




'''simple docstring'''





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


UpperCamelCase_						:      Optional[Any]       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						{'''vocab_file''': '''spiece.model'''}

UpperCamelCase_						:      int       =						{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<sep>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="<cls>" ,_SCREAMING_SNAKE_CASE="<mask>" ,_SCREAMING_SNAKE_CASE=["<eop>", "<eod>"] ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      None:
          _snake_case							=				AddedToken(__UpperCAmelCase ,lstrip=__UpperCAmelCase ,rstrip=__UpperCAmelCase   ) if isinstance(__UpperCAmelCase ,__UpperCAmelCase   ) else mask_token

          _snake_case							=				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=__UpperCAmelCase ,remove_space=__UpperCAmelCase ,keep_accents=__UpperCAmelCase ,bos_token=__UpperCAmelCase ,eos_token=__UpperCAmelCase ,unk_token=__UpperCAmelCase ,sep_token=__UpperCAmelCase ,pad_token=__UpperCAmelCase ,cls_token=__UpperCAmelCase ,mask_token=__UpperCAmelCase ,additional_special_tokens=__UpperCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,**__UpperCAmelCase ,)

          _snake_case							=				3

          _snake_case							=				do_lower_case
          _snake_case							=				remove_space
          _snake_case							=				keep_accents
          _snake_case							=				vocab_file

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(__UpperCAmelCase   )

          try:
               import jieba
          except ModuleNotFoundError as error:
               raise error.__class__(
                   "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                   "See https://pypi.org/project/jieba/ for installation."   )
          _snake_case							=				jieba
          _snake_case							=				str.maketrans(" \n" ,"\u2582\u2583"   )






     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def     _lowercase     (			self   )							->      Optional[Any]:
          return len(self.sp_model   )






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				{self.convert_ids_to_tokens(__UpperCAmelCase   ): i for i in range(self.vocab_size   )}
          vocab.update(self.added_tokens_encoder   )
          return vocab






     def __getstate__(			self   )							->      Optional[int]:
          _snake_case							=				self.__dict__.copy()
          _snake_case							=				None
          return state






     def __setstate__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				d

          # for backward compatibility
          if not hasattr(self ,"sp_model_kwargs"   ):
               _snake_case							=				{}

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(self.vocab_file   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:


          if self.remove_space:
               _snake_case							=				" ".join(inputs.strip().split()   )
          else:
               _snake_case							=				inputs
          _snake_case							=				outputs.replace("``" ,"\""   ).replace("\'\'" ,"\""   )

          if not self.keep_accents:
               _snake_case							=				unicodedata.normalize("NFKD" ,__UpperCAmelCase   )
               _snake_case							=				"".join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase   )]   )
          if self.do_lower_case:
               _snake_case							=				outputs.lower()

          return outputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				self.preprocess_text(__UpperCAmelCase   )
          _snake_case							=				self.sp_model.encode(__UpperCAmelCase ,out_type=__UpperCAmelCase   )
          _snake_case							=				[]
          for piece in pieces:
               if len(__UpperCAmelCase   ) > 1 and piece[-1] == str(","   ) and piece[-2].isdigit():
                    _snake_case							=				self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase ,""   )   )
                    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]   ) == 1:
                              _snake_case							=				cur_pieces[1:]
                         else:
                              _snake_case							=				cur_pieces[0][1:]
                    cur_pieces.append(piece[-1]   )
                    new_pieces.extend(__UpperCAmelCase   )
               else:
                    new_pieces.append(__UpperCAmelCase   )

          return new_pieces






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Any:
          return self.sp_model.PieceToId(__UpperCAmelCase   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          return self.sp_model.IdToPiece(__UpperCAmelCase   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				"".join(__UpperCAmelCase   ).replace(__UpperCAmelCase ," "   ).strip()
          return out_string






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[self.cls_token_id]
          if token_ids_a is None:
               return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False   )							->      List[int]:


          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase ,token_ids_a=__UpperCAmelCase ,already_has_special_tokens=__UpperCAmelCase   )

          if token_ids_a is not None:
               return ([0] * len(__UpperCAmelCase   )) + [1] + ([0] * len(__UpperCAmelCase   )) + [1, 1]
          return ([0] * len(__UpperCAmelCase   )) + [1, 1]






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[2]

          if token_ids_a is None:
               return len(token_ids_a + sep   ) * [0] + cls_segment_id
          return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:


          if not os.path.isdir(__UpperCAmelCase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              __UpperCAmelCase ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(__UpperCAmelCase   ) and os.path.isfile(self.vocab_file   ):
               copyfile(self.vocab_file ,__UpperCAmelCase   )
          elif not os.path.isfile(self.vocab_file   ):
               with open(__UpperCAmelCase ,"wb"   ) as fi:
                    _snake_case							=				self.sp_model.serialized_model_proto()
                    fi.write(__UpperCAmelCase   )

          return (out_vocab_file,)






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				super()._decode(*__UpperCAmelCase ,**__UpperCAmelCase   )
          _snake_case							=				text.replace(" " ,""   ).replace("\u2582" ," "   ).replace("\u2583" ,"\n"   )
          return text








'''simple docstring'''





# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCamelCase_						:      int       =						'''pytorch_model.bin'''
UpperCamelCase_						:      str       =						'''pytorch_model.bin.index.json'''
UpperCamelCase_						:      int       =						'''adapter_config.json'''
UpperCamelCase_						:      str       =						'''adapter_model.bin'''
UpperCamelCase_						:      str       =						'''adapter_model.safetensors'''
UpperCamelCase_						:      List[Any]       =						'''tf_model.h5'''
UpperCamelCase_						:      Union[str, Any]       =						'''tf_model.h5.index.json'''
UpperCamelCase_						:      Tuple       =						'''model.ckpt'''
UpperCamelCase_						:      Union[str, Any]       =						'''flax_model.msgpack'''
UpperCamelCase_						:      Union[str, Any]       =						'''flax_model.msgpack.index.json'''
UpperCamelCase_						:      Dict       =						'''model.safetensors'''
UpperCamelCase_						:      List[Any]       =						'''model.safetensors.index.json'''
UpperCamelCase_						:      Tuple       =						'''config.json'''
UpperCamelCase_						:      List[str]       =						'''preprocessor_config.json'''
UpperCamelCase_						:      List[Any]       =						FEATURE_EXTRACTOR_NAME
UpperCamelCase_						:      Union[str, Any]       =						'''generation_config.json'''
UpperCamelCase_						:      str       =						'''modelcard.json'''

UpperCamelCase_						:      List[Any]       =						'''▁'''
UpperCamelCase_						:      Tuple       =						SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCamelCase_						:      Any       =						[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCamelCase_						:      Tuple       =						[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCamelCase_						:      str       =						[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			int:

     """simple docstring"""




     if version.parse(_UpperCamelCase   ) < version.parse(_UpperCamelCase   ):
          if "dev" in min_version:
               _snake_case							=				(
                   "This example requires a source install from HuggingFace Transformers (see "
                   "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
               )
          else:
               _snake_case							=				F"""This example requires a minimum version of {min_version},"""
          error_message += F""" but the version found is {__version__}.\n"""
          raise ImportError(
              error_message
              + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
              "versions of HuggingFace Transformers."   )


