



import operator





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase = False       ,			_UpperCAmelCase = None    ):
    __a							=    operator.lt if reverse else operator.gt
    __a							=    solution or []

    if not arr:
        return solution

    __a							=    [arr.pop(0    )]
    for i, item in enumerate(_UpperCAmelCase    ):
        if _operator(_UpperCAmelCase       ,			sublist[-1]    ):
            sublist.append(_UpperCAmelCase    )
            arr.pop(_UpperCAmelCase    )

    #  merging sublist into solution list
    if not solution:
        solution.extend(_UpperCAmelCase    )
    else:
        while sublist:
            __a							=    sublist.pop(0    )
            for i, xx in enumerate(_UpperCAmelCase    ):
                if not _operator(_UpperCAmelCase       ,			_UpperCAmelCase    ):
                    solution.insert(_UpperCAmelCase       ,			_UpperCAmelCase    )
                    break
            else:
                solution.append(_UpperCAmelCase    )

    strand_sort(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
    return solution


if __name__ == "__main__":
 assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
 assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]










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	:



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


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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

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

           __a							=    self.get_config()

           return config, pixel_values, labels



       def 		_lowerCamelCase      (		self							:					List[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 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

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

           __a							=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           __a							=    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 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

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

           # test greyscale images
           __a							=    1
           __a							=    SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

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



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

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


@require_torch
class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Dict						 =						(
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       UpperCamelCase__  :   Optional[int]						 =						(
           {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase__  :   int						 =						False
       UpperCamelCase__  :   Tuple						 =						False
       UpperCamelCase__  :   Optional[Any]						 =						False
       UpperCamelCase__  :   Optional[Any]						 =						False



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[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 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           pass



       @unittest.skip(reason='''Swinv2 does not use inputs_embeds''')
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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

               __a							=    ['''pixel_values''']
               self.assertListEqual(arg_names[:1]					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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

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

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

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

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

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

               __a							=    outputs.attentions

               self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

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

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

           __a							=    outputs.hidden_states

           __a							=    getattr(
               self.model_tester					,		'''expected_num_hidden_layers'''					,		len(self.model_tester.depths) + 1)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

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

           __a							=    (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							=    outputs.reshaped_hidden_states
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           __a							=    (
               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							=    True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

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

               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

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

           __a							=    (
               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							=    (
               config.patch_size
               if isinstance(config.patch_size					,		collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

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

           for model_class in self.all_model_classes:
               __a							=    True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		(padded_height, padded_width))

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



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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



       @slow
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           __a							=    _config_zero_init(__SCREAMING_SNAKE_CASE)
           for model_class in self.all_model_classes:
               __a							=    model_class(config=__SCREAMING_SNAKE_CASE)
               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   ):



       @cached_property
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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





       @slow
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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

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

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

           # verify the logits
           __a							=    torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape					,		__SCREAMING_SNAKE_CASE)
           __a							=    torch.tensor([-0.39_47, -0.43_06, 0.00_26]).to(__SCREAMING_SNAKE_CASE)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		__SCREAMING_SNAKE_CASE					,		atol=1E-4))





