	:						int       =				2
														self.assertEqual(out_len + added_hidden_states     ,					len(_A     )     )

														__A	:						Optional[int]       =				outputs.attentions

														self.assertEqual(len(_A     )     ,					_A     )

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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						int       =				model_class(_A     )
									model.to(_A     )
									model.eval()

									with torch.no_grad():
														__A	:						Optional[int]       =				model(**self._prepare_for_class(_A     ,					_A     )     )

									__A	:						Optional[Any]       =				outputs.hidden_states

									__A	:						Dict       =				getattr(
									    self.model_tester     ,					'expected_num_hidden_layers'     ,					len(self.model_tester.depths     ) + 1     )
									self.assertEqual(len(_A     )     ,					_A     )

									# Swinv2 has a different seq_length
									__A	:						List[Any]       =				(
									    config.patch_size
									    if isinstance(config.patch_size     ,					collections.abc.Iterable     )
									    else (config.patch_size, config.patch_size)
									)

									__A	:						int       =				(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]     ,					)

									__A	:						str       =				outputs.reshaped_hidden_states
									self.assertEqual(len(_A     )     ,					_A     )

									__A    ,				__A    ,				__A    ,				__A	:						Union[str, Any]       =				reshaped_hidden_states[0].shape
									__A	:						Optional[Any]       =				(
									    reshaped_hidden_states[0].view(_A     ,					_A     ,					height * width     ).permute(0     ,					2     ,					1     )
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]     )     ,					[num_patches, self.model_tester.embed_dim]     ,					)



				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						Union[str, Any]       =				self.model_tester.prepare_config_and_inputs_for_common()

									__A	:						List[str]       =				(
									    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:
														__A	:						str       =				True
														self.check_hidden_states_output(_A     ,					_A     ,					_A     ,					_A     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														__A	:						Optional[Any]       =				True

														self.check_hidden_states_output(_A     ,					_A     ,					_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						Union[str, Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
									__A	:						Dict       =				3

									__A	:						Optional[int]       =				(
									    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)
									)
									__A	:						int       =				(
									    config.patch_size
									    if isinstance(config.patch_size     ,					collections.abc.Iterable     )
									    else (config.patch_size, config.patch_size)
									)

									__A	:						Optional[Any]       =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									__A	:						Optional[Any]       =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
														__A	:						Optional[int]       =				True
														self.check_hidden_states_output(_A     ,					_A     ,					_A     ,					(padded_height, padded_width)     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														__A	:						str       =				True
														self.check_hidden_states_output(_A     ,					_A     ,					_A     ,					(padded_height, padded_width)     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_A     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__A	:						Union[str, Any]       =				SwinvaModel.from_pretrained(_A     )
														self.assertIsNotNone(_A     )





				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						Optional[Any]       =				self.model_tester.prepare_config_and_inputs_for_common()

									__A	:						int       =				_config_zero_init(_A     )
									for model_class in self.all_model_classes:
														__A	:						Union[str, Any]       =				model_class(config=_A     )
														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 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				@cached_property
				def 					UpperCAmelCase_		(			self     ):
									return (
									    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'     )
									    if is_vision_available()
									    else None
									)





				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'     ).to(
									    _A     )
									__A	:						List[Any]       =				self.default_image_processor

									__A	:						Tuple       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									__A	:						Union[str, Any]       =				image_processor(images=_A     ,					return_tensors='pt'     ).to(_A     )

									# forward pass
									with torch.no_grad():
														__A	:						Any       =				model(**_A     )

									# verify the logits
									__A	:						Optional[int]       =				torch.Size((1, 1000)     )
									self.assertEqual(outputs.logits.shape     ,					_A     )
									__A	:						Optional[Any]       =				torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]     ).to(_A     )
									self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					_A     ,					atol=1e-4     )     )











def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




