
'''simple docstring'''





from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			str						,			lowerCAmelCase_	:			Tuple     ) ->							None:
        snake_case_    							=				vocab
        snake_case_    							=				normalizers.BertNormalizer(
            clean_text=lowerCAmelCase_						,			handle_chinese_chars=lowerCAmelCase_						,			strip_accents=lowerCAmelCase_						,			lowercase=lowerCAmelCase_						,			)
        try:
              import rjieba
        except ImportError:
              raise ImportError(
                  "You need to install rjieba to use RoFormerTokenizer. "
                  "See https://pypi.org/project/rjieba/ for installation."     )
        snake_case_    							=				rjieba

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			NormalizedString     ) ->							List[NormalizedString]:
        snake_case_    							=				[]

        # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
        for token, start, end in self.jieba.tokenize(str(lowerCAmelCase_     )						,			hmm=lowerCAmelCase_     ):
              if token in self.vocab:
                    splits.append(normalized_string[start:end]     )
              else:
                    snake_case_    							=				self.normalizers.normalize_str(lowerCAmelCase_     ).split()
                    for token in token_list:
                          if token:
                                snake_case_    							=				start + len(lowerCAmelCase_     )
                                splits.append(normalized_string[start:end]     )
                                snake_case_    							=				end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

        return splits


  def 		_lowerCamelCase						(self	:			List[Any]						,			lowerCAmelCase_	:			PreTokenizedString     ) ->							Any:
        pretok.split(self.jieba_split     )



'''simple docstring'''





from typing import TYPE_CHECKING

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


A							     = {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["MarianTokenizer"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = [
         "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MarianForCausalLM",
         "MarianModel",
         "MarianMTModel",
         "MarianPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

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

