






"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=DebertaTokenizer
   a      :				Optional[int] 						=True
   a      :				Optional[Any] 						=DebertaTokenizerFast

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCamelCase__         =    [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '[UNK]',
        ]
        UpperCamelCase__         =    dict(zip(snake_case_ ,				range(len(snake_case_      )      )      )      )
        UpperCamelCase__         =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        UpperCamelCase__         =    {'unk_token': '[UNK]'}

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       List[str]:
        kwargs.update(self.special_tokens_map      )
        return self.tokenizer_class.from_pretrained(self.tmpdirname ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
        UpperCamelCase__         =    'lower newer'
        UpperCamelCase__         =    'lower newer'
        return input_text, output_text

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
        UpperCamelCase__         =    self.get_tokenizer()
        UpperCamelCase__         =    'lower newer'
        UpperCamelCase__         =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        UpperCamelCase__         =    tokenizer.tokenize(snake_case_      )
        self.assertListEqual(snake_case_ ,				snake_case_      )

        UpperCamelCase__         =    tokens + [tokenizer.unk_token]
        UpperCamelCase__         =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_      ) ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
        UpperCamelCase__         =    self.get_tokenizer()
        UpperCamelCase__         =    tokenizer('Hello' ,				'World'      )
        UpperCamelCase__         =    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids'] ,				snake_case_      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
        UpperCamelCase__         =    self.tokenizer_class.from_pretrained('microsoft/deberta-base'      )

        UpperCamelCase__         =    tokenizer.encode('sequence builders' ,				add_special_tokens=snake_case_      )
        UpperCamelCase__         =    tokenizer.encode('multi-sequence build' ,				add_special_tokens=snake_case_      )

        UpperCamelCase__         =    tokenizer.encode(
            'sequence builders' ,				add_special_tokens=snake_case_ ,				add_prefix_space=snake_case_      )
        UpperCamelCase__         =    tokenizer.encode(
            'sequence builders' ,				'multi-sequence build' ,				add_special_tokens=snake_case_ ,				add_prefix_space=snake_case_      )

        UpperCamelCase__         =    tokenizer.build_inputs_with_special_tokens(snake_case_      )
        UpperCamelCase__         =    tokenizer.build_inputs_with_special_tokens(snake_case_ ,				snake_case_      )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
        UpperCamelCase__         =    [self.tokenizer_class]
        if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class      )

        for tokenizer_class in tokenizer_classes:
             UpperCamelCase__         =    tokenizer_class.from_pretrained('microsoft/deberta-base'      )

             UpperCamelCase__         =    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             UpperCamelCase__         =    tokenizer(snake_case_ ,				padding=snake_case_      )
             UpperCamelCase__         =    [tokenizer.decode(snake_case_ ,				skip_special_tokens=snake_case_      ) for seq in encoding['input_ids']]

             # fmt: off
             UpperCamelCase__         =    {
                 'input_ids': [
                     [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
                 ],
                 'token_type_ids': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 'attention_mask': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             UpperCamelCase__         =    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             self.assertDictEqual(encoding.data ,				snake_case_      )

             for expected, decoded in zip(snake_case_ ,				snake_case_      ):
                  self.assertEqual(snake_case_ ,				snake_case_      )












"""simple docstring"""





from collections import defaultdict
from math import ceil, sqrt







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 1_00_00_00 ,	SCREAMING_SNAKE_CASE = 10					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    defaultdict(SCREAMING_SNAKE_CASE					)

    for outer_width in range(3 ,	(t_limit // 4) + 2					):
        if outer_width * outer_width > t_limit:
            UpperCamelCase__         =    max(
                ceil(sqrt(outer_width * outer_width - t_limit					)					) ,	1					)
        else:
            UpperCamelCase__         =    1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(SCREAMING_SNAKE_CASE ,	outer_width - 1 ,	2					):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10					)


if __name__ == "__main__":
  print(F"""{solution() = }""")




