


"""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    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  DebertaTokenizer
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  DebertaTokenizerFast


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     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(__A			,   range(len(__A						)						)						)						)
     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(__A						) + '''\n'''						)
     with open(self.merges_file			,   '''w'''			,   encoding='''utf-8'''						) as fp:
         fp.write('''\n'''.join(__A						)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[int]:
     kwargs.update(self.special_tokens_map						)
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Union[str, Any]:
     a					='''lower newer'''
     a					='''lower newer'''
     return input_text, output_text


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=self.get_tokenizer()
     a					='''lower newer'''
     a					=['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
     a					=tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   __A						)

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


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


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

     a					=tokenizer.encode('''sequence builders'''			,   add_special_tokens=__A						)
     a					=tokenizer.encode('''multi-sequence build'''			,   add_special_tokens=__A						)

     a					=tokenizer.encode(
         '''sequence builders'''			,   add_special_tokens=__A			,   add_prefix_space=__A						)
     a					=tokenizer.encode(
         '''sequence builders'''			,   '''multi-sequence build'''			,   add_special_tokens=__A			,   add_prefix_space=__A						)

     a					=tokenizer.build_inputs_with_special_tokens(__A						)
     a					=tokenizer.build_inputs_with_special_tokens(__A			,   __A						)

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode





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

         # fmt: off
         a					={
             '''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					=[
             '''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			,   __A						)

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



"""simple docstring"""







from __future__ import annotations
def _A						(						lowercase	,   lowercase	,   lowercase	,   ):







    """simple docstring"""
    if (electron_conc, hole_conc, intrinsic_conc).count(0		) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''		)
    elif electron_conc < 0:
        raise ValueError('''Electron concentration cannot be negative in a semiconductor'''		)
    elif hole_conc < 0:
        raise ValueError('''Hole concentration cannot be negative in a semiconductor'''		)
    elif intrinsic_conc < 0:
        raise ValueError(
            '''Intrinsic concentration cannot be negative in a semiconductor'''		)
    elif electron_conc == 0:
        return (
            "electron_conc",
            intrinsic_conc**2 / hole_conc,
        )
    elif hole_conc == 0:
        return (
            "hole_conc",
            intrinsic_conc**2 / electron_conc,
        )
    elif intrinsic_conc == 0:
        return (
            "intrinsic_conc",
            (electron_conc * hole_conc) ** 0.5,
        )
    else:
        return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()