



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 		lowercase						:






	"""simple docstring"""


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

							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   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						Optional[int]					)      ->			List[str]:
							UpperCAmelCase_=       SwinvaForMaskedImageModeling(config=__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase					)
							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(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						int					)      ->			Optional[int]:
							UpperCAmelCase_=       self.type_sequence_label_size
							UpperCAmelCase_=       SwinvaForImageClassification(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()
							UpperCAmelCase_=       model(__UpperCAmelCase						,  labels=__UpperCAmelCase					)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							UpperCAmelCase_=       self.prepare_config_and_inputs()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       config_and_inputs
							UpperCAmelCase_=       {"""pixel_values""": pixel_values}
							return config, inputs_dict






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






	"""simple docstring"""


	a__				: Optional[int]		       =							(
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	a__				: Dict		       =							(
	    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	a__				: Union[str, Any]		       =							False
	a__				: int		       =							False
	a__				: Optional[int]		       =							False
	a__				: Optional[int]		       =							False




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Any:
							UpperCAmelCase_=       SwinvaModelTester(self					)
							UpperCAmelCase_=       ConfigTester(self						,  config_class=__UpperCAmelCase						,  embed_dim=37					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[str]:
							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   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase					)




	@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							pass




	@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[Any]:
							pass




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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[int]:
							UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													UpperCAmelCase_=       model_class(__UpperCAmelCase					)
													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]						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Any:
							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(__UpperCAmelCase					)
													model.to(__UpperCAmelCase					)
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=       model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase					)					)
													UpperCAmelCase_=       outputs.attentions
													UpperCAmelCase_=       len(self.model_tester.depths					)
													self.assertEqual(len(__UpperCAmelCase					)						,  __UpperCAmelCase					)

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

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

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

													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(__UpperCAmelCase					)					)

													UpperCAmelCase_=       outputs.attentions

													self.assertEqual(len(__UpperCAmelCase					)						,  __UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Any					)      ->			Dict:
							UpperCAmelCase_=       model_class(__UpperCAmelCase					)
							model.to(__UpperCAmelCase					)
							model.eval()

							with torch.no_grad():
													UpperCAmelCase_=       model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase					)					)

							UpperCAmelCase_=       outputs.hidden_states

							UpperCAmelCase_=       getattr(
							    self.model_tester						,  """expected_num_hidden_layers"""						,  len(self.model_tester.depths					) + 1					)
							self.assertEqual(len(__UpperCAmelCase					)						,  __UpperCAmelCase					)

							# 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(__UpperCAmelCase					)						,  __UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							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(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)

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

													self.check_hidden_states_output(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			str:
							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(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  (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(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  (padded_height, padded_width)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Dict:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase					)




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			str:
							for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase_=       SwinvaModel.from_pretrained(__UpperCAmelCase					)
													self.assertIsNotNone(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_=       _config_zero_init(__UpperCAmelCase					)
							for model_class in self.all_model_classes:
													UpperCAmelCase_=       model_class(config=__UpperCAmelCase					)
													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 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	@cached_property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[Any]:
							return (
							    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					)
							    if is_vision_available()
							    else None
							)




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Dict:
							UpperCAmelCase_=       SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					).to(
							    __UpperCAmelCase					)
							UpperCAmelCase_=       self.default_image_processor

							UpperCAmelCase_=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
							UpperCAmelCase_=       image_processor(images=__UpperCAmelCase						,  return_tensors="""pt"""					).to(__UpperCAmelCase					)

							# forward pass
							with torch.no_grad():
													UpperCAmelCase_=       model(**__UpperCAmelCase					)

							# verify the logits
							UpperCAmelCase_=       torch.Size((1, 1_000)					)
							self.assertEqual(outputs.logits.shape						,  __UpperCAmelCase					)
							UpperCAmelCase_=       torch.tensor([-0.3_947, -0.4_306, 0.0_026]					).to(__UpperCAmelCase					)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]						,  __UpperCAmelCase						,  atol=1E-4					)					)




from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




