
'''simple docstring'''





class 	UpperCamelCase__					:






 def __init__(     self					:Union[str, Any]     )			->     int:







       '''simple docstring'''



       __A    		=     [0]
       __A    		=     0






 def 		lowercase_  (     self					:Tuple		,			_A					:int     )			->     None:







       '''simple docstring'''



       __A    		=     self.__heap[i]
       while i // 2 > 0:
             if self.__heap[i] > self.__heap[i // 2]:
                   __A    		=     self.__heap[i // 2]
                   __A    		=     temporary
             i //= 2






 def 		lowercase_  (     self					:str		,			_A					:int     )			->     None:







       '''simple docstring'''



       self.__heap.append(_A     )
       self.__size += 1
       self.__swap_up(self.__size     )






 def 		lowercase_  (     self					:str		,			_A					:int     )			->     None:







       '''simple docstring'''



       while self.__size >= 2 * i:
             if 2 * i + 1 > self.__size:
                   __A    		=     2 * i
             else:
                   if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                         __A    		=     2 * i
                   else:
                         __A    		=     2 * i + 1
             __A    		=     self.__heap[i]
             if self.__heap[i] < self.__heap[bigger_child]:
                   __A    		=     self.__heap[bigger_child]
                   __A    		=     temporary
             __A    		=     bigger_child






 def 		lowercase_  (     self					:Union[str, Any]     )			->     int:







       '''simple docstring'''



       __A    		=     self.__heap[1]
       __A    		=     self.__heap[self.__size]
       self.__size -= 1
       self.__heap.pop()
       self.__swap_down(1     )
       return max_value






 @property
 def 		lowercase_  (     self					:List[str]     )			->     Dict:







       '''simple docstring'''



       return self.__heap[1:]






 def __len__(     self					:List[Any]     )			->     List[Any]:







       '''simple docstring'''



       return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 a__						:				Dict           =    BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(1_0)
 binary_heap.insert(1_5)
 binary_heap.insert(1_2)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))








'''simple docstring'''





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      Tuple  			=   PreTrainedTokenizerFast
 UpperCAmelCase__      :      Tuple  			=   False
 UpperCAmelCase__      :      str  			=   True
 UpperCAmelCase__      :      str  			=   'tokenizer_file'






 def 		lowercase_  (     self					:Union[str, Any]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     False  # because we don't have pretrained_vocab_files_map
       super().setUp()
       __A    		=     True

       __A    		=     ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
       __A    		=     'SaulLu/dummy-tokenizer-bytelevel-bpe'

       # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
       __A    		=     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

       __A    		=     PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
       tokenizer.save_pretrained(self.tmpdirname     )






 def 		lowercase_  (     self					:Any     )			->     List[str]:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Optional[Any]     )			->     Optional[Any]:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     Tuple:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     str:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer_with_special_tokens_change()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:Optional[Any]     )			->     Any:







       '''simple docstring'''



       __A    		=     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

       __A    		=     ('a' for _ in range(1_000     ))
       __A    		=     tokenizer.train_new_from_iterator(text_iterator=_A		,			length=1_000		,			vocab_size=50     )

       __A    		=     new_tokenizer.encode('a🤗'     )
       self.assertEqual(_A		,			[64, 172, 253, 97, 245]     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:Tuple     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     AutoTokenizer.from_pretrained('bert-base-cased'     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       __A    		=     len(_A     )

       with tempfile.TemporaryDirectory() as tmp_dir:
             # Hack to save this in the tokenizer_config.json
             __A    		=     ['tokenizer.4.0.0.json']
             tokenizer.save_pretrained(_A     )
             json.dump(_A		,			open(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			'w'     )     )

             # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     ) + 1     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

             # Will need to be adjusted if we reach v42 and this test is still here.
             # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
             shutil.move(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			os.path.join(_A		,			'tokenizer.42.0.0.json'     )     )
             __A    		=     ['tokenizer.42.0.0.json']
             tokenizer.save_pretrained(_A     )
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     )     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )






 def 		lowercase_  (     self					:List[Any]     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     'hf-internal-testing/test-two-tokenizers'

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       __A    		=     AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_997     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

       # Testing an older version by monkey-patching the version in the module it's used.
       import transformers as old_transformers

       __A    		=     '3.0.0'
       __A    		=     old_transformers.models.auto.AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_996     )
       __A    		=     json.loads(old_tokenizer._tokenizer.to_str()     )
       self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:List[str]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'     )
       __A    		=     'The Matrix is a 1999 science fiction action film.'

       with concurrent.futures.ThreadPoolExecutor() as executor:
             __A    		=     [executor.submit(self.fetch		,			_A		,			_A     ) for i in range(10     )]
             __A    		=     [future.result() for future in futures]
             self.assertEqual(_A		,			[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10     )]     )






 def 		lowercase_  (     self					:Optional[int]		,			_A					:str		,			_A					:Any     )			->     Optional[int]:







       '''simple docstring'''



       return tokenizer.encode(_A		,			truncation='longest_first'		,			padding='longest'     )






