


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				,   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__=0.02				,   a__=1e-5				,   a__=True				,   a__=None				,   a__=True				,   a__=10				,   a__=8				,   )	->				Optional[Any]:
					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      _UpperCAmelCase					(						self   )	->				str:
					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      _UpperCAmelCase					(						self   )	->				Tuple:
					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__   )	->				Union[str, Any]:
					A								=				SwinvaModel(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__   )

					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      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				List[str]:
					A								=				SwinvaForMaskedImageModeling(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__   )
					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(a__   )
					model.to(a__   )
					model.eval()

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				Dict:
					A								=				self.type_sequence_label_size
					A								=				SwinvaForImageClassification(a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__				,   labels=a__   )
					self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.prepare_config_and_inputs()
					A					,			A					,			A								=				config_and_inputs
					A								=				{"""pixel_values""": pixel_values}
					return config, inputs_dict






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

				"""simple docstring"""







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

				lowerCAmelCase 		= False
				lowerCAmelCase 		= False
				lowerCAmelCase 		= False
				lowerCAmelCase 		= False

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				SwinvaModelTester(self   )
					A								=				ConfigTester(self				,   config_class=a__				,   embed_dim=37   )

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					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   )	->				Tuple:
					A								=				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   )	->				Tuple:
					pass

				@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""   )
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					pass

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

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

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						A								=				model_class(a__   )
						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]				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					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(a__   )
						model.to(a__   )
						model.eval()
						with torch.no_grad():
							A								=				model(**self._prepare_for_class(a__				,   a__   )   )
						A								=				outputs.attentions
						A								=				len(self.model_tester.depths   )
						self.assertEqual(len(a__   )				,   a__   )

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

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

						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(a__   )   )

						A								=				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__   )	->				Optional[int]:
					A								=				model_class(a__   )
					model.to(a__   )
					model.eval()

					with torch.no_grad():
						A								=				model(**self._prepare_for_class(a__				,   a__   )   )

					A								=				outputs.hidden_states

					A								=				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								=				(
					    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(a__   )				,   a__   )

					A					,			A					,			A					,			A								=				reshaped_hidden_states[0].shape
					A								=				(
					    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   )	->				List[str]:
					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(a__				,   a__				,   a__				,   a__   )

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

						self.check_hidden_states_output(a__				,   a__				,   a__				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					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(a__				,   a__				,   a__				,   (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(a__				,   a__				,   a__				,   (padded_height, padded_width)   )

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*a__   )

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a__   )

				@slow
				def      _UpperCAmelCase					(						self   )	->				int:
					for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A								=				SwinvaModel.from_pretrained(a__   )
						self.assertIsNotNone(a__   )

				def      _UpperCAmelCase					(						self   )	->				int:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

					A								=				_config_zero_init(a__   )
					for model_class in self.all_model_classes:
						A								=				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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				@cached_property
				def      _UpperCAmelCase					(						self   )	->				str:
					return (
					    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""   )
					    if is_vision_available()
					    else None
					)

				@slow
				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""   ).to(
					    a__   )
					A								=				self.default_image_processor

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

					# forward pass
					with torch.no_grad():
						A								=				model(**a__   )

					# verify the logits
					A								=				torch.Size((1, 1000)   )
					self.assertEqual(outputs.logits.shape				,   a__   )
					A								=				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   )   )







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



