



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 			__magic_name__ :






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







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

								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   :  str					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  Optional[Any]   )					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase								=  SwinvaForMaskedImageModeling(config=__a   )
								model.to(__a   )
								model.eval()
								UpperCAmelCase								=  model(__a   )
								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(__a   )
								model.to(__a   )
								model.eval()

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






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  Any					,		UpperCamelCase__   :  Tuple   )					->       Optional[Any]:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[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 			__magic_name__ (				lowerCAmelCase__,	lowerCAmelCase__,	unittest.TestCase    ):
		lowercase   :							Optional[int]        =(
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		lowercase   :							str        =(
		    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowercase   :							str        =False
		lowercase   :							List[Any]        =False
		lowercase   :							Any        =False
		lowercase   :							Tuple        =False






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       Optional[Any]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       Union[str, Any]:
								'''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 SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  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 SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       str:
								'''simple docstring'''







								pass






		@unittest.skip(reason="Swinv2 does not use inputs_embeds"   )
		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       Tuple:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       Union[str, 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(__a   )
														self.assertIsInstance(model.get_input_embeddings()					,		(nn.Module)   )
														UpperCAmelCase								=  model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__a					,		nn.Linear   )   )






		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase								=  model_class(__a   )
														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]					,		__a   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple   )					->       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(__a   )
														model.to(__a   )
														model.eval()
														with torch.no_grad():
																				UpperCAmelCase								=  model(**self._prepare_for_class(__a					,		__a   )   )
														UpperCAmelCase								=  outputs.attentions
														UpperCAmelCase								=  len(self.model_tester.depths   )
														self.assertEqual(len(__a   )					,		__a   )

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

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

														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(__a   )   )

														UpperCAmelCase								=  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 SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]					,		UpperCamelCase__   :  Any					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  int   )					->       Optional[int]:
								'''simple docstring'''







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

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

								UpperCAmelCase								=  outputs.hidden_states

								UpperCAmelCase								=  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
								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(__a   )					,		__a   )

								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase								=  reshaped_hidden_states[0].shape
								UpperCAmelCase								=  (
								    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 SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       str:
								'''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(__a					,		__a					,		__a					,		__a   )

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

														self.check_hidden_states_output(__a					,		__a					,		__a					,		__a   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]   )					->       List[str]:
								'''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(__a					,		__a					,		__a					,		(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(__a					,		__a					,		__a					,		(padded_height, padded_width)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       Optional[Any]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Union[str, Any]:
								'''simple docstring'''







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






		@slow
		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       str:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Optional[int]:
								'''simple docstring'''







								UpperCAmelCase							,		UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase								=  _config_zero_init(__a   )
								for model_class in self.all_model_classes:
														UpperCAmelCase								=  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 			__magic_name__ (				unittest.TestCase    ):






		@cached_property
		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       Union[str, Any]:
								'''simple docstring'''







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







		@slow
		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       List[Any]:
								'''simple docstring'''







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

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

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

								# verify the logits
								UpperCAmelCase								=  torch.Size((1, 10_00)   )
								self.assertEqual(outputs.logits.shape					,		__a   )
								UpperCAmelCase								=  torch.tensor([-0.39_47, -0.43_06, 0.00_26]   ).to(__a   )
								self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		__a					,		atol=1e-4   )   )







"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     False
			while is_sorted is False:  # Until all the indices are traversed keep looping
						__lowerCAmelCase							       =     True
						for i in range(0       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all even indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False

						for i in range(1       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all odd indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False
			return input_list


if __name__ == "__main__":
	print("Enter list to be sorted")
	A	:  Tuple            =     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A	:  Dict            =     odd_even_sort(input_list)
	print("The sorted list is")
	print(sorted_list)




