






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    :    Dict       						=  DebertaTokenizer
		_snake_case    :    str       						=  True
		_snake_case    :    str       						=  DebertaTokenizerFast



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							super().setUp()

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

							UpperCAmelCase_    :      Tuple            =						os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES['vocab_file']   )
							UpperCAmelCase_    :      List[str]            =						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   )	->							Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname				,		**_UpperCamelCase   )



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



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

							UpperCAmelCase_    :      Any            =						tokens + [tokenizer.unk_token]
							UpperCAmelCase_    :      Tuple            =						[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   )	->							List[str]:
							UpperCAmelCase_    :      Tuple            =						self.get_tokenizer()
							UpperCAmelCase_    :      List[str]            =						tokenizer('Hello'				,		'World'   )
							UpperCAmelCase_    :      List[str]            =						[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[str]:
							UpperCAmelCase_    :      Union[str, Any]            =						self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

							UpperCAmelCase_    :      Dict            =						tokenizer.encode('sequence builders'				,		add_special_tokens=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						tokenizer.encode('multi-sequence build'				,		add_special_tokens=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						tokenizer.encode(
							    'sequence builders'				,		add_special_tokens=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						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_    :      Tuple            =						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   )	->							str:
							UpperCAmelCase_    :      List[str]            =						[self.tokenizer_class]
							if self.test_rust_tokenizer:
												tokenizer_classes.append(self.rust_tokenizer_class   )

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

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

												# fmt: off
												UpperCAmelCase_    :      Any            =						{
												    '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_    :      List[Any]            =						[
												    '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   )














def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if a < 0 or b < 0:
										raise ValueError('the value of both inputs must be positive'  )

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

					return "0b" + "".join(
					    str(int(char_a == '1' and char_b == '1'  )  )
					    for char_a, char_b in zip(a_binary.zfill(__snake_case  )     ,       b_binary.zfill(__snake_case  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






