
"""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 						lowercase(_lowercase						,	unittest.TestCase					):
     __snake_case:  Optional[Any]  				=    BartTokenizer
     __snake_case:  str  				=    BartTokenizerFast
     __snake_case:  List[Any]  				=    True
     __snake_case:  Tuple  				=    filter_roberta_detectors



     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



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

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



     def  lowercase__     (       self    ,							**__SCREAMING_SNAKE_CASE      )		->    str:







         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,							**__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							**__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,							**__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         return "lower newer", "lower newer"



     @cached_property
     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         return BartTokenizer.from_pretrained('facebook/bart-large'      )



     @cached_property
     def  lowercase__     (       self      )		->    Optional[Any]:







         """simple docstring"""



         return BartTokenizerFast.from_pretrained('facebook/bart-large'      )



     @require_torch
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         a__    = [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]:
             a__    = tokenizer(__SCREAMING_SNAKE_CASE    ,							max_length=len(__SCREAMING_SNAKE_CASE      )    ,							padding=__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'      )
             self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )

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



     @require_torch
     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



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



     @require_torch
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = [
             'Summary of the text.',
             'Another summary.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a__    = tokenizer(text_target=__SCREAMING_SNAKE_CASE    ,							max_length=3_2    ,							padding='max_length'    ,							return_tensors='pt'      )
             self.assertEqual(3_2    ,							targets['input_ids'].shape[1]      )



     @require_torch
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a__    = tokenizer(
                 ['I am a small frog' * 1_0_2_4, 'I am a small frog']    ,							padding=__SCREAMING_SNAKE_CASE    ,							truncation=__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'      )
             self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             self.assertEqual(batch.input_ids.shape    ,							(2, 1_0_2_4)      )



     @require_torch
     def  lowercase__     (       self      )		->    Tuple:







         """simple docstring"""



         a__    = ['A long paragraph for summarization.']
         a__    = [
             'Summary of the text.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a__    = tokenizer(__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'      )
             a__    = tokenizer(text_target=__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'      )
             a__    = inputs['input_ids']
             a__    = 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  lowercase__     (       self      )		->    str:







         """simple docstring"""



         pass




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







         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                 a__    = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE      )
                 a__    = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE      )
                 a__    = 'A, <mask> AllenNLP sentence.'
                 a__    = tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE    ,							add_special_tokens=__SCREAMING_SNAKE_CASE    ,							return_token_type_ids=__SCREAMING_SNAKE_CASE      )
                 a__    = tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE    ,							add_special_tokens=__SCREAMING_SNAKE_CASE    ,							return_token_type_ids=__SCREAMING_SNAKE_CASE      )

                 # 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']      )    ,							)

                 a__    = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
                 a__    = 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(
                     __SCREAMING_SNAKE_CASE    ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
                 self.assertSequenceEqual(
                     __SCREAMING_SNAKE_CASE    ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )




"""simple docstring"""




import math







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = len(__SCREAMING_SNAKE_CASE      )
         a__    = [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1    ,							0    ,							self.N - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[Any]:







         """simple docstring"""



         return idx * 2



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         return idx * 2 + 1



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:  # noqa: E741







         """simple docstring"""



         if l == r:
             a__    = A[l]
         else:
             a__    = (l + r) // 2
             self.build(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             self.build(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE      )
             a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         return self.update_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return True
         if l == r:
             a__    = val
             return True
         a__    = (l + r) // 2
         self.update_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.update_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )
         return True



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         return self.query_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Any:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return -math.inf
         if l >= a and r <= b:
             return self.st[idx]
         a__    = (l + r) // 2
         a__    = self.query_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self.query_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         return max(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )




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







         """simple docstring"""



         a__    = []
         for i in range(1    ,							N + 1      ):
             show_list += [self.query(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )]
         print(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
    a		:					Union[str, Any]    	=					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    a		:					Dict    	=					15
    a		:					Tuple    	=					SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 1_11)
    print(segt.query(1, 15))
    segt.update(7, 8, 2_35)
    segt.show_data()


