


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


a			:			Tuple												=					logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
a			:			Tuple												=					{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

a			:			List[str]												=					{
    '''RUCAIBox/mvp''': 1024,
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					VOCAB_FILES_NAMES
					__UpperCAmelCase							  =					PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase							  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase							  =					["input_ids", "attention_mask"]
					__UpperCAmelCase							  =					MvpTokenizer






					def __init__(					self			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_="replace"			,							snake_case_="<s>"			,							snake_case_="</s>"			,							snake_case_="</s>"			,							snake_case_="<s>"			,							snake_case_="<unk>"			,							snake_case_="<pad>"			,							snake_case_="<mask>"			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							snake_case_			,							tokenizer_file=snake_case_			,							errors=snake_case_			,							bos_token=snake_case_			,							eos_token=snake_case_			,							sep_token=snake_case_			,							cls_token=snake_case_			,							unk_token=snake_case_			,							pad_token=snake_case_			,							mask_token=snake_case_			,							add_prefix_space=snake_case_			,							trim_offsets=snake_case_			,							**snake_case_			,							)

								__lowercase			       = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
								if pre_tok_state.get('''add_prefix_space'''			,							snake_case_    ) != add_prefix_space:
											__lowercase			       = getattr(snake_case_			,							pre_tok_state.pop('''type'''    )    )
											__lowercase			       = add_prefix_space
											__lowercase			       = pre_tok_class(**snake_case_    )

								__lowercase			       = add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								__lowercase			       = '''post_processor'''
								__lowercase			       = getattr(self.backend_tokenizer			,							snake_case_			,							snake_case_    )
								if tokenizer_component_instance:
											__lowercase			       = json.loads(tokenizer_component_instance.__getstate__()    )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														__lowercase			       = tuple(state['''sep''']    )
											if "cls" in state:
														__lowercase			       = tuple(state['''cls''']    )

											__lowercase			       = False

											if state.get('''add_prefix_space'''			,							snake_case_    ) != add_prefix_space:
														__lowercase			       = add_prefix_space
														__lowercase			       = True

											if state.get('''trim_offsets'''			,							snake_case_    ) != trim_offsets:
														__lowercase			       = trim_offsets
														__lowercase			       = True

											if changes_to_apply:
														__lowercase			       = getattr(snake_case_			,							state.pop('''type'''    )    )
														__lowercase			       = component_class(**snake_case_    )
														setattr(self.backend_tokenizer			,							snake_case_			,							snake_case_    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								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			,							snake_case_    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = AddedToken(snake_case_			,							lstrip=snake_case_			,							rstrip=snake_case_    ) if isinstance(snake_case_			,							snake_case_    ) else value
								__lowercase			       = value






					def A   (					self			,							*snake_case_			,							**snake_case_    ) ->      BatchEncoding:







								'''simple docstring'''

								__lowercase			       = kwargs.get('''is_split_into_words'''			,							snake_case_    )

								if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
											    '''to use it with pretokenized inputs.'''    )

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






					def A   (					self			,							*snake_case_			,							**snake_case_    ) ->      BatchEncoding:







								'''simple docstring'''

								__lowercase			       = kwargs.get('''is_split_into_words'''			,							snake_case_    )

								if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
											    '''to use it with pretokenized inputs.'''    )

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






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      Tuple[str]:







								'''simple docstring'''

								__lowercase			       = self._tokenizer.model.save(snake_case_			,							name=snake_case_    )
								return tuple(snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_=None    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      List[int]:







								'''simple docstring'''

								__lowercase			       = [self.sep_token_id]
								__lowercase			       = [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 + sep + token_ids_a + sep    ) * [0]






from maths.prime_factors import prime_factors


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase							,      _UpperCamelCase	):
						__lowercase			       = F'Input value of [number={number}] must be an integer'
						raise TypeError(_UpperCamelCase	)
			if number < 1:
						raise ValueError('''Input must be a positive integer'''	)
			return -1 if len(prime_factors(_UpperCamelCase	)	) % 2 else 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()


