

'''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__      (      _snake_case						,						unittest.TestCase			):
    lowercase						=		DebertaTokenizer
    lowercase						=		True
    lowercase						=		DebertaTokenizerFast



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           super().setUp()

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

           A_						=      os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["""vocab_file"""]       )
           A_						=      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 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Any:





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



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           A_						=      """lower newer"""
           A_						=      """lower newer"""
           return input_text, output_text



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.get_tokenizer()
           A_						=      """lower newer"""
           A_						=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           A_						=      tokenizer.tokenize(UpperCamelCase__       )
           self.assertListEqual(UpperCamelCase__		,				UpperCamelCase__       )

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



    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           A_						=      self.get_tokenizer()
           A_						=      tokenizer("""Hello"""		,				"""World"""       )
           A_						=      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
           self.assertListEqual(tokd["""token_type_ids"""]		,				UpperCamelCase__       )



    @slow
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""       )

           A_						=      tokenizer.encode("""sequence builders"""		,				add_special_tokens=UpperCamelCase__       )
           A_						=      tokenizer.encode("""multi-sequence build"""		,				add_special_tokens=UpperCamelCase__       )

           A_						=      tokenizer.encode(
               """sequence builders"""		,				add_special_tokens=UpperCamelCase__		,				add_prefix_space=UpperCamelCase__       )
           A_						=      tokenizer.encode(
               """sequence builders"""		,				"""multi-sequence build"""		,				add_special_tokens=UpperCamelCase__		,				add_prefix_space=UpperCamelCase__       )

           A_						=      tokenizer.build_inputs_with_special_tokens(UpperCamelCase__       )
           A_						=      tokenizer.build_inputs_with_special_tokens(UpperCamelCase__		,				UpperCamelCase__       )

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode







    @slow
    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      [self.tokenizer_class]
           if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class       )

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

                  A_						=      [
                      """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_						=      tokenizer(UpperCamelCase__		,				padding=UpperCamelCase__       )
                  A_						=      [tokenizer.decode(UpperCamelCase__		,				skip_special_tokens=UpperCamelCase__       ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  A_						=      {
                      """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

                  A_						=      [
                      """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__       )



'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      0.01
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     A_						=      time.time()
                     locka.acquire(UpperCAmelCase__				)
                     assert time.time() - _start > timeout







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
       A_						=      """a""" * 10_00 + """.lock"""
       A_						=      FileLock(str(tmpdir / filename				)				)
       assert locka._lock_file.endswith(""".lock"""				)
       assert not locka._lock_file.endswith(UpperCAmelCase__				)
       assert len(os.path.basename(locka._lock_file				)				) <= 2_55
       A_						=      FileLock(tmpdir / filename				)
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     locka.acquire(0				)
