





"""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 			__UpperCamelCase							(		snake_case_						,			unittest.TestCase		):
							lowerCamelCase  :   List[str]    =DebertaTokenizer
							lowerCamelCase  :   Union[str, Any]    =True
							lowerCamelCase  :   Union[str, Any]    =DebertaTokenizerFast






							def        __a  (  self )      ->					List[Any]:
								super().setUp()

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

								a :						int      =							os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES["vocab_file"] )
								a :						Any      =							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        __a  (  self ,   **lowerCAmelCase__ )      ->					Dict:
								kwargs.update(self.special_tokens_map )
								return self.tokenizer_class.from_pretrained(self.tmpdirname ,   **__snake_case )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Optional[Any]:
								a :						Optional[Any]      =							"lower newer"
								a :						Optional[int]      =							"lower newer"
								return input_text, output_text






							def        __a  (  self )      ->					List[str]:
								a :						List[str]      =							self.get_tokenizer()
								a :						Dict      =							"lower newer"
								a :						List[Any]      =							["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
								a :						List[Any]      =							tokenizer.tokenize(__snake_case )
								self.assertListEqual(__snake_case ,   __snake_case )

								a :						int      =							tokens + [tokenizer.unk_token]
								a :						Tuple      =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) ,   __snake_case )






							def        __a  (  self )      ->					Tuple:
								a :						Optional[int]      =							self.get_tokenizer()
								a :						int      =							tokenizer("Hello" ,   "World" )
								a :						Optional[int]      =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
								self.assertListEqual(tokd["token_type_ids"] ,   __snake_case )






							@slow
							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[Any]      =							self.tokenizer_class.from_pretrained("microsoft/deberta-base" )

								a :						List[Any]      =							tokenizer.encode("sequence builders" ,   add_special_tokens=__snake_case )
								a :						str      =							tokenizer.encode("multi-sequence build" ,   add_special_tokens=__snake_case )

								a :						str      =							tokenizer.encode(
								    "sequence builders" ,   add_special_tokens=__snake_case ,   add_prefix_space=__snake_case )
								a :						List[str]      =							tokenizer.encode(
								    "sequence builders" ,   "multi-sequence build" ,   add_special_tokens=__snake_case ,   add_prefix_space=__snake_case )

								a :						Dict      =							tokenizer.build_inputs_with_special_tokens(__snake_case )
								a :						Optional[int]      =							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        __a  (  self )      ->					str:
								a :						List[str]      =							[self.tokenizer_class]
								if self.test_rust_tokenizer:
									tokenizer_classes.append(self.rust_tokenizer_class )

								for tokenizer_class in tokenizer_classes:
									a :						List[str]      =							tokenizer_class.from_pretrained("microsoft/deberta-base" )

									a :						Dict      =							[
									    "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 :						List[str]      =							tokenizer(__snake_case ,   padding=__snake_case )
									a :						List[str]      =							[tokenizer.decode(__snake_case ,   skip_special_tokens=__snake_case ) for seq in encoding["input_ids"]]

									# fmt: off
									a :						Tuple      =							{
									    "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

									a :						Dict      =							[
									    "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				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				""""""
      for word_or_phrase in separated:
            if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
                  raise Exception("""join() accepts only strings to be joined"""    )
            joined += word_or_phrase + separator
      return joined.strip(lowerCAmelCase    )


if __name__ == "__main__":
      from doctest import testmod

      testmod()






