"""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   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						DebertaTokenizer
				_lowerCAmelCase     :    List[str]			  =						True
				_lowerCAmelCase     :    List[str]			  =						DebertaTokenizerFast

				def 		snake_case (				self    ):






										"""simple docstring"""

										super().setUp()

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

										snake_case								=			os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']    )
										snake_case								=			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 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

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

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

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										snake_case								=			tokenizer.encode('sequence builders'		,    add_special_tokens=__lowerCAmelCase    )
										snake_case								=			tokenizer.encode('multi-sequence build'		,    add_special_tokens=__lowerCAmelCase    )

										snake_case								=			tokenizer.encode(
										    'sequence builders'		,    add_special_tokens=__lowerCAmelCase		,    add_prefix_space=__lowerCAmelCase    )
										snake_case								=			tokenizer.encode(
										    'sequence builders'		,    'multi-sequence build'		,    add_special_tokens=__lowerCAmelCase		,    add_prefix_space=__lowerCAmelCase    )

										snake_case								=			tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase    )
										snake_case								=			tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase		,    __lowerCAmelCase    )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

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

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

																snake_case								=			[
																    '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.',
																]

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

																# fmt: off
																snake_case								=			{
																    'input_ids': [
																        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

																snake_case								=			[
																    '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    )



"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			path_or_paths
										snake_case								=			split if split or isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else 'train'
										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass


