






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				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  DebertaTokenizer
		_snake_case   :   Union[str, Any]							=  True
		_snake_case   :   str							=  DebertaTokenizerFast



		def  __UpperCAmelCase      (							self       )     ->							str:
							super().setUp()

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

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



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Union[str, Any]:
							kwargs.update(self.special_tokens_map       )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				Tuple						=						'lower newer'
							UpperCAmelCase_   :				str						=						'lower newer'
							return input_text, output_text



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Dict						=						self.get_tokenizer()
							UpperCAmelCase_   :				Union[str, Any]						=						'lower newer'
							UpperCAmelCase_   :				Optional[int]						=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							UpperCAmelCase_   :				Union[str, Any]						=						tokenizer.tokenize(_UpperCamelCase       )
							self.assertListEqual(_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						tokens + [tokenizer.unk_token]
							UpperCAmelCase_   :				Optional[int]						=						[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase       )    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Tuple						=						self.get_tokenizer()
							UpperCAmelCase_   :				Union[str, Any]						=						tokenizer('Hello'    ,		'World'       )
							UpperCAmelCase_   :				Optional[int]						=						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
							self.assertListEqual(tokd['token_type_ids']    ,		_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Optional[int]						=						self.tokenizer_class.from_pretrained('microsoft/deberta-base'       )

							UpperCAmelCase_   :				List[Any]						=						tokenizer.encode('sequence builders'    ,		add_special_tokens=_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						tokenizer.encode('multi-sequence build'    ,		add_special_tokens=_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						tokenizer.encode(
							    'sequence builders'    ,		add_special_tokens=_UpperCamelCase    ,		add_prefix_space=_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						tokenizer.encode(
							    'sequence builders'    ,		'multi-sequence build'    ,		add_special_tokens=_UpperCamelCase    ,		add_prefix_space=_UpperCamelCase       )

							UpperCAmelCase_   :				Union[str, Any]						=						tokenizer.build_inputs_with_special_tokens(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						tokenizer.build_inputs_with_special_tokens(_UpperCamelCase    ,		_UpperCamelCase       )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Optional[Any]						=						[self.tokenizer_class]
							if self.test_rust_tokenizer:
												tokenizer_classes.append(self.rust_tokenizer_class       )

							for tokenizer_class in tokenizer_classes:
												UpperCAmelCase_   :				List[str]						=						tokenizer_class.from_pretrained('microsoft/deberta-base'       )

												UpperCAmelCase_   :				str						=						[
												    '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_   :				List[str]						=						tokenizer(_UpperCamelCase    ,		padding=_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						[tokenizer.decode(_UpperCamelCase    ,		skip_special_tokens=_UpperCamelCase       ) for seq in encoding['input_ids']]

												# fmt: off
												UpperCAmelCase_   :				Tuple						=						{
												    'input_ids': [
												        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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_   :				Tuple						=						[
												    '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    ,		_UpperCamelCase       )

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














from collections import defaultdict
from math import ceil, sqrt




def 					lowercase__				(					__snake_case   :							int = 1_000_000					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				defaultdict						=						defaultdict(__snake_case  )

					for outer_width in range(3					,	(t_limit // 4) + 2  ):
										if outer_width * outer_width > t_limit:
															UpperCAmelCase_   :				Union[str, Any]						=						max(
															    ceil(sqrt(outer_width * outer_width - t_limit  )  )					,	1  )
										else:
															UpperCAmelCase_   :				int						=						1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(__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() = }')






