



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        snake_case__    (__SCREAMING_SNAKE_CASE ,     unittest.TestCase		):







				"""simple docstring"""




				__lowerCAmelCase					:List[Any]      =					DebertaTokenizer
				__lowerCAmelCase					:List[Any]      =					True
				__lowerCAmelCase					:Union[str, Any]      =					DebertaTokenizerFast

				def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
							"""simple docstring"""




							super().setUp()

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

							a__       :						List[str]							=  os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""]  )
							a__       :						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 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				Dict:
							"""simple docstring"""




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

				def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
							"""simple docstring"""




							a__       :						Tuple							=  '''lower newer'''
							a__       :						Optional[Any]							=  '''lower newer'''
							return input_text, output_text

				def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
							"""simple docstring"""




							a__       :						Dict							=  self.get_tokenizer()
							a__       :						Optional[Any]							=  '''lower newer'''
							a__       :						Tuple							=  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							a__       :						Optional[Any]							=  tokenizer.tokenize(UpperCamelCase__  )
							self.assertListEqual(UpperCamelCase__ ,       UpperCamelCase__  )

							a__       :						Union[str, Any]							=  tokens + [tokenizer.unk_token]
							a__       :						List[str]							=  [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__  ) ,       UpperCamelCase__  )

				def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
							"""simple docstring"""




							a__       :						int							=  self.get_tokenizer()
							a__       :						Optional[int]							=  tokenizer("""Hello""" ,       """World"""  )
							a__       :						Optional[Any]							=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
							self.assertListEqual(tokd["""token_type_ids"""] ,       UpperCamelCase__  )

				@slow
				def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
							"""simple docstring"""




							a__       :						Optional[int]							=  self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""  )

							a__       :						Tuple							=  tokenizer.encode("""sequence builders""" ,       add_special_tokens=UpperCamelCase__  )
							a__       :						List[Any]							=  tokenizer.encode("""multi-sequence build""" ,       add_special_tokens=UpperCamelCase__  )

							a__       :						Dict							=  tokenizer.encode(
							    """sequence builders""" ,       add_special_tokens=UpperCamelCase__ ,       add_prefix_space=UpperCamelCase__  )
							a__       :						Optional[int]							=  tokenizer.encode(
							    """sequence builders""" ,       """multi-sequence build""" ,       add_special_tokens=UpperCamelCase__ ,       add_prefix_space=UpperCamelCase__  )

							a__       :						List[Any]							=  tokenizer.build_inputs_with_special_tokens(UpperCamelCase__  )
							a__       :						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 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
							"""simple docstring"""




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

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

										a__       :						Optional[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.''',
										]

										a__       :						Optional[Any]							=  tokenizer(UpperCamelCase__ ,       padding=UpperCamelCase__  )
										a__       :						List[str]							=  [tokenizer.decode(UpperCamelCase__ ,       skip_special_tokens=UpperCamelCase__  ) for seq in encoding['''input_ids''']]

										# fmt: off
										a__       :						Optional[int]							=  {
										    '''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

										a__       :						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__  )









from string import ascii_uppercase

_lowercase		:				str      		={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase		:				Dict      		=dict(enumerate(ascii_uppercase))

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Any							=  len(_lowercase)
			a__       :						Optional[int]							=  0
			while True:
						if x == i:
									a__       :						Optional[Any]							=  0
						if len(_lowercase) == len(_lowercase):
									break
						key += key[i]
						i += 1
			return key

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Tuple							=  """"""
			a__       :						str							=  0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									a__       :						List[str]							=  (dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						int							=  """"""
			a__       :						int							=  0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									a__       :						Dict							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						List[Any]							=  """THE GERMAN ATTACK"""
			a__       :						List[Any]							=  """SECRET"""
			a__       :						Tuple							=  generate_key(_lowercase						,       _lowercase)
			a__       :						str							=  cipher_text(_lowercase						,       _lowercase)
			print(F'''Encrypted Text = {s}''')
			print(F'''Original Text = {original_text(_lowercase						,       _lowercase)}''')


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




