from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers





class 	_lowercase		:

 def __init__(				self			:							List[str]     ,   snake_case			:							Any    )     ->		None:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      vocab
  UpperCamelCase_      :		Dict				=      normalizers.BertNormalizer(
      clean_text=snake_case     ,   handle_chinese_chars=snake_case     ,   strip_accents=snake_case     ,   lowercase=snake_case     ,   )
  try:
   import rjieba
  except ImportError:
   raise ImportError(
       'You need to install rjieba to use RoFormerTokenizer. '
       'See https://pypi.org/project/rjieba/ for installation.'    )
  UpperCamelCase_      :		Tuple				=      rjieba

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							int     ,   snake_case			:							NormalizedString    )     ->		List[NormalizedString]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      []

  # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
  for token, start, end in self.jieba.tokenize(str(snake_case    )     ,   hmm=snake_case    ):
   if token in self.vocab:
    splits.append(normalized_string[start:end]    )
   else:
    UpperCamelCase_      :		Any				=      self.normalizers.normalize_str(snake_case    ).split()
    for token in token_list:
     if token:
      UpperCamelCase_      :		Any				=      start + len(snake_case    )
      splits.append(normalized_string[start:end]    )
      UpperCamelCase_      :		List[Any]				=      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 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							PreTokenizedString    )     ->		int:

  """simple docstring"""





  pretok.split(self.jieba_split    )




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__)



