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



		def __UpperCamelCase			(			self	)  -> 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(A_    ,				range(len(A_	)	)	)	)
				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(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Any:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return self.tokenizer_class.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					'lower newer'
				UpperCamelCase          =					'lower newer'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.get_tokenizer()
				UpperCamelCase          =					'lower newer'
				UpperCamelCase          =					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""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']    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer_class.from_pretrained('microsoft/deberta-base'	)

				UpperCamelCase          =					tokenizer.encode('sequence builders'    ,				add_special_tokens=A_	)
				UpperCamelCase          =					tokenizer.encode('multi-sequence build'    ,				add_special_tokens=A_	)

				UpperCamelCase          =					tokenizer.encode(
				    'sequence builders'    ,				add_special_tokens=A_    ,				add_prefix_space=A_	)
				UpperCamelCase          =					tokenizer.encode(
				    'sequence builders'    ,				'multi-sequence build'    ,				add_special_tokens=A_    ,				add_prefix_space=A_	)

				UpperCamelCase          =					tokenizer.build_inputs_with_special_tokens(A_	)
				UpperCamelCase          =					tokenizer.build_inputs_with_special_tokens(A_    ,				A_	)

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode



		@slow
		def __UpperCamelCase			(			self	)  -> List[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(A_    ,				padding=A_	)
						UpperCamelCase          =					[tokenizer.decode(A_    ,				skip_special_tokens=A_	) for seq in encoding['input_ids']]

						# fmt: off
						UpperCamelCase          =					{
						    '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          =					[
						    '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 unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase :			Union[str, Any]              =	get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")


@require_sentencepiece
@require_tokenizers
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    GPTSwaTokenizer
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Union[str, Any]     					=    True
		__lowercase	:	Tuple     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase          =					GPTSwaTokenizer(A_    ,				eos_token='<unk>'    ,				bos_token='<unk>'    ,				pad_token='<unk>'	)

				tokenizer.save_pretrained(self.tmpdirname	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'This is a test'
				UpperCamelCase          =					'This is a test'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					'<s>'
				UpperCamelCase          =					1

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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""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(A_	)    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)

				UpperCamelCase          =					tokenizer.tokenize('This is a test'	)
				self.assertListEqual(A_    ,				['▁This', '▁is', '▁a', '▁t', 'est']	)

				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				[465, 287, 265, 631, 842]	)

				UpperCamelCase          =					tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    ,				)
				# fmt: on

				UpperCamelCase          =					tokenizer.convert_tokens_to_ids(A_	)
				self.assertListEqual(
				    A_    ,				[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]    ,				)

				UpperCamelCase          =					tokenizer.convert_ids_to_tokens(A_	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']	)
				# fmt: on



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)
				UpperCamelCase          =					['This is a test', 'I was born in 92000, and this is falsé.']
				UpperCamelCase          =					[
				    [465, 287, 265, 631, 842],
				    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
				]

				# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
				for text, expected_ids in zip(A_    ,				A_	):
						self.assertListEqual(tokenizer.encode_fast(A_	)    ,				A_	)

				# Test that decode_fast returns the input text
				for text, token_ids in zip(A_    ,				A_	):
						self.assertEqual(tokenizer.decode_fast(A_	)    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[
				    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
				    'Hey there, how are you doing this fine day?',
				    'This is a text with a trailing spaces followed by a dot     .',
				    'Häj sväjs lillebrör! =)',
				    'Det är inget fel på Mr. Cool',
				]

				# fmt: off
				UpperCamelCase          =					{'input_ids': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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]], '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, 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, 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], [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]]}
				# fmt: on
				self.tokenizer_integration_test_util(
				    expected_encoding=A_    ,				model_name='AI-Sweden/gpt-sw3-126m'    ,				sequences=A_    ,				)
