




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  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase = "▁"      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = "<unk>"      ,				__UpperCAmelCase = "</s>"      ,				__UpperCAmelCase = "<pad>"      ,				):
        lowerCAmelCase__				:  List[str]		       =     {
            '''pad''': {'''id''': 0, '''token''': pad_token},
            '''eos''': {'''id''': 1, '''token''': eos_token},
            '''unk''': {'''id''': 2, '''token''': unk_token},
        }

        lowerCAmelCase__				:  Optional[Any]		       =     [None] * len(self.special_tokens		)
        for token_dict in self.special_tokens.values():
          lowerCAmelCase__				:  Union[str, Any]		       =     token_dict['''token''']

        lowerCAmelCase__				:  str		       =     Tokenizer(Unigram()		)

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

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

        lowerCAmelCase__				:  Dict		       =     {
            '''model''': '''SentencePieceUnigram''',
            '''replacement''': replacement,
            '''add_prefix_space''': add_prefix_space,
        }

        super().__init__(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = 8000      ,				__UpperCAmelCase = True      ,				):
        lowerCAmelCase__				:  List[str]		       =     trainers.UnigramTrainer(
            vocab_size=__UpperCAmelCase      ,				special_tokens=self.special_tokens_list      ,				show_progress=__UpperCAmelCase      ,				)

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

        self.add_unk_id()

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = 8000      ,				__UpperCAmelCase = True      ,				):
        lowerCAmelCase__				:  str		       =     trainers.UnigramTrainer(
            vocab_size=__UpperCAmelCase      ,				special_tokens=self.special_tokens_list      ,				show_progress=__UpperCAmelCase      ,				)

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

        self.add_unk_id()







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     json.loads(self._tokenizer.to_str()		)

        lowerCAmelCase__				:  Optional[int]		       =     self.special_tokens['''unk''']['''id''']

        lowerCAmelCase__				:  List[str]		       =     Tokenizer.from_str(json.dumps(__UpperCAmelCase		)		)







from typing import TYPE_CHECKING

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


lowerCAmelCase_					    = {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = ["""LongformerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LongformerForMaskedLM""",
         """LongformerForMultipleChoice""",
         """LongformerForQuestionAnswering""",
         """LongformerForSequenceClassification""",
         """LongformerForTokenClassification""",
         """LongformerModel""",
         """LongformerPreTrainedModel""",
         """LongformerSelfAttention""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLongformerForMaskedLM""",
         """TFLongformerForMultipleChoice""",
         """TFLongformerForQuestionAnswering""",
         """TFLongformerForSequenceClassification""",
         """TFLongformerForTokenClassification""",
         """TFLongformerModel""",
         """TFLongformerPreTrainedModel""",
         """TFLongformerSelfAttention""",
     ]


if TYPE_CHECKING:
     from .configuration_longformer import (
         LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LongformerConfig,
         LongformerOnnxConfig,
     )
     from .tokenization_longformer import LongformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_longformer_fast import LongformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_longformer import (
              LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              LongformerForMaskedLM,
              LongformerForMultipleChoice,
              LongformerForQuestionAnswering,
              LongformerForSequenceClassification,
              LongformerForTokenClassification,
              LongformerModel,
              LongformerPreTrainedModel,
              LongformerSelfAttention,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_longformer import (
              TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLongformerForMaskedLM,
              TFLongformerForMultipleChoice,
              TFLongformerForQuestionAnswering,
              TFLongformerForSequenceClassification,
              TFLongformerForTokenClassification,
              TFLongformerModel,
              TFLongformerPreTrainedModel,
              TFLongformerSelfAttention,
          )

else:
     import sys

     lowerCAmelCase_					    = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

