


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							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = DebertaTokenizer
							_lowercase	:			Any        = True
							_lowercase	:			Union[str, Any]        = DebertaTokenizerFast

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Optional[Any]:





									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									lowercase__            =      [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''[UNK]''',
									]
									lowercase__            =      dict(zip(UpperCamelCase_    ,						range(len(UpperCamelCase_							)							)							)							)
									lowercase__            =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									lowercase__            =      {'''unk_token''': '''[UNK]'''}

									lowercase__            =      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['''vocab_file''']							)
									lowercase__            =      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       lowerCamelCase_ (							self:	Union[str, Any]    ,						**UpperCamelCase_:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	int							)			->   Tuple:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Any:





									"""simple docstring"""







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

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

							def       lowerCamelCase_ (							self:	List[Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.get_tokenizer()
									lowercase__            =      tokenizer('''Hello'''    ,						'''World'''							)
									lowercase__            =      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd['''token_type_ids''']    ,						UpperCamelCase_							)

							@slow
							def       lowerCamelCase_ (							self:	int							)			->   List[str]:





									"""simple docstring"""







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

									lowercase__            =      tokenizer.encode('''sequence builders'''    ,						add_special_tokens=UpperCamelCase_							)
									lowercase__            =      tokenizer.encode('''multi-sequence build'''    ,						add_special_tokens=UpperCamelCase_							)

									lowercase__            =      tokenizer.encode(
									    '''sequence builders'''    ,						add_special_tokens=UpperCamelCase_    ,						add_prefix_space=UpperCamelCase_							)
									lowercase__            =      tokenizer.encode(
									    '''sequence builders'''    ,						'''multi-sequence build'''    ,						add_special_tokens=UpperCamelCase_    ,						add_prefix_space=UpperCamelCase_							)

									lowercase__            =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase_							)
									lowercase__            =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,						UpperCamelCase_							)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode


							@slow
							def       lowerCamelCase_ (							self:	str							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      [self.tokenizer_class]
									if self.test_rust_tokenizer:
											tokenizer_classes.append(self.rust_tokenizer_class							)

									for tokenizer_class in tokenizer_classes:
											lowercase__            =      tokenizer_class.from_pretrained('''microsoft/deberta-base'''							)

											lowercase__            =      [
											    '''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.''',
											]

											lowercase__            =      tokenizer(UpperCamelCase_    ,						padding=UpperCamelCase_							)
											lowercase__            =      [tokenizer.decode(UpperCamelCase_    ,						skip_special_tokens=UpperCamelCase_							) for seq in encoding['''input_ids''']]

											# fmt: off
											lowercase__            =      {
											    '''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

											lowercase__            =      [
											    '''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_							)






from __future__ import annotations


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      text, pattern
									lowercase__     ,		lowercase__            =      len(UpperCamelCase_							), len(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if char == self.pattern[i]:
													return i
									return -1

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if self.pattern[i] != self.text[current_pos + i]:
													return current_pos + i
									return -1


							def       lowerCamelCase_ (							self:	List[Any]							)			->   list[int]:





									"""simple docstring"""







									lowercase__            =      []
									for i in range(self.textLen - self.patLen + 1							):
											lowercase__            =      self.mismatch_in_text(UpperCamelCase_							)
											if mismatch_index == -1:
													positions.append(UpperCamelCase_							)
											else:
													lowercase__            =      self.match_in_pattern(self.text[mismatch_index]							)
													lowercase__            =      (
													    mismatch_index - match_index
													)  # shifting index lgtm [py/multiple-definition]
									return positions
lowerCAmelCase       =							'ABAABA'
lowerCAmelCase       =							'AB'
lowerCAmelCase       =							BoyerMooreSearch(text, pattern)
lowerCAmelCase       =							bms.bad_character_heuristic()

if len(positions) == 0:
			print('No match found')
else:
			print('Pattern found in following positions: ')
			print(positions)


