




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 ):
						lowerCAmelCase__			      =DebertaTokenizer
						lowerCAmelCase__			      =True
						lowerCAmelCase__			      =DebertaTokenizerFast



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								super().setUp()

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

								SCREAMING_SNAKE_CASE_					      =							os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']  )
								SCREAMING_SNAKE_CASE_					      =							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(snake_case__  ) + '\n'  )
								with open(self.merges_file					,							'w'					,							encoding='utf-8'  ) as fp:
										fp.write('\n'.join(snake_case__  )  )



						def 	__a					(			self	:   Tuple					,							**snake_case__	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   str					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'lower newer'
								SCREAMING_SNAKE_CASE_					      =							'lower newer'
								return input_text, output_text



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()
								SCREAMING_SNAKE_CASE_					      =							'lower newer'
								SCREAMING_SNAKE_CASE_					      =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
								SCREAMING_SNAKE_CASE_					      =							tokenizer.tokenize(snake_case__  )
								self.assertListEqual(snake_case__					,							snake_case__  )

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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()
								SCREAMING_SNAKE_CASE_					      =							tokenizer('Hello'					,							'World'  )
								SCREAMING_SNAKE_CASE_					      =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
								self.assertListEqual(tokd['token_type_ids']					,							snake_case__  )



						@slow
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer.encode('sequence builders'					,							add_special_tokens=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.encode('multi-sequence build'					,							add_special_tokens=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer.encode(
								    'sequence builders'					,							add_special_tokens=snake_case__					,							add_prefix_space=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.encode(
								    'sequence builders'					,							'multi-sequence build'					,							add_special_tokens=snake_case__					,							add_prefix_space=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer.build_inputs_with_special_tokens(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.build_inputs_with_special_tokens(snake_case__					,							snake_case__  )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode






						@slow
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

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

								for tokenizer_class in tokenizer_classes:
										SCREAMING_SNAKE_CASE_					      =							tokenizer_class.from_pretrained('microsoft/deberta-base'  )

										SCREAMING_SNAKE_CASE_					      =							[
										    '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.',
										]

										SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__					,							padding=snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							[tokenizer.decode(snake_case__					,							skip_special_tokens=snake_case__  ) for seq in encoding['input_ids']]

										# fmt: off
										SCREAMING_SNAKE_CASE_					      =							{
										    '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

										SCREAMING_SNAKE_CASE_					      =							[
										    '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					,							snake_case__  )

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





import functools


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# Validation
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ) or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for day in days ):
				raise ValueError('The parameter days should be a list of integers' )

		if len(lowerCAmelCase ) != 3 or not all(isinstance(lowerCAmelCase    ,						lowerCAmelCase ) for cost in costs ):
				raise ValueError('The parameter costs should be a list of three integers' )

		if len(lowerCAmelCase ) == 0:
				return 0

		if min(lowerCAmelCase ) <= 0:
				raise ValueError('All days elements should be greater than 0' )

		if max(lowerCAmelCase ) >= 366:
				raise ValueError('All days elements should be less than 366' )

		SCREAMING_SNAKE_CASE_					      =							set(lowerCAmelCase )

		@functools.cache
		def dynamic_programming(lowerCAmelCase ) -> int:
				if index > 365:
						return 0

				if index not in days_set:
						return dynamic_programming(index + 1 )

				return min(
				    costs[0] + dynamic_programming(index + 1 )    ,						costs[1] + dynamic_programming(index + 7 )    ,						costs[2] + dynamic_programming(index + 30 )    ,						)

		return dynamic_programming(1 )


if __name__ == "__main__":
						import doctest

						doctest.testmod()