



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 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		DebertaTokenizer
					lowercase_								=		True
					lowercase_								=		DebertaTokenizerFast







					def 			snake_case					(							self :     Any					):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									lowercase__					:   Optional[int]													=					[
									    "l",
									    "o",
									    "w",
									    "e",
									    "r",
									    "s",
									    "t",
									    "i",
									    "d",
									    "n",
									    "\u0120",
									    "\u0120l",
									    "\u0120n",
									    "\u0120lo",
									    "\u0120low",
									    "er",
									    "\u0120lowest",
									    "\u0120newer",
									    "\u0120wider",
									    "[UNK]",
									]
									lowercase__					:   List[str]													=					dict(zip(SCREAMING_SNAKE_CASE				,       range(len(SCREAMING_SNAKE_CASE					)					)					)					)
									lowercase__					:   str													=					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
									lowercase__					:   Tuple													=					{"unk_token": "[UNK]"}

									lowercase__					:   Union[str, Any]													=					os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES["vocab_file"]					)
									lowercase__					:   Tuple													=					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(SCREAMING_SNAKE_CASE					) + "\n"					)
									with open(self.merges_file				,       "w"				,       encoding="utf-8"					) as fp:
													fp.write("\n".join(SCREAMING_SNAKE_CASE					)					)







					def 			snake_case					(							self :     Optional[Any]				,       **SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									kwargs.update(self.special_tokens_map					)
									return self.tokenizer_class.from_pretrained(self.tmpdirname				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[str]					):
									lowercase__					:   Tuple													=					"lower newer"
									lowercase__					:   Tuple													=					"lower newer"
									return input_text, output_text







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Tuple													=					self.get_tokenizer()
									lowercase__					:   str													=					"lower newer"
									lowercase__					:   Dict													=					["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
									lowercase__					:   Tuple													=					tokenizer.tokenize(SCREAMING_SNAKE_CASE					)
									self.assertListEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   List[Any]													=					tokens + [tokenizer.unk_token]
									lowercase__					:   Tuple													=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE					)				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					self.get_tokenizer()
									lowercase__					:   Dict													=					tokenizer("Hello"				,       "World"					)
									lowercase__					:   Optional[int]													=					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd["token_type_ids"]				,       SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     int					):
									lowercase__					:   Optional[int]													=					self.tokenizer_class.from_pretrained("microsoft/deberta-base"					)

									lowercase__					:   int													=					tokenizer.encode("sequence builders"				,       add_special_tokens=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					tokenizer.encode("multi-sequence build"				,       add_special_tokens=SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					tokenizer.encode(
									    "sequence builders"				,       add_special_tokens=SCREAMING_SNAKE_CASE				,       add_prefix_space=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					tokenizer.encode(
									    "sequence builders"				,       "multi-sequence build"				,       add_special_tokens=SCREAMING_SNAKE_CASE				,       add_prefix_space=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[int]													=					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE					)
									lowercase__					:   int													=					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







					@slow
					def 			snake_case					(							self :     str					):
									lowercase__					:   int													=					[self.tokenizer_class]
									if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class					)

									for tokenizer_class in tokenizer_classes:
													lowercase__					:   Optional[Any]													=					tokenizer_class.from_pretrained("microsoft/deberta-base"					)

													lowercase__					:   List[str]													=					[
													    "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__					:   Dict													=					tokenizer(SCREAMING_SNAKE_CASE				,       padding=SCREAMING_SNAKE_CASE					)
													lowercase__					:   List[Any]													=					[tokenizer.decode(SCREAMING_SNAKE_CASE				,       skip_special_tokens=SCREAMING_SNAKE_CASE					) for seq in encoding["input_ids"]]

													# fmt: off
													lowercase__					:   Dict													=					{
													    "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__					:   str													=					[
													    "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				,       SCREAMING_SNAKE_CASE					)

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









from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase__					  =  TypeVar('''KEY''')
lowerCAmelCase__					  =  TypeVar('''VAL''')

@dataclass(frozen=_UpperCamelCase    ,      slots=_UpperCamelCase      )
class 				snake_case__(Generic[KEY, VAL]      ):

					"""simple docstring"""






					lowercase_								=		42
					lowercase_								=		42






class 				snake_case__(_Item      ):

					"""simple docstring"""






					def __init__(							self :     List[str]					):
									super().__init__(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __bool__(							self :     Tuple					):
									return False






lowerCAmelCase__					  =  _DeletedItem()

class 				snake_case__(MutableMapping[KEY, VAL]      ):

					"""simple docstring"""






					def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     int = 8				,       SCREAMING_SNAKE_CASE :     float = 0.75					):
									lowercase__					:   Any													=					initial_block_size
									lowercase__					:   list[_Item | None]													=					[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase__					:   Dict													=					capacity_factor
									lowercase__					:   Optional[int]													=					0







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									return hash(SCREAMING_SNAKE_CASE					) % len(self._buckets					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     int					):
									return (ind + 1) % len(self._buckets					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									lowercase__					:   Tuple													=					self._buckets[ind]
									if not stored:
													lowercase__					:   int													=					_Item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													self._len += 1
													return True
									elif stored.key == key:
													lowercase__					:   str													=					_Item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													return True
									else:
													return False







					def 			snake_case					(							self :     str					):
									lowercase__					:   str													=					len(self._buckets					) * self._capacity_factor
									return len(self					) >= int(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]					):
									if len(self._buckets					) <= self._initial_block_size:
													return False
									lowercase__					:   Optional[Any]													=					len(self._buckets					) * self._capacity_factor / 2
									return len(self					) < limit







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Tuple													=					self._buckets
									lowercase__					:   Optional[int]													=					[None] * new_size
									lowercase__					:   int													=					0
									for item in old_buckets:
													if item:
																	self._add_item(item.key				,       item.val					)







					def 			snake_case					(							self :     int					):
									self._resize(len(self._buckets					) * 2					)







					def 			snake_case					(							self :     Optional[Any]					):
									self._resize(len(self._buckets					) // 2					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									lowercase__					:   Tuple													=					self._get_bucket_index(SCREAMING_SNAKE_CASE					)
									for _ in range(len(self._buckets					)					):
													yield ind
													lowercase__					:   Union[str, Any]													=					self._get_next_ind(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													if self._try_set(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																	break







					def __setitem__(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									if self._is_full():
													self._size_up()

									self._add_item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __delitem__(							self :     int				,       SCREAMING_SNAKE_CASE :     KEY					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Union[str, Any]													=					self._buckets[ind]
													if item is None:
																	raise KeyError(SCREAMING_SNAKE_CASE					)
													if item is _deleted:
																	continue
													if item.key == key:
																	lowercase__					:   Optional[int]													=					_deleted
																	self._len -= 1
																	break
									if self._is_sparse():
													self._size_down()







					def __getitem__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     KEY					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Any													=					self._buckets[ind]
													if item is None:
																	break
													if item is _deleted:
																	continue
													if item.key == key:
																	return item.val
									raise KeyError(SCREAMING_SNAKE_CASE					)







					def __len__(							self :     Optional[Any]					):
									return self._len







					def __iter__(							self :     List[str]					):
									yield from (item.key for item in self._buckets if item)







					def __repr__(							self :     str					):
									lowercase__					:   int													=					" ,".join(
									    f"""{item.key}: {item.val}""" for item in self._buckets if item					)
									return f"""HashMap({val_string})"""




