






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  :




    """simple docstring"""



    def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=16    ,	_lowerCAmelCase=[1, 2, 1]    ,	_lowerCAmelCase=[2, 2, 4]    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=2.0    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase="gelu"    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=1e-5    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=10    ,	_lowerCAmelCase=8    ,	):



        '''simple docstring'''
        lowerCAmelCase__   :List[str]      							=				parent
        lowerCAmelCase__   :Any      							=				batch_size
        lowerCAmelCase__   :List[str]      							=				image_size
        lowerCAmelCase__   :str      							=				patch_size
        lowerCAmelCase__   :Optional[Any]      							=				num_channels
        lowerCAmelCase__   :List[Any]      							=				embed_dim
        lowerCAmelCase__   :Dict      							=				depths
        lowerCAmelCase__   :str      							=				num_heads
        lowerCAmelCase__   :Union[str, Any]      							=				window_size
        lowerCAmelCase__   :int      							=				mlp_ratio
        lowerCAmelCase__   :int      							=				qkv_bias
        lowerCAmelCase__   :List[str]      							=				hidden_dropout_prob
        lowerCAmelCase__   :Union[str, Any]      							=				attention_probs_dropout_prob
        lowerCAmelCase__   :str      							=				drop_path_rate
        lowerCAmelCase__   :Optional[int]      							=				hidden_act
        lowerCAmelCase__   :int      							=				use_absolute_embeddings
        lowerCAmelCase__   :Optional[Any]      							=				patch_norm
        lowerCAmelCase__   :Optional[Any]      							=				layer_norm_eps
        lowerCAmelCase__   :List[Any]      							=				initializer_range
        lowerCAmelCase__   :Dict      							=				is_training
        lowerCAmelCase__   :str      							=				scope
        lowerCAmelCase__   :str      							=				use_labels
        lowerCAmelCase__   :int      							=				type_sequence_label_size
        lowerCAmelCase__   :Tuple      							=				encoder_stride
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :str      							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        lowerCAmelCase__   :Any      							=				None
        if self.use_labels:
            lowerCAmelCase__   :Tuple      							=				ids_tensor([self.batch_size]    ,	self.type_sequence_label_size		)

        lowerCAmelCase__   :List[Any]      							=				self.get_config()

        return config, pixel_values, labels
    def 			snake_case_      (   self		):



        '''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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



        '''simple docstring'''
        lowerCAmelCase__   :Optional[int]      							=				SwinvaModel(config=__UpperCamelCase		)
        model.to(__UpperCamelCase		)
        model.eval()
        lowerCAmelCase__   :Optional[int]      							=				model(__UpperCamelCase		)

        lowerCAmelCase__   :Tuple      							=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
        lowerCAmelCase__   :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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



        '''simple docstring'''
        lowerCAmelCase__   :Dict      							=				SwinvaForMaskedImageModeling(config=__UpperCamelCase		)
        model.to(__UpperCamelCase		)
        model.eval()
        lowerCAmelCase__   :List[Any]      							=				model(__UpperCamelCase		)
        self.parent.assertEqual(
            result.logits.shape    ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

        # test greyscale images
        lowerCAmelCase__   :int      							=				1
        lowerCAmelCase__   :Union[str, Any]      							=				SwinvaForMaskedImageModeling(__UpperCamelCase		)
        model.to(__UpperCamelCase		)
        model.eval()

        lowerCAmelCase__   :List[Any]      							=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        lowerCAmelCase__   :int      							=				model(__UpperCamelCase		)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, 1, self.image_size, self.image_size)		)
    def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



        '''simple docstring'''
        lowerCAmelCase__   :List[str]      							=				self.type_sequence_label_size
        lowerCAmelCase__   :Any      							=				SwinvaForImageClassification(__UpperCamelCase		)
        model.to(__UpperCamelCase		)
        model.eval()
        lowerCAmelCase__   :str      							=				model(__UpperCamelCase    ,	labels=__UpperCamelCase		)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.type_sequence_label_size)		)



    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :Optional[Any]      							=				self.prepare_config_and_inputs()
        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				config_and_inputs
        lowerCAmelCase__   :int      							=				{"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class 	_UpperCAmelCase  (  __snake_case				,							__snake_case				,							unittest.TestCase		):




    """simple docstring"""



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

    A          =    False
    A          =    False
    A          =    False
    A          =    False
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :List[Any]      							=				SwinvaModelTester(self		)
        lowerCAmelCase__   :List[Any]      							=				ConfigTester(self    ,	config_class=__UpperCamelCase    ,	embed_dim=37		)
    def 			snake_case_      (   self		):



        '''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 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :List[str]      							=				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 			snake_case_      (   self		):



        '''simple docstring'''
        pass
    @unittest.skip(reason="Swinv2 does not use inputs_embeds"		)
    def 			snake_case_      (   self		):



        '''simple docstring'''
        pass
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            lowerCAmelCase__   :str      							=				model_class(__UpperCamelCase		)
            self.assertIsInstance(model.get_input_embeddings()    ,	(nn.Module)		)
            lowerCAmelCase__   :Tuple      							=				model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__UpperCamelCase    ,	nn.Linear		)		)
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__							,lowerCAmelCase__   :str      							=				self.model_tester.prepare_config_and_inputs_for_common()

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

            lowerCAmelCase__   :Dict      							=				["pixel_values"]
            self.assertListEqual(arg_names[:1]    ,	__UpperCamelCase		)
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__   :int      							=				True

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

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            lowerCAmelCase__   :Dict      							=				True
            lowerCAmelCase__   :Tuple      							=				config.window_size**2
            lowerCAmelCase__   :Any      							=				model_class(__UpperCamelCase		)
            model.to(__UpperCamelCase		)
            model.eval()
            with torch.no_grad():
                lowerCAmelCase__   :Dict      							=				model(**self._prepare_for_class(__UpperCamelCase    ,	__UpperCamelCase		)		)
            lowerCAmelCase__   :str      							=				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]    ,	)
            lowerCAmelCase__   :Optional[Any]      							=				len(__UpperCamelCase		)

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

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

            lowerCAmelCase__   :Tuple      							=				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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



        '''simple docstring'''
        lowerCAmelCase__   :Optional[Any]      							=				model_class(__UpperCamelCase		)
        model.to(__UpperCamelCase		)
        model.eval()

        with torch.no_grad():
            lowerCAmelCase__   :List[str]      							=				model(**self._prepare_for_class(__UpperCamelCase    ,	__UpperCamelCase		)		)

        lowerCAmelCase__   :str      							=				outputs.hidden_states

        lowerCAmelCase__   :List[str]      							=				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
        lowerCAmelCase__   :str      							=				(
            config.patch_size
            if isinstance(config.patch_size    ,	collections.abc.Iterable		)
            else (config.patch_size, config.patch_size)
        )

        lowerCAmelCase__   :Tuple      							=				(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]    ,	)

        lowerCAmelCase__   :Dict      							=				outputs.reshaped_hidden_states
        self.assertEqual(len(__UpperCamelCase		)    ,	__UpperCamelCase		)

        lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				reshaped_hidden_states[0].shape
        lowerCAmelCase__   :Any      							=				(
            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 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__							,lowerCAmelCase__   :Union[str, Any]      							=				self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__   :Tuple      							=				(
            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:
            lowerCAmelCase__   :str      							=				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"]
            lowerCAmelCase__   :Tuple      							=				True

            self.check_hidden_states_output(__UpperCamelCase    ,	__UpperCamelCase    ,	__UpperCamelCase    ,	__UpperCamelCase		)
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__   :Tuple      							=				3

        lowerCAmelCase__   :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)
        )
        lowerCAmelCase__   :int      							=				(
            config.patch_size
            if isinstance(config.patch_size    ,	collections.abc.Iterable		)
            else (config.patch_size, config.patch_size)
        )

        lowerCAmelCase__   :Optional[int]      							=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        lowerCAmelCase__   :List[Any]      							=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
            lowerCAmelCase__   :Union[str, Any]      							=				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"]
            lowerCAmelCase__   :Optional[Any]      							=				True
            self.check_hidden_states_output(__UpperCamelCase    ,	__UpperCamelCase    ,	__UpperCamelCase    ,	(padded_height, padded_width)		)
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :Tuple      							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase		)
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :str      							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)
    @slow
    def 			snake_case_      (   self		):



        '''simple docstring'''


        for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCAmelCase__   :Optional[int]      							=				SwinvaModel.from_pretrained(__UpperCamelCase		)
            self.assertIsNotNone(__UpperCamelCase		)



    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__   :Tuple      							=				_config_zero_init(__UpperCamelCase		)
        for model_class in self.all_model_classes:
            lowerCAmelCase__   :str      							=				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 	_UpperCAmelCase  (  unittest.TestCase		):




    """simple docstring"""



    @cached_property
    def 			snake_case_      (   self		):



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



    @slow
    def 			snake_case_      (   self		):



        '''simple docstring'''
        lowerCAmelCase__   :Optional[Any]      							=				SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		).to(
            __UpperCamelCase		)
        lowerCAmelCase__   :Union[str, Any]      							=				self.default_image_processor

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

        # forward pass
        with torch.no_grad():
            lowerCAmelCase__   :List[Any]      							=				model(**__UpperCamelCase		)

        # verify the logits
        lowerCAmelCase__   :List[str]      							=				torch.Size((1, 1_000)		)
        self.assertEqual(outputs.logits.shape    ,	__UpperCamelCase		)
        lowerCAmelCase__   :Union[str, Any]      							=				torch.tensor([-0.3947, -0.4306, 0.0026]		).to(__UpperCamelCase		)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	__UpperCamelCase    ,	atol=1e-4		)		)











def 				snake_case__							(							UpperCAmelCase							:  Tuple       ):
    lowerCAmelCase__   :List[Any]      							=				len(UpperCAmelCase       )
    for i in range(length - 1       ):
        lowerCAmelCase__   :Union[str, Any]      							=				i
        for k in range(i + 1    ,			UpperCAmelCase       ):
            if collection[k] < collection[least]:
                lowerCAmelCase__   :Any      							=				k
        if least != i:
            lowerCAmelCase__							,lowerCAmelCase__   :int      							=				(collection[i], collection[least])
    return collection


if __name__ == "__main__":
 _a    :   Optional[int]        =			input("""Enter numbers separated by a comma:\n""").strip()
 _a    :   Any        =			[int(item) for item in user_input.split(""",""")]
 print(selection_sort(unsorted))



