





# 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,
)


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

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

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Dict:
  if version.parse(snake_case__     ) < version.parse(snake_case__     ):
    if "dev" in min_version:
      _lowercase							= (
          'This example requires a source install from HuggingFace Transformers (see '
          '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
      )
    else:
      _lowercase							= 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.'     )






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


snake_case 				=       logging.get_logger(__name__)
snake_case 				=       {"""vocab_file""": """spiece.model"""}

snake_case 				=       {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

snake_case 				=       {
    """AI-Sweden/gpt-sw3-126m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : str      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : List[Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  ['''input_ids''', '''attention_mask''']







       def __init__(    self	:	Any						,__A	:	Tuple						,__A	:	Any=False						,__A	:	int=False						,__A	:	List[str]=False						,__A	:	List[str]=None						,__A	:	Dict=None						,__A	:	Dict=None						,__A	:	Union[str, Any]=None						,__A	:	Optional[Dict[str, Any]] = None						,**__A	:	Tuple						,)						->					None:
         _lowercase							= {} if sp_model_kwargs is None else sp_model_kwargs

         _lowercase							= kwargs.get('name_or_path'  )
         if name_or_path is None:
           logger.warning(
               'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
               ' you are testing the model, this can safely be ignored'  )
           _lowercase							= 'None'

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         _lowercase							= '<|endoftext|>' if eos_token is None else eos_token
         _lowercase							= '<unk>' if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
           _lowercase							= unk_token if pad_token is None else pad_token
           _lowercase							= eos_token if bos_token is None else bos_token
         else:
           _lowercase							= '<pad>' if pad_token is None else pad_token
           _lowercase							= '<s>' if bos_token is None else bos_token

         super().__init__(
             do_lower_case=__A						,remove_space=__A						,keep_accents=__A						,bos_token=__A						,eos_token=__A						,unk_token=__A						,pad_token=__A						,sp_model_kwargs=self.sp_model_kwargs						,**__A						,)

         _lowercase							= do_lower_case
         _lowercase							= remove_space
         _lowercase							= keep_accents
         _lowercase							= vocab_file

         _lowercase							= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(__A  )

         # Used for whitespace normalization in input texts
         # fmt : off
         _lowercase							= {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         _lowercase							= re.compile(
             F"""[{"".join(map(__A						,list(range(0						,9  )  ) + list(range(11						,32  )  ) + list(range(127						,160  )  ) + [160, 173, 8203]  )  )}]"""  )







       def __getstate__(    self	:	List[Any]  )						->					List[str]:
         _lowercase							= self.__dict__.copy()
         _lowercase							= None
         return state







       def __setstate__(    self	:	Optional[Any]						,__A	:	Dict  )						->					str:
         _lowercase							= d

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

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







       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         return len(self.sp_model  )







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str  )						->					str:
         _lowercase							= self.non_printing_characters_re.sub(''						,__A  )

         # Normalize whitespaces
         _lowercase							= ''.join([char if char not in self.whitespaces else ' ' for char in text]  )

         # NFC Unicode normalization
         _lowercase							= unicodedata.normalize('NFC'						,__A  )
         return text







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str						,**__A	:	Optional[int]  )						->					List[str]:
         _lowercase							= self.preprocess_text(__A  )
         return self.sp_model.encode(__A						,out_type=__A  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	str  )						->					int:
         return self.sp_model.PieceToId(__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int  )						->					str:
         return self.sp_model.IdToPiece(__A  )







       @staticmethod
       def    __UpperCAmelCase (    __A	:	str  )						->					str:
         return out_string







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	List[str]  )						->					str:
         _lowercase							= []
         _lowercase							= ''
         _lowercase							= False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
             if not prev_is_special:
               out_string += " "

             out_string += self.sp_model.decode(__A  ) + token
             _lowercase							= True
             _lowercase							= []
           else:
             current_sub_tokens.append(__A  )
             _lowercase							= False
         out_string += self.sp_model.decode(__A  )

         return out_string







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Dict[str, int]:
         _lowercase							= {self.convert_ids_to_tokens(__A  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str						,__A	:	Optional[str] = None  )						->					Tuple[str]:
         if not os.path.isdir(__A  ):
           logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
           return
         _lowercase							= 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  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file						,__A  )
         elif not os.path.isfile(self.vocab_file  ):
           with open(__A						,'wb'  ) as fi:
             _lowercase							= self.sp_model.serialized_model_proto()
             fi.write(__A  )

         return (out_vocab_file,)







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, List[str]]						,__A	:	Union[str, bool] = False  )						->					Union[List[int], List[List[int]], "torch.Tensor"]:

         if isinstance(__A						,__A  ):
           _lowercase							= self.preprocess_text(__A  )
           _lowercase							= self.sp_model.encode(__A  )
         else:
           _lowercase							= [self.preprocess_text(__A  ) for t in text]
           _lowercase							= self.sp_model.encode(__A  )

         if return_tensors is True or return_tensors == "pt":
           _lowercase							= torch.tensor(__A  )

         return token_ids







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Union[int, List[int]]  )						->					str:
         return self.sp_model.decode(__A  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	"Conversation"  )						->					List[int]:
         _lowercase							= [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         _lowercase							= (
             F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__A  ) + F"""{self.bos_token}Bot:"""
         )
         return self.encode(text=__A  )