




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_   (			__A		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				DebertaTokenizer
    UpperCAmelCase__							  =				True
    UpperCAmelCase__							  =				DebertaTokenizerFast


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Union[str, Any]:





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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			**UpperCAmelCase__				:     Dict)						->Dict:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     List[str])						->Optional[Any]:





           '''simple docstring'''

           A__							=   '''lower newer'''
           A__							=   '''lower newer'''
           return input_text, output_text


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.get_tokenizer()
           A__							=   '''lower newer'''
           A__							=   ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
           A__							=   tokenizer.tokenize(__lowercase)
           self.assertListEqual(__lowercase ,			__lowercase)

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Union[str, 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'''] ,			__lowercase)


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   self.tokenizer_class.from_pretrained('''microsoft/deberta-base''')

           A__							=   tokenizer.encode('''sequence builders''' ,			add_special_tokens=__lowercase)
           A__							=   tokenizer.encode('''multi-sequence build''' ,			add_special_tokens=__lowercase)

           A__							=   tokenizer.encode(
               '''sequence builders''' ,			add_special_tokens=__lowercase ,			add_prefix_space=__lowercase)
           A__							=   tokenizer.encode(
               '''sequence builders''' ,			'''multi-sequence build''' ,			add_special_tokens=__lowercase ,			add_prefix_space=__lowercase)

           A__							=   tokenizer.build_inputs_with_special_tokens(__lowercase)
           A__							=   tokenizer.build_inputs_with_special_tokens(__lowercase ,			__lowercase)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->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(__lowercase ,			padding=__lowercase)
                  A__							=   [tokenizer.decode(__lowercase ,			skip_special_tokens=__lowercase) for seq in encoding['''input_ids''']]

                  # fmt: off
                  A__							=   {
                      '''input_ids''': [
                          [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 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_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 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, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 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 ,			__lowercase)

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








_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609_344,
    "knot": 1.852,
}

_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 0.277_777_778,
    "mph": 0.621_371_192,
    "knot": 0.539_956_803,
}







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	float:



       """simple docstring"""

       if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
              A__							=   (
                  f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
                  f"""Valid values are: {", ".join(lowercase_						)}"""
              )
              raise ValueError(lowercase_						)
       return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]			,    3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


