

'''simple docstring'''
import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
     import os

     from flax.core.frozen_dict import unfreeze
     from flax.traverse_util import flatten_dict

     from transformers import FlaxBertModel

     _UpperCAmelCase  :		Optional[int]  =		'''0.12'''  # assumed parallelism: 8

@require_flax
@is_staging_test
class 						__magic_name__   (							unittest.TestCase ):







 @classmethod
 def 						_A(       cls   ):
   lowercase									=TOKEN
   HfFolder.save_token(snake_case_   )







 @classmethod
 def 						_A(       cls   ):
   try:
     delete_repo(token=cls._token  , repo_id='''test-model-flax'''   )
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token  , repo_id='''valid_org/test-model-flax-org'''   )
   except HTTPError:
     pass







 def 						_A(       self   ):
   lowercase									=BertConfig(
       vocab_size=99  , hidden_size=32  , num_hidden_layers=5  , num_attention_heads=4  , intermediate_size=37   )
   lowercase									=FlaxBertModel(snake_case_   )
   model.push_to_hub('''test-model-flax'''  , use_auth_token=self._token   )

   lowercase									=FlaxBertModel.from_pretrained(f'{USER}/test-model-flax'   )

   lowercase									=flatten_dict(unfreeze(model.params   )   )
   lowercase									=flatten_dict(unfreeze(new_model.params   )   )

   for key in base_params.keys():
     lowercase									=(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(snake_case_  , 1E-3  , msg=f'{key} not identical'   )

   # Reset repo
   delete_repo(token=self._token  , repo_id='''test-model-flax'''   )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(snake_case_  , repo_id='''test-model-flax'''  , push_to_hub=snake_case_  , use_auth_token=self._token   )

   lowercase									=FlaxBertModel.from_pretrained(f'{USER}/test-model-flax'   )

   lowercase									=flatten_dict(unfreeze(model.params   )   )
   lowercase									=flatten_dict(unfreeze(new_model.params   )   )

   for key in base_params.keys():
     lowercase									=(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(snake_case_  , 1E-3  , msg=f'{key} not identical'   )







 def 						_A(       self   ):
   lowercase									=BertConfig(
       vocab_size=99  , hidden_size=32  , num_hidden_layers=5  , num_attention_heads=4  , intermediate_size=37   )
   lowercase									=FlaxBertModel(snake_case_   )
   model.push_to_hub('''valid_org/test-model-flax-org'''  , use_auth_token=self._token   )

   lowercase									=FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''   )

   lowercase									=flatten_dict(unfreeze(model.params   )   )
   lowercase									=flatten_dict(unfreeze(new_model.params   )   )

   for key in base_params.keys():
     lowercase									=(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(snake_case_  , 1E-3  , msg=f'{key} not identical'   )

   # Reset repo
   delete_repo(token=self._token  , repo_id='''valid_org/test-model-flax-org'''   )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(
         snake_case_  , repo_id='''valid_org/test-model-flax-org'''  , push_to_hub=snake_case_  , use_auth_token=self._token   )

   lowercase									=FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''   )

   lowercase									=flatten_dict(unfreeze(model.params   )   )
   lowercase									=flatten_dict(unfreeze(new_model.params   )   )

   for key in base_params.keys():
     lowercase									=(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(snake_case_  , 1E-3  , msg=f'{key} not identical'   )




def 	UpperCamelCase   (       lowercase_						:    Optional[Any]     ,					lowercase_						:    str	)		->	Any:




  '''simple docstring'''



  lowercase									=True
  lowercase									=flatten_dict(modela.params	)
  lowercase									=flatten_dict(modela.params	)
  for key in flat_params_a.keys():
    if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]	)	) > 1E-4:
      lowercase									=False

  return models_are_equal




@require_flax
class 						__magic_name__   (							unittest.TestCase ):







 def 						_A(       self   ):
   lowercase									=BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''   )
   lowercase									=FlaxBertModel(snake_case_   )

   lowercase									='''bert'''
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(os.path.join(snake_case_  , snake_case_   )   )

     with self.assertRaises(snake_case_   ):
       lowercase									=FlaxBertModel.from_pretrained(snake_case_   )

     lowercase									=FlaxBertModel.from_pretrained(snake_case_  , subfolder=snake_case_   )

   self.assertTrue(check_models_equal(snake_case_  , snake_case_   )   )







 def 						_A(       self   ):
   lowercase									=BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''   )
   lowercase									=FlaxBertModel(snake_case_   )

   lowercase									='''bert'''
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(os.path.join(snake_case_  , snake_case_   )  , max_shard_size='''10KB'''   )

     with self.assertRaises(snake_case_   ):
       lowercase									=FlaxBertModel.from_pretrained(snake_case_   )

     lowercase									=FlaxBertModel.from_pretrained(snake_case_  , subfolder=snake_case_   )

   self.assertTrue(check_models_equal(snake_case_  , snake_case_   )   )







 def 						_A(       self   ):
   lowercase									='''bert'''
   lowercase									='''hf-internal-testing/tiny-random-bert-subfolder'''

   with self.assertRaises(snake_case_   ):
     lowercase									=FlaxBertModel.from_pretrained(snake_case_   )

   lowercase									=FlaxBertModel.from_pretrained(snake_case_  , subfolder=snake_case_   )

   self.assertIsNotNone(snake_case_   )







 def 						_A(       self   ):
   lowercase									='''bert'''
   lowercase									='''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
   with self.assertRaises(snake_case_   ):
     lowercase									=FlaxBertModel.from_pretrained(snake_case_   )

   lowercase									=FlaxBertModel.from_pretrained(snake_case_  , subfolder=snake_case_   )

   self.assertIsNotNone(snake_case_   )


"""simple docstring"""

from collections import Counter

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

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

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

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




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



