





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					(					lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		DebertaTokenizer
			lowerCAmelCase_							  =		True
			lowerCAmelCase_							  =		DebertaTokenizerFast





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase							) ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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							)










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	if days_between_payments <= 0:
		raise ValueError('''days_between_payments must be > 0'''							)
	if daily_interest_rate < 0:
		raise ValueError('''daily_interest_rate must be >= 0'''							)
	if principal <= 0:
		raise ValueError('''principal must be > 0'''							)
	return principal * daily_interest_rate * days_between_payments





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,)       ->   float:







	'''simple docstring'''







	if number_of_compounding_periods <= 0:
		raise ValueError('''number_of_compounding_periods must be > 0'''							)
	if nominal_annual_interest_rate_percentage < 0:
		raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0'''							)
	if principal <= 0:
		raise ValueError('''principal must be > 0'''							)

	return principal * (
	    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
	    - 1
	)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,)       ->   float:







	'''simple docstring'''







	if number_of_years <= 0:
		raise ValueError('''number_of_years must be > 0'''							)
	if nominal_annual_percentage_rate < 0:
		raise ValueError('''nominal_annual_percentage_rate must be >= 0'''							)
	if principal <= 0:
		raise ValueError('''principal must be > 0'''							)

	return compound_interest(
	    __snake_case ,nominal_annual_percentage_rate / 365 ,number_of_years * 365							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



