"""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    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						int      =     DebertaTokenizer
					UpperCamelCase_			:						Tuple      =     True
					UpperCamelCase_			:						Tuple      =     DebertaTokenizerFast




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							super().setUp()

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

							lowerCAmelCase__   = os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
							lowerCAmelCase__   = 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    :  List[str]	,    **snake_case__    :  Union[str, Any]  ):
							kwargs.update(self.special_tokens_map  )
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  str  ):
							lowerCAmelCase__   = """lower newer"""
							lowerCAmelCase__   = """lower newer"""
							return input_text, output_text




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = self.get_tokenizer()
							lowerCAmelCase__   = """lower newer"""
							lowerCAmelCase__   = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
							lowerCAmelCase__   = tokenizer.tokenize(snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )

							lowerCAmelCase__   = tokens + [tokenizer.unk_token]
							lowerCAmelCase__   = [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    :  str  ):
							lowerCAmelCase__   = self.get_tokenizer()
							lowerCAmelCase__   = tokenizer("""Hello"""	,    """World"""  )
							lowerCAmelCase__   = [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    :  Dict  ):
							lowerCAmelCase__   = self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""  )

							lowerCAmelCase__   = tokenizer.encode("""sequence builders"""	,    add_special_tokens=snake_case__  )
							lowerCAmelCase__   = tokenizer.encode("""multi-sequence build"""	,    add_special_tokens=snake_case__  )

							lowerCAmelCase__   = tokenizer.encode(
							    """sequence builders"""	,    add_special_tokens=snake_case__	,    add_prefix_space=snake_case__  )
							lowerCAmelCase__   = tokenizer.encode(
							    """sequence builders"""	,    """multi-sequence build"""	,    add_special_tokens=snake_case__	,    add_prefix_space=snake_case__  )

							lowerCAmelCase__   = tokenizer.build_inputs_with_special_tokens(snake_case__  )
							lowerCAmelCase__   = 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    :  Optional[Any]  ):
							lowerCAmelCase__   = [self.tokenizer_class]
							if self.test_rust_tokenizer:
									tokenizer_classes.append(self.rust_tokenizer_class  )

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

									lowerCAmelCase__   = [
									    """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.""",
									]

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

									# fmt: off
									lowerCAmelCase__   = {
									    """input_ids""": [
									        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 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, 11126, 565, 24536, 80, 43797, 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, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

									lowerCAmelCase__   = [
									    """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"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




