





"""simple docstring"""





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class       snake_case		(       __UpperCAmelCase			,					unittest.TestCase       ):
      lowerCamelCase__      =			BartTokenizer
      lowerCamelCase__      =			BartTokenizerFast
      lowerCamelCase__      =			True
      lowerCamelCase__      =			filter_roberta_detectors

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       super().setUp()
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       __SCREAMING_SNAKE_CASE			:      int 							=	dict(zip(_lowerCamelCase						,							range(len(_lowerCamelCase						)						)						)						)
       __SCREAMING_SNAKE_CASE			:      int 							=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       __SCREAMING_SNAKE_CASE			:      int 							=	{'''unk_token''': '''<unk>'''}

       __SCREAMING_SNAKE_CASE			:      int 							=	os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['''vocab_file''']						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['''merges_file''']						)
       with open(self.vocab_file						,							'''w'''						,							encoding='''utf-8'''						) as fp:
        fp.write(json.dumps(_lowerCamelCase						) + '''\n'''						)
       with open(self.merges_file						,							'''w'''						,							encoding='''utf-8'''						) as fp:
        fp.write('''\n'''.join(_lowerCamelCase						)						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							**_lowerCamelCase       :Union[str, Any]						):
       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,							**_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						,							**_lowerCamelCase       :Tuple						):
       kwargs.update(self.special_tokens_map						)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,							**_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :str						):
       return "lower newer", "lower newer"

      @cached_property
      def 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       return BartTokenizer.from_pretrained('''facebook/bart-large'''						)

      @cached_property
      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						):
       return BartTokenizerFast.from_pretrained('''facebook/bart-large'''						)

      @require_torch
      def 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       __SCREAMING_SNAKE_CASE			:      int 							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __SCREAMING_SNAKE_CASE			:      str 							=	tokenizer(_lowerCamelCase						,							max_length=len(_lowerCamelCase						)						,							padding=_lowerCamelCase						,							return_tensors='''pt'''						)
        self.assertIsInstance(_lowerCamelCase						,							_lowerCamelCase						)

        self.assertEqual((2, 9)						,							batch.input_ids.shape						)
        self.assertEqual((2, 9)						,							batch.attention_mask.shape						)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	batch.input_ids.tolist()[0]
        self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)
        # Test that special tokens are reset

      @require_torch
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __SCREAMING_SNAKE_CASE			:      int 							=	tokenizer(_lowerCamelCase						,							padding=_lowerCamelCase						,							return_tensors='''pt'''						)
        # check if input_ids are returned and no labels
        self.assertIn('''input_ids'''						,							_lowerCamelCase						)
        self.assertIn('''attention_mask'''						,							_lowerCamelCase						)
        self.assertNotIn('''labels'''						,							_lowerCamelCase						)
        self.assertNotIn('''decoder_attention_mask'''						,							_lowerCamelCase						)

      @require_torch
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	tokenizer(text_target=_lowerCamelCase						,							max_length=3_2						,							padding='''max_length'''						,							return_tensors='''pt'''						)
        self.assertEqual(3_2						,							targets['''input_ids'''].shape[1]						)

      @require_torch
      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	tokenizer(
            ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']						,							padding=_lowerCamelCase						,							truncation=_lowerCamelCase						,							return_tensors='''pt'''						)
        self.assertIsInstance(_lowerCamelCase						,							_lowerCamelCase						)
        self.assertEqual(batch.input_ids.shape						,							(2, 1_0_2_4)						)

      @require_torch
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	['''A long paragraph for summarization.''']
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	[
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	tokenizer(_lowerCamelCase						,							return_tensors='''pt'''						)
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tokenizer(text_target=_lowerCamelCase						,							return_tensors='''pt'''						)
        __SCREAMING_SNAKE_CASE			:      int 							=	inputs['''input_ids''']
        __SCREAMING_SNAKE_CASE			:      str 							=	targets['''input_ids''']
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       pass



      def 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.rust_tokenizer_class.from_pretrained(_lowerCamelCase						,							**_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.tokenizer_class.from_pretrained(_lowerCamelCase						,							**_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE			:      str 							=	'''A, <mask> AllenNLP sentence.'''
         __SCREAMING_SNAKE_CASE			:      str 							=	tokenizer_r.encode_plus(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase						,							return_token_type_ids=_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE			:      str 							=	tokenizer_p.encode_plus(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase						,							return_token_type_ids=_lowerCamelCase						)

         # token_type_ids should put 0 everywhere
         self.assertEqual(sum(tokens_r['''token_type_ids''']						)						,							sum(tokens_p['''token_type_ids''']						)						)

         # attention_mask should put 1 everywhere, so sum over length should be 1
         self.assertEqual(
             sum(tokens_r['''attention_mask''']						) / len(tokens_r['''attention_mask''']						)						,							sum(tokens_p['''attention_mask''']						) / len(tokens_p['''attention_mask''']						)						,							)

         __SCREAMING_SNAKE_CASE			:      Tuple 							=	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']						)
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']						)

         # Rust correctly handles the space before the mask while python doesnt
         self.assertSequenceEqual(tokens_p['''input_ids''']						,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)
         self.assertSequenceEqual(tokens_r['''input_ids''']						,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)

         self.assertSequenceEqual(
             _lowerCamelCase						,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']						)
         self.assertSequenceEqual(
             _lowerCamelCase						,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']						)








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Any			,				lowercase_       :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	index
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	2 * index + 1
 __SCREAMING_SNAKE_CASE			:      Any 							=	2 * index + 2
 if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      str 							=	left_index

 if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	right_index

 if largest != index:
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[str] 							=	unsorted[index], unsorted[largest]
  heapify(lowercase_			,				lowercase_			,				lowercase_		)




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	len(lowercase_		)
 for i in range(n // 2 - 1			,				-1			,				-1		):
  heapify(lowercase_			,				lowercase_			,				lowercase_		)
 for i in range(n - 1			,				0			,				-1		):
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	unsorted[i], unsorted[0]
  heapify(lowercase_			,				0			,				lowercase_		)
 return unsorted


if __name__ == "__main__":
    _lowerCamelCase  					=     input('''Enter numbers separated by a comma:\n''').strip()
    _lowerCamelCase  					=     [int(item) for item in user_input.split(''',''')]
    print(heap_sort(unsorted))

