



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   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		DebertaTokenizer
							lowerCamelCase__   			=		True
							lowerCamelCase__   			=		DebertaTokenizerFast







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

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

														_lowerCAmelCase     :							List[str]    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"])
														_lowerCAmelCase     :							str    =    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(__a) + "\n")
														with open(self.merges_file,			"w",			encoding="utf-8") as fp:
																					fp.write("\n".join(__a))







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    "lower newer"
														_lowerCAmelCase     :							List[str]    =    "lower newer"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.get_tokenizer()
														_lowerCAmelCase     :							Dict    =    "lower newer"
														_lowerCAmelCase     :							List[Any]    =    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
														_lowerCAmelCase     :							Dict    =    tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Dict    =    tokens + [tokenizer.unk_token]
														_lowerCAmelCase     :							Any    =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							str    =    tokenizer("Hello",			"World")
														_lowerCAmelCase     :							Optional[int]    =    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
														self.assertListEqual(tokd["token_type_ids"],			__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.tokenizer_class.from_pretrained("microsoft/deberta-base")

														_lowerCAmelCase     :							List[Any]    =    tokenizer.encode("sequence builders",			add_special_tokens=__a)
														_lowerCAmelCase     :							str    =    tokenizer.encode("multi-sequence build",			add_special_tokens=__a)

														_lowerCAmelCase     :							Tuple    =    tokenizer.encode(
														    "sequence builders",			add_special_tokens=__a,			add_prefix_space=__a)
														_lowerCAmelCase     :							Optional[Any]    =    tokenizer.encode(
														    "sequence builders",			"multi-sequence build",			add_special_tokens=__a,			add_prefix_space=__a)

														_lowerCAmelCase     :							Dict    =    tokenizer.build_inputs_with_special_tokens(__a)
														_lowerCAmelCase     :							Any    =    tokenizer.build_inputs_with_special_tokens(__a,			__a)

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

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

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

																					_lowerCAmelCase     :							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.",
																					]

																					_lowerCAmelCase     :							Dict    =    tokenizer(__a,			padding=__a)
																					_lowerCAmelCase     :							Tuple    =    [tokenizer.decode(__a,			skip_special_tokens=__a) for seq in encoding["input_ids"]]

																					# fmt: off
																					_lowerCAmelCase     :							int    =    {
																					    "input_ids": [
																					        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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     :							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,			__a)

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





import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case		=  get_tests_dir("fixtures/test_sentencepiece_bpe.model")



class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		BartphoTokenizer
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase     :							str    =    ["▁This", "▁is", "▁a", "▁t", "est"]
														_lowerCAmelCase     :							List[str]    =    dict(zip(__a,			range(len(__a))))
														_lowerCAmelCase     :							Optional[Any]    =    {"unk_token": "<unk>"}

														_lowerCAmelCase     :							Optional[int]    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["monolingual_vocab_file"])
														with open(self.monolingual_vocab_file,			"w",			encoding="utf-8") as fp:
																					for token in vocab_tokens:
																												fp.write(f"{token} {vocab_tokens[token]}\n")

														_lowerCAmelCase     :							Optional[Any]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return BartphoTokenizer.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "This is a là test"
														_lowerCAmelCase     :							Optional[int]    =    "This is a<unk><unk> test"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														_lowerCAmelCase     :							List[Any]    =    "This is a là test"
														_lowerCAmelCase     :							str    =    "▁This ▁is ▁a ▁l à ▁t est".split()
														_lowerCAmelCase     :							str    =    tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Tuple    =    tokens + [tokenizer.unk_token]
														_lowerCAmelCase     :							List[str]    =    [4, 5, 6, 3, 3, 7, 8, 3]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),			__a)
