



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  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Dict          =DebertaTokenizer
		lowercase      :       List[Any]          =True
		lowercase      :       Tuple          =DebertaTokenizerFast






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					List[str]:
								'''simple docstring'''







								super().setUp()

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

								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		**UpperCamelCase__      :     Tuple      )				->					Union[str, Any]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Optional[int]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "lower newer"
								UpperCAmelCase         =     "lower newer"
								return input_text, output_text






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.get_tokenizer()
								UpperCAmelCase         =     "lower newer"
								UpperCAmelCase         =     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.get_tokenizer()
								UpperCAmelCase         =     tokenizer("Hello"				,		"World"      )
								UpperCAmelCase         =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
								self.assertListEqual(tokd["token_type_ids"]				,		UpperCamelCase__      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.tokenizer_class.from_pretrained("microsoft/deberta-base"      )

								UpperCAmelCase         =     tokenizer.encode("sequence builders"				,		add_special_tokens=UpperCamelCase__      )
								UpperCAmelCase         =     tokenizer.encode("multi-sequence build"				,		add_special_tokens=UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.encode(
								    "sequence builders"				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )
								UpperCAmelCase         =     tokenizer.encode(
								    "sequence builders"				,		"multi-sequence build"				,		add_special_tokens=UpperCamelCase__				,		add_prefix_space=UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.build_inputs_with_special_tokens(UpperCamelCase__      )
								UpperCAmelCase         =     tokenizer.build_inputs_with_special_tokens(UpperCamelCase__				,		UpperCamelCase__      )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[Any]:
								'''simple docstring'''







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

								for tokenizer_class in tokenizer_classes:
														UpperCAmelCase         =     tokenizer_class.from_pretrained("microsoft/deberta-base"      )

														UpperCAmelCase         =     [
														    "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         =     tokenizer(UpperCamelCase__				,		padding=UpperCamelCase__      )
														UpperCAmelCase         =     [tokenizer.decode(UpperCamelCase__				,		skip_special_tokens=UpperCamelCase__      ) for seq in encoding["input_ids"]]

														# fmt: off
														UpperCAmelCase         =     {
														    "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

														UpperCAmelCase         =     [
														    "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__      )








import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase     :							Any  							=				get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Any          =ReformerTokenizer
		lowercase      :       str          =ReformerTokenizerFast
		lowercase      :       str          =True
		lowercase      :       Optional[int]          =False
		lowercase      :       List[str]          =True






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     ReformerTokenizer(UpperCamelCase__				,		keep_accents=UpperCamelCase__      )
								tokenizer.save_pretrained(self.tmpdirname      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "<s>"
								UpperCAmelCase         =     1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__      )				,		UpperCamelCase__      )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__      )				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     list(self.get_tokenizer().get_vocab().keys()      )

								self.assertEqual(vocab_keys[0]				,		"<unk>"      )
								self.assertEqual(vocab_keys[1]				,		"<s>"      )
								self.assertEqual(vocab_keys[-1]				,		"j"      )
								self.assertEqual(len(UpperCamelCase__      )				,		10_00      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								self.assertEqual(self.get_tokenizer().vocab_size				,		10_00      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
								'''simple docstring'''







								if not self.test_rust_tokenizer:
														return

								UpperCAmelCase         =     self.get_tokenizer()
								UpperCAmelCase         =     self.get_rust_tokenizer()

								UpperCAmelCase         =     "I was born in 92000, and this is falsé."

								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.encode(UpperCamelCase__				,		add_special_tokens=UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase         =     self.get_rust_tokenizer()
								UpperCAmelCase         =     tokenizer.encode(UpperCamelCase__      )
								UpperCAmelCase         =     rust_tokenizer.encode(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Union[str, Any]=15      )				->					List[str]:
								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																				UpperCAmelCase         =     self.rust_tokenizer_class.from_pretrained(UpperCamelCase__				,		**UpperCamelCase__      )

																				# Simple input
																				UpperCAmelCase         =     "This is a simple input"
																				UpperCAmelCase         =     ["This is a simple input 1", "This is a simple input 2"]
																				UpperCAmelCase         =     ("This is a simple input", "This is a pair")
																				UpperCAmelCase         =     [
																				    ("This is a simple input 1", "This is a simple input 2"),
																				    ("This is a simple pair 1", "This is a simple pair 2"),
																				]

																				# Simple input tests
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Simple input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Simple input
																				self.assertRaises(
																				    UpperCamelCase__				,		tokenizer_r.batch_encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"				,		)

																				# Pair input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Pair input
																				self.assertRaises(UpperCamelCase__				,		tokenizer_r.encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"      )

																				# Pair input
																				self.assertRaises(
																				    UpperCamelCase__				,		tokenizer_r.batch_encode_plus				,		UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding="max_length"				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     ReformerTokenizer(UpperCamelCase__				,		keep_accents=UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.tokenize("This is a test"      )
								self.assertListEqual(UpperCamelCase__				,		["▁This", "▁is", "▁a", "▁t", "est"]      )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		[2_85, 46, 10, 1_70, 3_82]				,		)

								UpperCAmelCase         =     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]				,		)
								UpperCAmelCase         =     tokenizer.convert_tokens_to_ids(UpperCamelCase__      )
								self.assertListEqual(
								    UpperCamelCase__				,		[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,		)

								UpperCAmelCase         =     tokenizer.convert_ids_to_tokens(UpperCamelCase__      )
								self.assertListEqual(
								    UpperCamelCase__				,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]				,		)






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     "Hello World!"
								UpperCAmelCase         =     [1_26, 32, 2_62, 1_52, 38, 72, 2_87]

								self.assertListEqual(UpperCamelCase__				,		self.big_tokenizer.encode(UpperCamelCase__      )      )






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								UpperCAmelCase         =     [
								    1_08,
								    2_65,
								    24,
								    1_11,
								    4,
								    2_58,
								    1_56,
								    35,
								    28,
								    2_75,
								    3,
								    2_59,
								    2_97,
								    2_60,
								    84,
								    4,
								    35,
								    1_10,
								    44,
								    8,
								    2_59,
								    91,
								    2_68,
								    21,
								    11,
								    2_09,
								    2_74,
								    1_09,
								    2_66,
								    2_77,
								    1_17,
								    86,
								    93,
								    3_15,
								    2_58,
								    2_78,
								    2_58,
								    2_77,
								    2_58,
								    0,
								    2_58,
								    2_88,
								    2_58,
								    3_19,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    0,
								    2_58,
								    2_87,
								    2_58,
								    3_15,
								    2_58,
								    2_89,
								    2_58,
								    2_78,
								    99,
								    2_69,
								    2_66,
								    2_62,
								    8,
								    2_59,
								    2_41,
								    4,
								    2_17,
								    2_30,
								    2_68,
								    2_66,
								    55,
								    1_68,
								    1_06,
								    75,
								    1_93,
								    2_66,
								    2_23,
								    27,
								    49,
								    26,
								    2_82,
								    25,
								    2_64,
								    2_99,
								    19,
								    26,
								    0,
								    2_58,
								    2_77,
								    1_17,
								    86,
								    93,
								    1_76,
								    1_83,
								    2_70,
								    11,
								    2_62,
								    42,
								    61,
								    2_65,
								]

								self.assertListEqual(UpperCamelCase__				,		self.big_tokenizer.encode(UpperCamelCase__      )      )






		@require_torch
		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								UpperCAmelCase         =     list(self.big_tokenizer.get_vocab().keys()      )[:10]
								UpperCAmelCase         =     " ".join(UpperCamelCase__      )
								UpperCAmelCase         =     self.big_tokenizer.encode_plus(UpperCamelCase__				,		return_tensors="pt"      )
								UpperCAmelCase         =     self.big_tokenizer.batch_encode_plus([sequence, sequence]				,		return_tensors="pt"      )

								UpperCAmelCase         =     ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								UpperCAmelCase         =     encoded_sequence["input_ids"].shape
								UpperCAmelCase         =     ReformerModel(UpperCamelCase__      )

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
														model(**UpperCamelCase__      )
														model(**UpperCamelCase__      )







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     {"input_ids": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], "attention_mask": [[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]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								UpperCAmelCase         =     [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCamelCase__				,		model_name="google/reformer-crime-and-punishment"				,		revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"				,		padding=UpperCamelCase__				,		sequences=UpperCamelCase__				,		)



