'''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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							DebertaTokenizer
				UpperCAmelCase__							=							True
				UpperCAmelCase__							=							DebertaTokenizerFast





				def  snake_case__				(						self					:						List[Any]					)							->Dict:


										'''simple docstring'''




										super().setUp()

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

										_UpperCamelCase			:			int   			=						os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES["vocab_file"]					)
										_UpperCamelCase			:			Dict   			=						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(lowercase__					) + "\n"					)
										with open(self.merges_file       ,		"w"       ,		encoding="utf-8"					) as fp:
																fp.write("\n".join(lowercase__					)					)





				def  snake_case__				(						self					:						List[str]       ,		**lowercase__					:						Optional[int]					)							->str:


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						List[Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						"lower newer"
										_UpperCamelCase			:			Union[str, Any]   			=						"lower newer"
										return input_text, output_text





				def  snake_case__				(						self					:						int					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						self.get_tokenizer()
										_UpperCamelCase			:			Union[str, Any]   			=						"lower newer"
										_UpperCamelCase			:			List[Any]   			=						["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										_UpperCamelCase			:			Optional[int]   			=						tokenizer.tokenize(lowercase__					)
										self.assertListEqual(lowercase__       ,		lowercase__					)

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





				def  snake_case__				(						self					:						Dict					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						self.get_tokenizer()
										_UpperCamelCase			:			List[str]   			=						tokenizer("Hello"       ,		"World"					)
										_UpperCamelCase			:			List[Any]   			=						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd["token_type_ids"]       ,		lowercase__					)





				@slow
				def  snake_case__				(						self					:						str					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.tokenizer_class.from_pretrained("microsoft/deberta-base"					)

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

										_UpperCamelCase			:			Tuple   			=						tokenizer.encode(
										    "sequence builders"       ,		add_special_tokens=lowercase__       ,		add_prefix_space=lowercase__					)
										_UpperCamelCase			:			Tuple   			=						tokenizer.encode(
										    "sequence builders"       ,		"multi-sequence build"       ,		add_special_tokens=lowercase__       ,		add_prefix_space=lowercase__					)

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

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







				@slow
				def  snake_case__				(						self					:						List[str]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						[self.tokenizer_class]
										if self.test_rust_tokenizer:
																tokenizer_classes.append(self.rust_tokenizer_class					)

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

																_UpperCamelCase			:			int   			=						[
																    "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.",
																]

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

																# fmt: off
																_UpperCamelCase			:			Optional[Any]   			=						{
																    "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

																_UpperCamelCase			:			Optional[int]   			=						[
																    "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       ,		lowercase__					)

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



'''simple docstring'''





lowerCAmelCase_	:		Union[str, Any]			=     """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCAmelCase_	:		List[Any]			=     [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase_	:		int			=     {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


