


import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing





class  _a							(    _lowercase):




	def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str = "▁"		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Union[str, AddedToken] = "<unk>"		, _SCREAMING_SNAKE_CASE						:		Union[str, AddedToken] = "</s>"		, _SCREAMING_SNAKE_CASE						:		Union[str, AddedToken] = "<pad>"		, )->	Optional[Any]:
			lowerCAmelCase__       :      Tuple   					= {
			    '''pad''': {'''id''': 0, '''token''': pad_token},
			    '''eos''': {'''id''': 1, '''token''': eos_token},
			    '''unk''': {'''id''': 2, '''token''': unk_token},
			}

			lowerCAmelCase__       :      List[str]   					= [None] * len(self.special_tokens		)
			for token_dict in self.special_tokens.values():
					lowerCAmelCase__       :      int   					= token_dict['''token''']

			lowerCAmelCase__       :      Dict   					= Tokenizer(Unigram()		)

			lowerCAmelCase__       :      List[str]   					= normalizers.Sequence(
			    [
			        normalizers.Nmt(),
			        normalizers.NFKC(),
			        normalizers.Replace(Regex(''' {2,}'''		)		, ''' '''		),
			        normalizers.Lowercase(),
			    ]		)
			lowerCAmelCase__       :      str   					= pre_tokenizers.Sequence(
			    [
			        pre_tokenizers.Metaspace(replacement=_SCREAMING_SNAKE_CASE		, add_prefix_space=_SCREAMING_SNAKE_CASE		),
			        pre_tokenizers.Digits(individual_digits=_SCREAMING_SNAKE_CASE		),
			        pre_tokenizers.Punctuation(),
			    ]		)
			lowerCAmelCase__       :      Optional[int]   					= decoders.Metaspace(replacement=_SCREAMING_SNAKE_CASE		, add_prefix_space=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      str   					= TemplateProcessing(
			    single=F'$A {self.special_tokens["eos"]["token"]}'		, special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]		, )

			lowerCAmelCase__       :      List[Any]   					= {
			    '''model''': '''SentencePieceUnigram''',
			    '''replacement''': replacement,
			    '''add_prefix_space''': add_prefix_space,
			}

			super().__init__(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Union[str, List[str]]		, _SCREAMING_SNAKE_CASE						:		int = 8000		, _SCREAMING_SNAKE_CASE						:		bool = True		, )->	List[str]:
			lowerCAmelCase__       :      str   					= trainers.UnigramTrainer(
			    vocab_size=_SCREAMING_SNAKE_CASE		, special_tokens=self.special_tokens_list		, show_progress=_SCREAMING_SNAKE_CASE		, )

			if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		):
					lowerCAmelCase__       :      Any   					= [files]
			self._tokenizer.train(_SCREAMING_SNAKE_CASE		, trainer=_SCREAMING_SNAKE_CASE		)

			self.add_unk_id()




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Union[Iterator[str], Iterator[Iterator[str]]]		, _SCREAMING_SNAKE_CASE						:		int = 8000		, _SCREAMING_SNAKE_CASE						:		bool = True		, )->	Union[str, Any]:
			lowerCAmelCase__       :      Optional[int]   					= trainers.UnigramTrainer(
			    vocab_size=_SCREAMING_SNAKE_CASE		, special_tokens=self.special_tokens_list		, show_progress=_SCREAMING_SNAKE_CASE		, )

			self._tokenizer.train_from_iterator(_SCREAMING_SNAKE_CASE		, trainer=_SCREAMING_SNAKE_CASE		)

			self.add_unk_id()




	def 							UpperCAmelCase__( self						:		List[str]		)->	Dict:
			lowerCAmelCase__       :      List[Any]   					= json.loads(self._tokenizer.to_str()		)

			lowerCAmelCase__       :      str   					= self.special_tokens['''unk''']['''id''']

			lowerCAmelCase__       :      Optional[Any]   					= Tokenizer.from_str(json.dumps(_SCREAMING_SNAKE_CASE		)		)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase						=		{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		['''BeitFeatureExtractor''']
		lowerCamelCase						=		['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase						=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




