
"""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 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: int            =			DebertaTokenizer
		A_		: Optional[int]            =			True
		A_		: Optional[Any]            =			DebertaTokenizerFast

		def 							__lowerCamelCase							(   self				):
			super().setUp()

			# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
			__lowerCAmelCase    :		Optional[Any]		    =   [
			    'l',
			    'o',
			    'w',
			    'e',
			    'r',
			    's',
			    't',
			    'i',
			    'd',
			    'n',
			    '\u0120',
			    '\u0120l',
			    '\u0120n',
			    '\u0120lo',
			    '\u0120low',
			    'er',
			    '\u0120lowest',
			    '\u0120newer',
			    '\u0120wider',
			    '[UNK]',
			]
			__lowerCAmelCase    :		List[Any]		    =   dict(zip(_SCREAMING_SNAKE_CASE ,	range(len(_SCREAMING_SNAKE_CASE				)				)				)				)
			__lowerCAmelCase    :		Optional[int]		    =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
			__lowerCAmelCase    :		Any		    =   {'unk_token': '[UNK]'}

			__lowerCAmelCase    :		int		    =   os.path.join(self.tmpdirname ,	VOCAB_FILES_NAMES['vocab_file']				)
			__lowerCAmelCase    :		List[Any]		    =   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 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			kwargs.update(self.special_tokens_map				)
			return self.tokenizer_class.from_pretrained(self.tmpdirname ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   'lower newer'
			__lowerCAmelCase    :		List[str]		    =   'lower newer'
			return input_text, output_text

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.get_tokenizer()
			__lowerCAmelCase    :		Dict		    =   'lower newer'
			__lowerCAmelCase    :		Optional[int]		    =   ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
			__lowerCAmelCase    :		List[Any]		    =   tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.get_tokenizer()
			__lowerCAmelCase    :		int		    =   tokenizer('Hello' ,	'World'				)
			__lowerCAmelCase    :		Any		    =   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
			self.assertListEqual(tokd['token_type_ids'] ,	_SCREAMING_SNAKE_CASE				)

		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   self.tokenizer_class.from_pretrained('microsoft/deberta-base'				)

			__lowerCAmelCase    :		Dict		    =   tokenizer.encode('sequence builders' ,	add_special_tokens=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   tokenizer.encode('multi-sequence build' ,	add_special_tokens=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Union[str, Any]		    =   tokenizer.encode(
			    'sequence builders' ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	add_prefix_space=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   tokenizer.encode(
			    'sequence builders' ,	'multi-sequence build' ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	add_prefix_space=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   [self.tokenizer_class]
			if self.test_rust_tokenizer:
				tokenizer_classes.append(self.rust_tokenizer_class				)

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

				__lowerCAmelCase    :		Dict		    =   [
				    '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    :		str		    =   tokenizer(_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[Any]		    =   [tokenizer.decode(_SCREAMING_SNAKE_CASE ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				) for seq in encoding['input_ids']]

				# fmt: off
				__lowerCAmelCase    :		int		    =   {
				    '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

				__lowerCAmelCase    :		Tuple		    =   [
				    '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				)

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()