



"""simple docstring"""
import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


_UpperCAmelCase       =     5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors





def        __magic_name__			(							lowercase	):
   return PTtoTFCommand(
       args.model_name							,     args.local_dir							,     args.max_error							,     args.new_weights							,     args.no_pr							,     args.push							,     args.extra_commit_description							,     args.override_model_class							,     )
class    a		(					_lowerCAmelCase    ):



  @staticmethod
  def 		lowerCamelCase__    (  lowerCAmelCase	:   ArgumentParser						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =parser.add_parser(
         """pt-to-tf"""    ,						help=(
             """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
             """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
         )    ,						)
     train_parser.add_argument(
         """--model-name"""    ,						type=_lowerCamelCase    ,						required=_lowerCamelCase    ,						help="""The model name, including owner/organization, as seen on the hub."""    ,						)
     train_parser.add_argument(
         """--local-dir"""    ,						type=_lowerCamelCase    ,						default=""""""    ,						help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""    ,						)
     train_parser.add_argument(
         """--max-error"""    ,						type=_lowerCamelCase    ,						default=_lowerCamelCase    ,						help=(
             f'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
         )    ,						)
     train_parser.add_argument(
         """--new-weights"""    ,						action="""store_true"""    ,						help="""Optional flag to create new TensorFlow weights, even if they already exist."""    ,						)
     train_parser.add_argument(
         """--no-pr"""    ,						action="""store_true"""    ,						help="""Optional flag to NOT open a PR with converted weights."""						)
     train_parser.add_argument(
         """--push"""    ,						action="""store_true"""    ,						help="""Optional flag to push the weights directly to `main` (requires permissions)"""    ,						)
     train_parser.add_argument(
         """--extra-commit-description"""    ,						type=_lowerCamelCase    ,						default=""""""    ,						help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""    ,						)
     train_parser.add_argument(
         """--override-model-class"""    ,						type=_lowerCamelCase    ,						default=_lowerCamelCase    ,						help="""If you think you know better than the auto-detector, you can specify the model class here. """
         """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""    ,						)
     train_parser.set_defaults(func=_lowerCamelCase						)



  @staticmethod
  def 		lowerCamelCase__    (  lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =set(pt_outputs.keys()						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =set(tf_outputs.keys()						)
     if pt_out_attrs != tf_out_attrs:
        raise ValueError(
            f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
            f''' {tf_out_attrs})'''						)

     # 2. For each output attribute, computes the difference
     def _find_pt_tf_differences(lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   List[str]    ,						lowerCAmelCase	:   Union[str, Any]    ,						lowerCAmelCase	:   Union[str, Any]=""						):
        # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
        # recursivelly, keeping the name of the attribute.
        if isinstance(_lowerCamelCase    ,						torch.Tensor						):
           SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.max(np.abs(pt_out.numpy() - tf_out.numpy()						)						)
           SCREAMING_SNAKE_CASE_:	int            =tensor_difference
        else:
           SCREAMING_SNAKE_CASE_:	int            =attr_name
           for i, pt_item in enumerate(_lowerCamelCase						):
              # If it is a named attribute, we keep the name. Otherwise, just its index.
              if isinstance(_lowerCamelCase    ,						_lowerCamelCase						):
                 SCREAMING_SNAKE_CASE_:	Tuple            =root_name + pt_item
                 SCREAMING_SNAKE_CASE_:	Any            =tf_out[pt_item]
                 SCREAMING_SNAKE_CASE_:	Tuple            =pt_out[pt_item]
              else:
                 SCREAMING_SNAKE_CASE_:	Dict            =root_name + f'''[{i}]'''
                 SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tf_out[i]
              SCREAMING_SNAKE_CASE_:	List[Any]            =_find_pt_tf_differences(_lowerCamelCase    ,						_lowerCamelCase    ,						_lowerCamelCase    ,						_lowerCamelCase						)

        return differences

     return _find_pt_tf_differences(_lowerCamelCase    ,						_lowerCamelCase    ,						{}						)



  def __init__(  self	:   List[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   float    ,						lowerCAmelCase	:   bool    ,						lowerCAmelCase	:   bool    ,						lowerCAmelCase	:   bool    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   str    ,						*lowerCAmelCase	:   Dict    ,						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =logging.get_logger("""transformers-cli/pt_to_tf"""						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model_name
     SCREAMING_SNAKE_CASE_:	Any            =local_dir if local_dir else os.path.join("""/tmp"""    ,						_lowerCamelCase						)
     SCREAMING_SNAKE_CASE_:	Tuple            =max_error
     SCREAMING_SNAKE_CASE_:	str            =new_weights
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =no_pr
     SCREAMING_SNAKE_CASE_:	int            =push
     SCREAMING_SNAKE_CASE_:	Any            =extra_commit_description
     SCREAMING_SNAKE_CASE_:	Any            =override_model_class



  def 		lowerCamelCase__    (  self	:   List[Any]    ,						lowerCAmelCase	:   List[str]    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict						)       ->   str:


     '''simple docstring'''




     def _get_audio_input():
        SCREAMING_SNAKE_CASE_:	Any            =load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,						"""clean"""    ,						split="""validation"""						)
        SCREAMING_SNAKE_CASE_:	Tuple            =ds.sort("""id"""						).select(range(2						)						)[:2]["""audio"""]
        SCREAMING_SNAKE_CASE_:	str            =[x["""array"""] for x in speech_samples]
        return raw_samples

     SCREAMING_SNAKE_CASE_:	Optional[int]            =type(pt_model.config						)
     if model_config_class in PROCESSOR_MAPPING:
        SCREAMING_SNAKE_CASE_:	List[Any]            =AutoProcessor.from_pretrained(self._local_dir						)
        if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
           SCREAMING_SNAKE_CASE_:	Any            =processor.tokenizer.eos_token
     elif model_config_class in IMAGE_PROCESSOR_MAPPING:
        SCREAMING_SNAKE_CASE_:	Optional[int]            =AutoImageProcessor.from_pretrained(self._local_dir						)
     elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
        SCREAMING_SNAKE_CASE_:	Dict            =AutoFeatureExtractor.from_pretrained(self._local_dir						)
     elif model_config_class in TOKENIZER_MAPPING:
        SCREAMING_SNAKE_CASE_:	str            =AutoTokenizer.from_pretrained(self._local_dir						)
        if processor.pad_token is None:
           SCREAMING_SNAKE_CASE_:	List[Any]            =processor.eos_token
     else:
        raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''						)

     SCREAMING_SNAKE_CASE_:	List[str]            =set(inspect.signature(pt_model.forward						).parameters.keys()						)
     SCREAMING_SNAKE_CASE_:	Dict            ={}
     if "input_ids" in model_forward_signature:
        processor_inputs.update(
            {
                """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
                """padding""": True,
                """truncation""": True,
            }						)
     if "pixel_values" in model_forward_signature:
        SCREAMING_SNAKE_CASE_:	Any            =load_dataset("""cifar10"""    ,						"""plain_text"""    ,						split="""test"""						)[:2]["""img"""]
        processor_inputs.update({"""images""": sample_images}						)
     if "input_features" in model_forward_signature:
        SCREAMING_SNAKE_CASE_:	List[str]            =inspect.signature(processor.feature_extractor						).parameters
        # Pad to the largest input length by default but take feature extractor default
        # padding value if it exists e.g. "max_length" and is not False or None
        if "padding" in feature_extractor_signature:
           SCREAMING_SNAKE_CASE_:	List[Any]            =feature_extractor_signature["""padding"""].default
           if default_strategy is not False and default_strategy is not None:
              SCREAMING_SNAKE_CASE_:	Optional[int]            =default_strategy
           else:
              SCREAMING_SNAKE_CASE_:	int            =True
        else:
           SCREAMING_SNAKE_CASE_:	Tuple            =True
        processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}						)
     if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
        processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}						)
     SCREAMING_SNAKE_CASE_:	str            =processor(**_lowerCamelCase    ,						return_tensors="""pt"""						)
     SCREAMING_SNAKE_CASE_:	str            =processor(**_lowerCamelCase    ,						return_tensors="""tf"""						)

     # Extra input requirements, in addition to the input modality
     if (
         config.is_encoder_decoder
         or (hasattr(_lowerCamelCase    ,						"""encoder"""						) and hasattr(_lowerCamelCase    ,						"""decoder"""						))
         or "decoder_input_ids" in tf_dummy_inputs
     ):
        SCREAMING_SNAKE_CASE_:	List[Any]            =np.asarray([[1], [1]]    ,						dtype=_lowerCamelCase						) * (pt_model.config.decoder_start_token_id or 0)
        pt_input.update({"""decoder_input_ids""": torch.tensor(_lowerCamelCase						)}						)
        tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(_lowerCamelCase						)}						)

     return pt_input, tf_input





  def 		lowerCamelCase__    (  self	:   int						)       ->   Any:


     '''simple docstring'''




     if version.parse(huggingface_hub.__version__						) < version.parse("""0.9.0"""						):
        raise ImportError(
            """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
            """ installation."""						)
     else:
        from huggingface_hub import Repository, create_commit
        from huggingface_hub._commit_api import CommitOperationAdd

     # Fetch remote data
     SCREAMING_SNAKE_CASE_:	int            =Repository(local_dir=self._local_dir    ,						clone_from=self._model_name						)

     # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
     SCREAMING_SNAKE_CASE_:	int            =AutoConfig.from_pretrained(self._local_dir						)
     SCREAMING_SNAKE_CASE_:	Dict            =config.architectures
     if self._override_model_class is not None:
        if self._override_model_class.startswith("""TF"""						):
           SCREAMING_SNAKE_CASE_:	Optional[Any]            =[self._override_model_class[2:]]
        else:
           SCREAMING_SNAKE_CASE_:	List[Any]            =[self._override_model_class]
        try:
           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =getattr(import_module("""transformers"""						)    ,						architectures[0]						)
        except AttributeError:
           raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''						)
        try:
           SCREAMING_SNAKE_CASE_:	str            =getattr(import_module("""transformers"""						)    ,						"""TF""" + architectures[0]						)
        except AttributeError:
           raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.'''						)
     elif architectures is None:  # No architecture defined -- use auto classes
        SCREAMING_SNAKE_CASE_:	Optional[int]            =getattr(import_module("""transformers"""						)    ,						"""AutoModel"""						)
        SCREAMING_SNAKE_CASE_:	str            =getattr(import_module("""transformers"""						)    ,						"""TFAutoModel"""						)
        self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""						)
     else:  # Architecture defined -- use it
        if len(_lowerCamelCase						) > 1:
           raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''						)
        self._logger.warning(f'''Detected architecture: {architectures[0]}'''						)
        SCREAMING_SNAKE_CASE_:	str            =getattr(import_module("""transformers"""						)    ,						architectures[0]						)
        try:
           SCREAMING_SNAKE_CASE_:	Optional[int]            =getattr(import_module("""transformers"""						)    ,						"""TF""" + architectures[0]						)
        except AttributeError:
           raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''						)

         # Check the TF dummy inputs to see what keys we need in the forward pass
     SCREAMING_SNAKE_CASE_:	List[str]            =tf_class.from_config(_lowerCamelCase						)
     SCREAMING_SNAKE_CASE_:	List[str]            =tf_from_pt_model.dummy_inputs

     del tf_from_pt_model  # Try to keep only one model in memory at a time

     # Load the model and get some basic inputs
     SCREAMING_SNAKE_CASE_:	int            =pt_class.from_pretrained(self._local_dir						)
     pt_model.eval()

     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	str            =self.get_inputs(_lowerCamelCase    ,						_lowerCamelCase    ,						_lowerCamelCase						)

     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:	int            =pt_model(**_lowerCamelCase    ,						output_hidden_states=_lowerCamelCase						)
     del pt_model  # will no longer be used, and may have a large memory footprint

     SCREAMING_SNAKE_CASE_:	int            =tf_class.from_pretrained(self._local_dir    ,						from_pt=_lowerCamelCase						)
     SCREAMING_SNAKE_CASE_:	List[str]            =tf_from_pt_model(**_lowerCamelCase    ,						output_hidden_states=_lowerCamelCase    ,						training=_lowerCamelCase						)

     # Confirms that cross loading PT weights into TF worked.
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.find_pt_tf_differences(_lowerCamelCase    ,						_lowerCamelCase						)
     SCREAMING_SNAKE_CASE_:	Any            ={k: v for k, v in crossload_differences.items() if """hidden""" not in k}
     SCREAMING_SNAKE_CASE_:	Optional[int]            ={k: v for k, v in crossload_differences.items() if """hidden""" in k}
     if len(_lowerCamelCase						) == 0 and architectures is not None:
        raise ValueError(
            f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
            """ output was found. All outputs start with \'hidden\'"""						)
     SCREAMING_SNAKE_CASE_:	Dict            =max(output_differences.values()						) if output_differences else 0.0
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max(hidden_differences.values()						)
     if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
        raise ValueError(
            """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
            + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
            + """\n""".join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]						)
            + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
            + """\n""".join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]						)						)

     # Save the weights in a TF format (if needed) and confirms that the results are still good
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =os.path.join(self._local_dir    ,						_lowerCamelCase						)
     SCREAMING_SNAKE_CASE_:	Tuple            =os.path.join(self._local_dir    ,						_lowerCamelCase						)
     if (not os.path.exists(_lowerCamelCase						) and not os.path.exists(_lowerCamelCase						)) or self._new_weights:
        tf_from_pt_model.save_pretrained(self._local_dir						)
     del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

     SCREAMING_SNAKE_CASE_:	int            =tf_class.from_pretrained(self._local_dir						)
     SCREAMING_SNAKE_CASE_:	List[str]            =tf_model(**_lowerCamelCase    ,						output_hidden_states=_lowerCamelCase						)

     SCREAMING_SNAKE_CASE_:	int            =self.find_pt_tf_differences(_lowerCamelCase    ,						_lowerCamelCase						)
     SCREAMING_SNAKE_CASE_:	List[str]            ={k: v for k, v in conversion_differences.items() if """hidden""" not in k}
     SCREAMING_SNAKE_CASE_:	str            ={k: v for k, v in conversion_differences.items() if """hidden""" in k}
     if len(_lowerCamelCase						) == 0 and architectures is not None:
        raise ValueError(
            f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
            """ output was found. All outputs start with \'hidden\'"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max(output_differences.values()						) if output_differences else 0.0
     SCREAMING_SNAKE_CASE_:	int            =max(hidden_differences.values()						)
     if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
        raise ValueError(
            """The converted TensorFlow model has different outputs, something went wrong!\n"""
            + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
            + """\n""".join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]						)
            + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
            + """\n""".join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]						)						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""Update TF weights""" if self._new_weights else """Add TF weights"""
     if self._push:
        repo.git_add(auto_lfs_track=_lowerCamelCase						)
        repo.git_commit(_lowerCamelCase						)
        repo.git_push(blocking=_lowerCamelCase						)  # this prints a progress bar with the upload
        self._logger.warning(f'''TF weights pushed into {self._model_name}'''						)
     elif not self._no_pr:
        self._logger.warning("""Uploading the weights into a new PR..."""						)
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
            """Model converted by the [`transformers`\' `pt_to_tf`"""
            """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
            """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
            f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
            f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
            f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
            f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
        )
        if self._max_error > MAX_ERROR:
           commit_descrition += (
               f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
           )
        if self._extra_commit_description:
           commit_descrition += "\n\n" + self._extra_commit_description

        # sharded model -> adds all related files (index and .h5 shards)
        if os.path.exists(_lowerCamelCase						):
           SCREAMING_SNAKE_CASE_:	Tuple            =[
               CommitOperationAdd(path_in_repo=_lowerCamelCase    ,						path_or_fileobj=_lowerCamelCase						)
           ]
           for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""						):
              operations += [
                  CommitOperationAdd(path_in_repo=os.path.basename(_lowerCamelCase						)    ,						path_or_fileobj=_lowerCamelCase						)
              ]
        else:
           SCREAMING_SNAKE_CASE_:	int            =[CommitOperationAdd(path_in_repo=_lowerCamelCase    ,						path_or_fileobj=_lowerCamelCase						)]

        SCREAMING_SNAKE_CASE_:	str            =create_commit(
            repo_id=self._model_name    ,						operations=_lowerCamelCase    ,						commit_message=_lowerCamelCase    ,						commit_description=_lowerCamelCase    ,						repo_type="""model"""    ,						create_pr=_lowerCamelCase    ,						).pr_url
        self._logger.warning(f'''PR open in {hub_pr_url}'''						)








"""simple docstring"""
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class    a		:



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =vocab
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =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."""						)
     SCREAMING_SNAKE_CASE_:	Dict            =rjieba



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   NormalizedString						)       ->   List[NormalizedString]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[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(lowerCAmelCase						)    ,						hmm=lowerCAmelCase						):
        if token in self.vocab:
           splits.append(normalized_string[start:end]						)
        else:
           SCREAMING_SNAKE_CASE_:	List[Any]            =self.normalizers.normalize_str(lowerCAmelCase						).split()
           for token in token_list:
              if token:
                 SCREAMING_SNAKE_CASE_:	Dict            =start + len(lowerCAmelCase						)
                 splits.append(normalized_string[start:end]						)
                 SCREAMING_SNAKE_CASE_:	Optional[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 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   PreTokenizedString						)       ->   Tuple:


     '''simple docstring'''




     pretok.split(self.jieba_split						)



