




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    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   int					,							snake_case__	:   str = "▁"					,							snake_case__	:   bool = True					,							snake_case__	:   Union[str, AddedToken] = "<unk>"					,							snake_case__	:   Union[str, AddedToken] = "</s>"					,							snake_case__	:   Union[str, AddedToken] = "<pad>"					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'pad': {'id': 0, 'token': pad_token},
								    'eos': {'id': 1, 'token': eos_token},
								    'unk': {'id': 2, 'token': unk_token},
								}

								SCREAMING_SNAKE_CASE_					      =							[None] * len(self.special_tokens  )
								for token_dict in self.special_tokens.values():
										SCREAMING_SNAKE_CASE_					      =							token_dict['token']

								SCREAMING_SNAKE_CASE_					      =							Tokenizer(Unigram()  )

								SCREAMING_SNAKE_CASE_					      =							normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(' {2,}'  )					,							' '  ),
								        normalizers.Lowercase(),
								    ]  )
								SCREAMING_SNAKE_CASE_					      =							pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=snake_case__					,							add_prefix_space=snake_case__  ),
								        pre_tokenizers.Digits(individual_digits=snake_case__  ),
								        pre_tokenizers.Punctuation(),
								    ]  )
								SCREAMING_SNAKE_CASE_					      =							decoders.Metaspace(replacement=snake_case__					,							add_prefix_space=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							TemplateProcessing(
								    single=f'''$A {self.special_tokens["eos"]["token"]}'''					,							special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]					,							)

								SCREAMING_SNAKE_CASE_					      =							{
								    'model': 'SentencePieceUnigram',
								    'replacement': replacement,
								    'add_prefix_space': add_prefix_space,
								}

								super().__init__(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Union[str, List[str]]					,							snake_case__	:   int = 80_00					,							snake_case__	:   bool = True					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							trainers.UnigramTrainer(
								    vocab_size=snake_case__					,							special_tokens=self.special_tokens_list					,							show_progress=snake_case__					,							)

								if isinstance(snake_case__					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							[files]
								self._tokenizer.train(snake_case__					,							trainer=snake_case__  )

								self.add_unk_id()



						def 	__a					(			self	:   Tuple					,							snake_case__	:   Union[Iterator[str], Iterator[Iterator[str]]]					,							snake_case__	:   int = 80_00					,							snake_case__	:   bool = True					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							trainers.UnigramTrainer(
								    vocab_size=snake_case__					,							special_tokens=self.special_tokens_list					,							show_progress=snake_case__					,							)

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

								self.add_unk_id()






						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							json.loads(self._tokenizer.to_str()  )

								SCREAMING_SNAKE_CASE_					      =							self.special_tokens['unk']['id']

								SCREAMING_SNAKE_CASE_					      =							Tokenizer.from_str(json.dumps(snake_case__  )  )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)