"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


a							=logging.get_logger(__name__)

a							={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a							={
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a							={
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a							={
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       VOCAB_FILES_NAMES
				A__      :   str  							=       PRETRAINED_VOCAB_FILES_MAP
				A__      :   Union[str, Any]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__      :   Union[str, Any]  							=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
				A__      :   List[str]  							=       DebertaTokenizer

				def __init__(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase="replace"	, _lowerCamelCase="[CLS]"	, _lowerCamelCase="[SEP]"	, _lowerCamelCase="[SEP]"	, _lowerCamelCase="[CLS]"	, _lowerCamelCase="[UNK]"	, _lowerCamelCase="[PAD]"	, _lowerCamelCase="[MASK]"	, _lowerCamelCase=False	, **_lowerCamelCase	, ):
										super().__init__(
										    _lowerCamelCase	, _lowerCamelCase	, tokenizer_file=_lowerCamelCase	, errors=_lowerCamelCase	, bos_token=_lowerCamelCase	, eos_token=_lowerCamelCase	, unk_token=_lowerCamelCase	, sep_token=_lowerCamelCase	, cls_token=_lowerCamelCase	, pad_token=_lowerCamelCase	, mask_token=_lowerCamelCase	, add_prefix_space=_lowerCamelCase	, **_lowerCamelCase	, )
										lowerCamelCase__					=kwargs.pop("add_bos_token"	, _lowerCamelCase      )

										lowerCamelCase__					=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get("add_prefix_space"	, _lowerCamelCase      ) != add_prefix_space:
																lowerCamelCase__					=getattr(_lowerCamelCase	, pre_tok_state.pop("type"      )      )
																lowerCamelCase__					=add_prefix_space
																lowerCamelCase__					=pre_tok_class(**_lowerCamelCase      )

										lowerCamelCase__					=add_prefix_space

				@property
				def 		_a	(      self      ):
										if self._mask_token is None:
																if self.verbose:
																						logger.error("Using mask_token, but it is not set yet."      )
																return None
										return str(self._mask_token      )

				@mask_token.setter
				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=AddedToken(_lowerCamelCase	, lstrip=_lowerCamelCase	, rstrip=_lowerCamelCase      ) if isinstance(_lowerCamelCase	, _lowerCamelCase      ) else value
										lowerCamelCase__					=value

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None      ):
										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowerCamelCase__					=[self.cls_token_id]
										lowerCamelCase__					=[self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None      ):
										lowerCamelCase__					=[self.sep_token_id]
										lowerCamelCase__					=[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep      ) * [0]
										return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

				def 		_a	(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										lowerCamelCase__					=kwargs.get("is_split_into_words"	, _lowerCamelCase      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										lowerCamelCase__					=kwargs.get("is_split_into_words"	, _lowerCamelCase      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None      ):
										lowerCamelCase__					=self._tokenizer.model.save(_lowerCamelCase	, name=_lowerCamelCase      )
										return tuple(_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(_lowerCamelCase	, add_special_tokens=_lowerCamelCase      ) + [self.eos_token_id]      )

										if len(_lowerCamelCase      ) > self.model_max_length:
																lowerCamelCase__					=input_ids[-self.model_max_length :]
										return input_ids








'''simple docstring'''







import unittest
from timeit import timeit






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       first_num if first_num >= second_num else second_num
		_snake_case	       =       max_num
		while (common_mult % first_num > 0) or (common_mult % second_num > 0):
				common_mult += max_num
		return common_mult






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return b if a == 0 else greatest_common_divisor(b % a						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return first_num // greatest_common_divisor(lowercase_						,							lowercase_  ) * second_num






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       (
		    "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
		)
		print(
		    "least_common_multiple_slow():"						,							timeit("least_common_multiple_slow(1000, 999)"						,							setup=lowercase_  )						,							)
		print(
		    "least_common_multiple_fast():"						,							timeit("least_common_multiple_fast(1000, 999)"						,							setup=lowercase_  )						,							)





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (
					    (1_0, 2_0),
					    (1_3, 1_5),
					    (4, 3_1),
					    (1_0, 4_2),
					    (4_3, 3_4),
					    (5, 1_2),
					    (1_2, 2_5),
					    (1_0, 2_5),
					    (6, 9),
					)
					lowercase__    = (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for i, (first_num, second_num) in enumerate(self.test_inputs ):
									_snake_case	       =       least_common_multiple_slow(__UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       least_common_multiple_fast(__UpperCamelCase,  __UpperCamelCase )
									with self.subTest(i=__UpperCamelCase ):
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )
											self.assertEqual(__UpperCamelCase,  self.expected_results[i] )


if __name__ == "__main__":
						benchmark()
						unittest.main()





