
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 :			Tuple							,A :			Optional[Any]							,A :			Optional[int]=13							,A :			Optional[Any]=30							,A :			Union[str, Any]=2							,A :			Dict=3							,A :			Dict=True							,A :			Union[str, Any]=True							,A :			Optional[Any]=32							,A :			str=5							,A :			List[Any]=4							,A :			Union[str, Any]=37							,A :			Union[str, Any]="gelu"							,A :			Tuple=0.1							,A :			List[str]=0.1							,A :			int=10							,A :			Dict=0.02							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			image_size
					__A             =			patch_size
					__A             =			num_channels
					__A             =			is_training
					__A             =			use_labels
					__A             =			hidden_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			intermediate_size
					__A             =			hidden_act
					__A             =			hidden_dropout_prob
					__A             =			attention_probs_dropout_prob
					__A             =			type_sequence_label_size
					__A             =			initializer_range

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






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

					__A             =			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=A							,initializer_range=self.initializer_range							,)

					return config, pixel_values






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]							,A :			List[str]			):
					__A             =			FlaxViTModel(config=A			)
					__A             =			model(A			)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
					__A             =			(self.image_size, self.image_size)
					__A             =			(self.patch_size, self.patch_size)
					__A             =			(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 :			Any							,A :			int							,A :			Union[str, Any]			):
					__A             =			self.type_sequence_label_size
					__A             =			FlaxViTForImageClassification(config=A			)
					__A             =			model(A			)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size)			)

					# test greyscale images
					__A             =			1
					__A             =			FlaxViTForImageClassification(A			)

					__A             =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
					__A             =			model(A			)







	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.prepare_config_and_inputs()
					(
					    (
					    __A
					)      ,							(
					    __A
					)      ,							
					)             =			config_and_inputs
					__A             =			{"pixel_values": pixel_values}
					return config, inputs_dict





@require_flax
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			FlaxViTModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,has_text_modality=A							,hidden_size=37			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					self.config_tester.run_common_tests()






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A			)






	def     UpperCamelCase_  (	self :			Union[str, 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.__call__			)
									# 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 :			int			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__			):
													__A             =			self._prepare_for_class(A							,A			)
													__A             =			model_class(A			)

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

													with self.subTest("JIT Enabled"			):
																	__A             =			model_jitted(**A			).to_tuple()

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

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







	@slow
	def     UpperCamelCase_  (	self :			Dict			):
					for model_class_name in self.all_model_classes:
									__A             =			model_class_name.from_pretrained("google/vit-base-patch16-224"			)
									__A             =			model(np.ones((1, 3, 2_24, 2_24)			)			)
									self.assertIsNotNone(A			)


from collections import Counter

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

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



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