




'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase							:		int										=						{'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		Optional[int]										=						['LevitFeatureExtractor']
						_lowerCamelCase							:		int										=						['LevitImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:		int										=						[
						    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LevitForImageClassification',
						    'LevitForImageClassificationWithTeacher',
						    'LevitModel',
						    'LevitPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_levit import LevitFeatureExtractor
												from .image_processing_levit import LevitImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_levit import (
												    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LevitForImageClassification,
												    LevitForImageClassificationWithTeacher,
												    LevitModel,
												    LevitPreTrainedModel,
												)
else:
						import sys

						_lowerCamelCase							:		Dict										=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_lowerCamelCase							:		int										=						logging.get_logger(__name__)

_lowerCamelCase							:		Any										=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase							:		Dict										=						{
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase							:		List[str]										=						{
    'microsoft/mpnet-base': 512,
}

_lowerCamelCase							:		Tuple										=						{
    'microsoft/mpnet-base': {'do_lower_case': True},
}





class   lowerCamelCase_							( UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__					=				VOCAB_FILES_NAMES
					lowercase__					=				PRETRAINED_VOCAB_FILES_MAP
					lowercase__					=				PRETRAINED_INIT_CONFIGURATION
					lowercase__					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__					=				MPNetTokenizer
					lowercase__					=				["""input_ids""", """attention_mask"""]






					def __init__(					self,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=True,						__UpperCamelCase="<s>",						__UpperCamelCase="</s>",						__UpperCamelCase="</s>",						__UpperCamelCase="<s>",						__UpperCamelCase="[UNK]",						__UpperCamelCase="<pad>",						__UpperCamelCase="<mask>",						__UpperCamelCase=True,						__UpperCamelCase=None,						**__UpperCamelCase,						):
							super().__init__(
							    __UpperCamelCase,						tokenizer_file=__UpperCamelCase,						do_lower_case=__UpperCamelCase,						bos_token=__UpperCamelCase,						eos_token=__UpperCamelCase,						sep_token=__UpperCamelCase,						cls_token=__UpperCamelCase,						unk_token=__UpperCamelCase,						pad_token=__UpperCamelCase,						mask_token=__UpperCamelCase,						tokenize_chinese_chars=__UpperCamelCase,						strip_accents=__UpperCamelCase,						**__UpperCamelCase,						)

							_snake_case			 = json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
							if (
							    pre_tok_state.get("lowercase",						__UpperCamelCase				) != do_lower_case
							    or pre_tok_state.get("strip_accents",						__UpperCamelCase				) != strip_accents
							):
									_snake_case			 = getattr(__UpperCamelCase,						pre_tok_state.pop("type"				)				)
									_snake_case			 = do_lower_case
									_snake_case			 = strip_accents
									_snake_case			 = pre_tok_class(**__UpperCamelCase				)

							_snake_case			 = do_lower_case






					@property
					def        __SCREAMING_SNAKE_CASE			(					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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							_snake_case			 = AddedToken(__UpperCamelCase,						lstrip=__UpperCamelCase,						rstrip=__UpperCamelCase				) if isinstance(__UpperCamelCase,						__UpperCamelCase				) else value
							_snake_case			 = value






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase=None				):
							_snake_case			 = [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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
							_snake_case			 = [self.sep_token_id]
							_snake_case			 = [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]






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
							_snake_case			 = self._tokenizer.model.save(__UpperCamelCase,						name=__UpperCamelCase				)
							return tuple(__UpperCamelCase				)





