


"""simple docstring"""







from collections import Counter

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

a__    :							Union[str, Any]            =					datasets.load_iris()

a__    :							Any            =					np.array(data['''data'''])
a__    :							Dict            =					np.array(data['''target'''])
a__    :							Optional[int]            =					data['''target_names''']

a__      ,					a__      ,					a__      ,					a__    :							Dict            =					train_test_split(X, y)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=5       ):




					'''simple docstring'''


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




"""simple docstring"""







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Tuple=1_3      ,      UpperCAmelCase__      :    Optional[int]=3_0      ,      UpperCAmelCase__      :    Any=2      ,      UpperCAmelCase__      :    List[str]=3      ,      UpperCAmelCase__      :    Optional[Any]=True      ,      UpperCAmelCase__      :    Optional[int]=True      ,      UpperCAmelCase__      :    int=3_2      ,      UpperCAmelCase__      :    Any=5      ,      UpperCAmelCase__      :    Union[str, Any]=4      ,      UpperCAmelCase__      :    List[str]=3_7      ,      UpperCAmelCase__      :    Tuple="gelu"      ,      UpperCAmelCase__      :    List[str]=0.1      ,      UpperCAmelCase__      :    str=0.1      ,      UpperCAmelCase__      :    Any=1_0      ,      UpperCAmelCase__      :    str=0.02      ,      )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    parent
						__SCREAMING_SNAKE_CASE						       =    batch_size
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    is_training
						__SCREAMING_SNAKE_CASE						       =    use_labels
						__SCREAMING_SNAKE_CASE						       =    hidden_size
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    intermediate_size
						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    type_sequence_label_size
						__SCREAMING_SNAKE_CASE						       =    initializer_range

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						__SCREAMING_SNAKE_CASE						       =    (image_size // patch_size) ** 2
						__SCREAMING_SNAKE_CASE						       =    num_patches + 1




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						__SCREAMING_SNAKE_CASE						       =    ViTConfig(
						    image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      hidden_size=self.hidden_size      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      intermediate_size=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      is_decoder=UpperCAmelCase__      ,      initializer_range=self.initializer_range      ,      )

						return config, pixel_values




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    List[str]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    FlaxViTModel(config=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						__SCREAMING_SNAKE_CASE						       =    (self.image_size, self.image_size)
						__SCREAMING_SNAKE_CASE						       =    (self.patch_size, self.patch_size)
						__SCREAMING_SNAKE_CASE						       =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, num_patches + 1, self.hidden_size)       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Tuple       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    self.type_sequence_label_size
						__SCREAMING_SNAKE_CASE						       =    FlaxViTForImageClassification(config=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						__SCREAMING_SNAKE_CASE						       =    1
						__SCREAMING_SNAKE_CASE						       =    FlaxViTForImageClassification(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )




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




@require_flax
class 			UpperCamelCase_     (			UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  List[str]    =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




	def   UpperCAmelCase_				(			self      :    int       )	->     None:
						__SCREAMING_SNAKE_CASE						       =    FlaxViTModelTester(self       )
						__SCREAMING_SNAKE_CASE						       =    ConfigTester(self      ,      config_class=UpperCAmelCase__      ,      has_text_modality=UpperCAmelCase__      ,      hidden_size=3_7       )




	def   UpperCAmelCase_				(			self      :    int       )	->     Union[str, Any]:
						self.config_tester.run_common_tests()




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Any:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											__SCREAMING_SNAKE_CASE						       =    ["pixel_values"]
											self.assertListEqual(arg_names[:1]      ,      UpperCAmelCase__       )




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

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__       ):
																__SCREAMING_SNAKE_CASE						       =    self._prepare_for_class(UpperCAmelCase__      ,      UpperCAmelCase__       )
																__SCREAMING_SNAKE_CASE						       =    model_class(UpperCAmelCase__       )

																@jax.jit
																def model_jitted(UpperCAmelCase__      :    Optional[int]      ,      **UpperCAmelCase__      :    Any       ):
																					return model(pixel_values=UpperCAmelCase__      ,      **UpperCAmelCase__       )

																with self.subTest("JIT Enabled"       ):
																					__SCREAMING_SNAKE_CASE						       =    model_jitted(**UpperCAmelCase__       ).to_tuple()

																with self.subTest("JIT Disabled"       ):
																					with jax.disable_jit():
																										__SCREAMING_SNAKE_CASE						       =    model_jitted(**UpperCAmelCase__       ).to_tuple()

																self.assertEqual(len(UpperCAmelCase__       )      ,      len(UpperCAmelCase__       )       )
																for jitted_output, output in zip(UpperCAmelCase__      ,      UpperCAmelCase__       ):
																					self.assertEqual(jitted_output.shape      ,      output.shape       )




	@slow
	def   UpperCAmelCase_				(			self      :    int       )	->     Union[str, Any]:
						for model_class_name in self.all_model_classes:
											__SCREAMING_SNAKE_CASE						       =    model_class_name.from_pretrained("google/vit-base-patch16-224"       )
											__SCREAMING_SNAKE_CASE						       =    model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
											self.assertIsNotNone(UpperCAmelCase__       )
