



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   ( _UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  str      =			DebertaTokenizer
			lowercase__      :  Any      =			True
			lowercase__      :  Union[str, Any]      =			DebertaTokenizerFast





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__lowercase					=				[
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """[UNK]""",
									]
									__lowercase					=				dict(zip(lowercase			,     range(len(lowercase   )   )   )   )
									__lowercase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									__lowercase					=				{"""unk_token""": """[UNK]"""}

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





			def 		snake_case__			(				self	:       int			,     **lowercase	:       int   )						->					str:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Any			,     lowercase	:       str   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				"""lower newer"""
									__lowercase					=				"""lower newer"""
									return input_text, output_text





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									__lowercase					=				self.get_tokenizer()
									__lowercase					=				"""lower newer"""
									__lowercase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
									__lowercase					=				tokenizer.tokenize(lowercase   )
									self.assertListEqual(lowercase			,     lowercase   )

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





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					int:
									"""simple docstring"""







									__lowercase					=				self.get_tokenizer()
									__lowercase					=				tokenizer("""Hello"""			,     """World"""   )
									__lowercase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd["""token_type_ids"""]			,     lowercase   )





			@slow
			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

									__lowercase					=				tokenizer.encode("""sequence builders"""			,     add_special_tokens=lowercase   )
									__lowercase					=				tokenizer.encode("""multi-sequence build"""			,     add_special_tokens=lowercase   )

									__lowercase					=				tokenizer.encode(
									    """sequence builders"""			,     add_special_tokens=lowercase			,     add_prefix_space=lowercase   )
									__lowercase					=				tokenizer.encode(
									    """sequence builders"""			,     """multi-sequence build"""			,     add_special_tokens=lowercase			,     add_prefix_space=lowercase   )

									__lowercase					=				tokenizer.build_inputs_with_special_tokens(lowercase   )
									__lowercase					=				tokenizer.build_inputs_with_special_tokens(lowercase			,     lowercase   )

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







			@slow
			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				[self.tokenizer_class]
									if self.test_rust_tokenizer:
															tokenizer_classes.append(self.rust_tokenizer_class   )

									for tokenizer_class in tokenizer_classes:
															__lowercase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

															__lowercase					=				[
															    """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.""",
															]

															__lowercase					=				tokenizer(lowercase			,     padding=lowercase   )
															__lowercase					=				[tokenizer.decode(lowercase			,     skip_special_tokens=lowercase   ) for seq in encoding["""input_ids"""]]

															# fmt: off
															__lowercase					=				{
															    """input_ids""": [
															        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 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_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 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, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 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

															__lowercase					=				[
															    """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			,     lowercase   )

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










def  UpperCAmelCase__    (    lowercase__ = 100   ) ->      int:
						__lowercase					=				n * (n + 1) * (2 * n + 1) / 6
						__lowercase					=				(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares   )


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





