






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__							=     {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__							=     {
    """google/rembert""": 256,
}




class 			snake_case__   (							lowercase_):

							'''simple docstring'''
							lowerCamelCase							:				Union[str, Any] 				= VOCAB_FILES_NAMES
							lowerCamelCase							:				List[str] 				= PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase							:				Any 				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



							def __init__(							self			,						a__			,						a__=False			,						a__=True			,						a__=True			,						a__="[CLS]"			,						a__="[SEP]"			,						a__="[UNK]"			,						a__="[SEP]"			,						a__="[PAD]"			,						a__="[CLS]"			,						a__="[MASK]"			,						**a__			,						) ->      Tuple:






												'''simple docstring'''





												super().__init__(
												    do_lower_case=a__			,						remove_space=a__			,						keep_accents=a__			,						bos_token=a__			,						eos_token=a__			,						unk_token=a__			,						sep_token=a__			,						pad_token=a__			,						cls_token=a__			,						mask_token=a__			,						**a__			,						)

												__snake_case			:Dict          =						do_lower_case
												__snake_case			:int          =						remove_space
												__snake_case			:Any          =						keep_accents
												__snake_case			:Optional[int]          =						vocab_file

												__snake_case			:Optional[int]          =						spm.SentencePieceProcessor()
												self.sp_model.Load(a__    )



							@property
							def 					__lowercase						(							self    ) ->      List[Any]:






												'''simple docstring'''





												return len(self.sp_model    )



							def 					__lowercase						(							self    ) ->      Optional[int]:






												'''simple docstring'''





												__snake_case			:Any          =						{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
												vocab.update(self.added_tokens_encoder    )
												return vocab



							def __getstate__(							self    ) ->      Any:






												'''simple docstring'''





												__snake_case			:List[str]          =						self.__dict__.copy()
												__snake_case			:Any          =						None
												return state



							def __setstate__(							self			,						a__    ) ->      str:






												'''simple docstring'''





												__snake_case			:Tuple          =						d
												__snake_case			:int          =						spm.SentencePieceProcessor()
												self.sp_model.Load(self.vocab_file    )



							def 					__lowercase						(							self			,						a__			,						a__=False    ) ->      str:






												'''simple docstring'''





												__snake_case			:List[Any]          =						self.sp_model.EncodeAsPieces(a__    )
												return pieces



							def 					__lowercase						(							self			,						a__    ) ->      Union[str, Any]:






												'''simple docstring'''





												return self.sp_model.PieceToId(a__    )



							def 					__lowercase						(							self			,						a__    ) ->      Union[str, Any]:






												'''simple docstring'''





												return self.sp_model.IdToPiece(a__    )



							def 					__lowercase						(							self			,						a__    ) ->      Union[str, Any]:






												'''simple docstring'''





												__snake_case			:Tuple          =						self.sp_model.decode_pieces(a__    )
												return out_string



							def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      Optional[int]:






												'''simple docstring'''





												__snake_case			:Union[str, Any]          =						[self.sep_token_id]
												__snake_case			:int          =						[self.cls_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep



							def 					__lowercase						(							self			,						a__			,						a__ = None			,						a__ = False    ) ->      Optional[Any]:






												'''simple docstring'''





												if already_has_special_tokens:
																	if token_ids_a is not None:
																						raise ValueError(
																						    """You should not supply a second sequence if the provided sequence of """
																						    """ids is already formatted with special tokens for the model."""    )
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



							def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      List[Any]:






												'''simple docstring'''





												__snake_case			:Dict          =						[self.sep_token_id]
												__snake_case			:int          =						[self.cls_token_id]

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



							def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      List[str]:






												'''simple docstring'''





												if not os.path.isdir(a__    ):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(a__    )    )
																	return
												__snake_case			:int          =						os.path.join(
												    a__			,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

												if os.path.abspath(self.vocab_file    ) != os.path.abspath(a__    ):
																	copyfile(self.vocab_file			,						a__    )

												return (out_vocab_file,)










# 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,
)


lowerCamelCase__							=     """pytorch_model.bin"""
lowerCamelCase__							=     """pytorch_model.bin.index.json"""
lowerCamelCase__							=     """adapter_config.json"""
lowerCamelCase__							=     """adapter_model.bin"""
lowerCamelCase__							=     """adapter_model.safetensors"""
lowerCamelCase__							=     """tf_model.h5"""
lowerCamelCase__							=     """tf_model.h5.index.json"""
lowerCamelCase__							=     """model.ckpt"""
lowerCamelCase__							=     """flax_model.msgpack"""
lowerCamelCase__							=     """flax_model.msgpack.index.json"""
lowerCamelCase__							=     """model.safetensors"""
lowerCamelCase__							=     """model.safetensors.index.json"""
lowerCamelCase__							=     """config.json"""
lowerCamelCase__							=     """preprocessor_config.json"""
lowerCamelCase__							=     FEATURE_EXTRACTOR_NAME
lowerCamelCase__							=     """generation_config.json"""
lowerCamelCase__							=     """modelcard.json"""

lowerCamelCase__							=     """▁"""
lowerCamelCase__							=     SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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






def   UpperCamelCase  (       snake_case__     :   Dict  ):




     '''simple docstring'''






     if version.parse(snake_case__  ) < version.parse(snake_case__  ):
          if "dev" in min_version:
               __snake_case			:Tuple          =						(
                   """This example requires a source install from HuggingFace Transformers (see """
                   """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
               )
          else:
               __snake_case			:List[str]          =						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."""  )


