




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
     import tensorflow as tf

if is_tensorflow_text_available():
     from transformers.models.bert import TFBertTokenizer


__a          =			['bert-base-uncased', 'bert-base-cased']
__a          =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







     class   __a(					tf.keras.Model       ):






         """simple docstring"""





         def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
              super().__init__()
              UpperCAmelCase_      :							Tuple       =      tokenizer
              UpperCAmelCase_      :							List[Any]       =      AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      TFAutoModel.from_config(_SCREAMING_SNAKE_CASE  )





         def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      self.bert(**_SCREAMING_SNAKE_CASE  )
              return out["pooler_output"]







@require_tf
@require_tensorflow_text
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Any:
         super().setUp()

         UpperCAmelCase_      :							Optional[Any]       =      [
             BertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
         ]  # repeat for when fast_bert_tokenizer=false
         UpperCAmelCase_      :							Optional[int]       =      [TFBertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
             TFBertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,use_fast_bert_tokenizer=_SCREAMING_SNAKE_CASE  )
             for checkpoint in TOKENIZER_CHECKPOINTS
         ]
         assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

         UpperCAmelCase_      :							Optional[int]       =      [
             '''This is a straightforward English test sentence.''',
             '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
             '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
             '''And some much more rare Chinese: 齉 堃 齉堃''',
             '''Je vais aussi écrire en français pour tester les accents''',
             '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
         ]
         UpperCAmelCase_      :							Optional[int]       =      list(zip(self.test_sentences       ,self.test_sentences[::-1]  )  )





    def 							a__				(				self  )			->		Optional[Any]:
         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers  ):
              for test_inputs in (self.test_sentences, self.paired_sentences):
                   UpperCAmelCase_      :							Optional[Any]       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_tensors='''tf'''       ,padding='''longest'''  )
                   UpperCAmelCase_      :							int       =      tf_tokenizer(_SCREAMING_SNAKE_CASE  )

                   for key in python_outputs.keys():
                        self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape  )  )
                        self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,tf.intaa  ) == tf_outputs[key]  )  )





    @slow
    def 							a__				(				self  )			->		Any:
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_      :							Union[str, Any]       =      tf_tokenizer(self.paired_sentences  )
              UpperCAmelCase_      :							Any       =      tf_tokenizer(
                  text=[sentence[0] for sentence in self.paired_sentences]       ,text_pair=[sentence[1] for sentence in self.paired_sentences]       ,)
              for key in merged_outputs.keys():
                   self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,tf.intaa  ) == separated_outputs[key]  )  )





    @slow
    def 							a__				(				self  )			->		str:
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_      :							List[str]       =      tf.function(_SCREAMING_SNAKE_CASE  )
              for test_inputs in (self.test_sentences, self.paired_sentences):
                   UpperCAmelCase_      :							Tuple       =      tf.constant(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Tuple       =      compiled_tokenizer(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Union[str, Any]       =      tf_tokenizer(_SCREAMING_SNAKE_CASE  )

                   for key in eager_outputs.keys():
                        self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]  )  )





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_      :							Union[str, Any]       =      ModelToSave(tokenizer=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							List[str]       =      tf.convert_to_tensor(self.test_sentences  )
              UpperCAmelCase_      :							Tuple       =      model(_SCREAMING_SNAKE_CASE  )  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                   UpperCAmelCase_      :							Any       =      Path(_SCREAMING_SNAKE_CASE  ) / '''saved.model'''
                   model.save(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      tf.keras.models.load_model(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      loaded_model(_SCREAMING_SNAKE_CASE  )
              # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
              self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output  )  )       ,1e-5  )





def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def lowerCamelCase__       (      _lowercase			,						_lowercase=0				):

     '''simple docstring'''


     return sorted(_lowercase			,						key=lambda _lowercase				: x[column]				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(points_counts - 1				):
          for j in range(i + 1			,						_lowercase				):
               UpperCAmelCase_      :							Optional[int]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[Any]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(min(6			,						points_counts - 1				)			,						_lowercase				):
          for j in range(max(0			,						i - 6				)			,						_lowercase				):
               UpperCAmelCase_      :							List[str]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[int]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase			,						_lowercase				)

     # recursion
     UpperCAmelCase_      :							Optional[int]       =      points_counts // 2
     UpperCAmelCase_      :							List[Any]       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[:mid]			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
     UpperCAmelCase_      :							Union[str, Any]       =      min(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							str       =      []
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
               cross_strip.append(_lowercase				)

     UpperCAmelCase_      :							Optional[Any]       =      dis_between_closest_in_strip(
         _lowercase			,						len(_lowercase				)			,						_lowercase				)
     return min(_lowercase			,						_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      column_based_sort(_lowercase			,						column=0				)
     UpperCAmelCase_      :							List[Any]       =      column_based_sort(_lowercase			,						column=1				)
     return (
         closest_pair_of_points_sqr(
             _lowercase			,						_lowercase			,						_lowercase				)
     ) ** 0.5


if __name__ == "__main__":
     __a          =			[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))