





'''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 SCREAMING_SNAKE_CASE_ (					lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    DebertaTokenizer
					A__				 =    True
					A__				 =    DebertaTokenizerFast





					def 					__magic_name__ ( self	):
										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(_lowerCAmelCase	,  range(len(_lowerCAmelCase	)	)	)	)
										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(_lowerCAmelCase	) + "\n"	)
										with open(self.merges_file	,  "w"	,  encoding="utf-8"	) as fp:
															fp.write("\n".join(_lowerCAmelCase	)	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       "lower newer"
										return input_text, output_text





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										lowerCamelCase__       =       tokenizer.tokenize(_lowerCAmelCase	)
										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)

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





					def 					__magic_name__ ( self	):
										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"]	,  _lowerCAmelCase	)





					@slow
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tokenizer_class.from_pretrained("microsoft/deberta-base"	)

										lowerCamelCase__       =       tokenizer.encode("sequence builders"	,  add_special_tokens=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.encode("multi-sequence build"	,  add_special_tokens=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer.encode(
										    "sequence builders"	,  add_special_tokens=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.encode(
										    "sequence builders"	,  "multi-sequence build"	,  add_special_tokens=_lowerCAmelCase	,  add_prefix_space=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	,  _lowerCAmelCase	)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode






					@slow
					def 					__magic_name__ ( self	):
										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(_lowerCAmelCase	,  padding=_lowerCAmelCase	)
															lowerCamelCase__       =       [tokenizer.decode(_lowerCAmelCase	,  skip_special_tokens=_lowerCAmelCase	) for seq in encoding["input_ids"]]

															# fmt: off
															lowerCamelCase__       =       {
															    "input_ids": [
															        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 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, 1_1126, 565, 2_4536, 80, 4_3797, 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, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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	,  _lowerCAmelCase	)

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








def    __UpperCamelCase      (    a,     a,     a=False) ->Dict:

					if isinstance(a,     a) and isinstance(a,     a):
										lowerCamelCase__       =       len(set_a.intersection(a))

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
										else:
															lowerCamelCase__       =       len(set_a.union(a))

										return intersection / union

					if isinstance(a,     (list, tuple)) and isinstance(a,     (list, tuple)):
										lowerCamelCase__       =       [element for element in set_a if element in set_b]

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
															return len(a) / union
										else:
															lowerCamelCase__       =       set_a + [element for element in set_b if element not in set_a]
															return len(a) / len(a)

										return len(a) / len(a)
					return None


if __name__ == "__main__":
						A_												=     {"a", "b", "c", "d", "e"}
						A_												=     {"c", "d", "e", "f", "h", "i"}
						print(jaccard_similarity(set_a, set_b))

