



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.roberta.modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				)






class     __a			(  unittest.TestCase					):



	def __init__(    self				:     Dict					,						snake_case_				:     int					,						snake_case_				:     List[Any]=13					,						snake_case_				:     Union[str, Any]=7					,						snake_case_				:     int=True					,						snake_case_				:     List[str]=True					,						snake_case_				:     List[Any]=True					,						snake_case_				:     Tuple=True					,						snake_case_				:     Optional[Any]=99					,						snake_case_				:     List[str]=32					,						snake_case_				:     int=5					,						snake_case_				:     Optional[Any]=4					,						snake_case_				:     List[str]=37					,						snake_case_				:     Dict="gelu"					,						snake_case_				:     str=0.1					,						snake_case_				:     Tuple=0.1					,						snake_case_				:     Dict=5_12					,						snake_case_				:     List[str]=16					,						snake_case_				:     str=2					,						snake_case_				:     Union[str, Any]=0.0_2					,						snake_case_				:     str=4					,						)->				Tuple:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =seq_length
						__lowerCAmelCase						 =is_training
						__lowerCAmelCase						 =use_attention_mask
						__lowerCAmelCase						 =use_token_type_ids
						__lowerCAmelCase						 =use_labels
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_hidden_layers
						__lowerCAmelCase						 =num_attention_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =hidden_act
						__lowerCAmelCase						 =hidden_dropout_prob
						__lowerCAmelCase						 =attention_probs_dropout_prob
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =type_vocab_size
						__lowerCAmelCase						 =type_sequence_label_size
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =num_choices



	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						__lowerCAmelCase						 =ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

						__lowerCAmelCase						 =None
						if self.use_attention_mask:
											__lowerCAmelCase						 =random_attention_mask([self.batch_size, self.seq_length])

						__lowerCAmelCase						 =None
						if self.use_token_type_ids:
											__lowerCAmelCase						 =ids_tensor([self.batch_size, self.seq_length]					,						self.type_vocab_size)

						__lowerCAmelCase						 =RobertaConfig(
						    vocab_size=self.vocab_size					,						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					,						max_position_embeddings=self.max_position_embeddings					,						type_vocab_size=self.type_vocab_size					,						is_decoder=snake_case_					,						initializer_range=self.initializer_range					,						)

						return config, input_ids, token_type_ids, attention_mask



	def 		UpperCamelCase				(    self				:     int)->				Tuple:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =config_and_inputs
						__lowerCAmelCase						 ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
						return config, inputs_dict



	def 		UpperCamelCase				(    self				:     int)->				Union[str, Any]:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =config_and_inputs

						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
						__lowerCAmelCase						 =ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)






@require_flax
class     __a			(  SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							True

	SCREAMING_SNAKE_CASE  	=							(
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)



	def 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[Any]:
						__lowerCAmelCase						 =FlaxRobertaModelTester(self)



	@slow
	def 		UpperCamelCase				(    self				:     str)->				List[str]:
						for model_class_name in self.all_model_classes:
											__lowerCAmelCase						 =model_class_name.from_pretrained("""roberta-base"""					,						from_pt=snake_case_)
											__lowerCAmelCase						 =model(np.ones((1, 1)))
											self.assertIsNotNone(snake_case_)









from collections import Counter

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

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =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]))




