




'''simple docstring'''






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase_       :   str   =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
UpperCAmelCase_       :   List[str]   =       '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'

class  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Tuple								=   tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir,"models/bert/"     )     )
          _lowerCamelCase				:   Any								=   self.transformer_dir
          shutil.copy(
              os.path.join(__A,"src/transformers/models/bert/modeling_bert.py"     ),os.path.join(self.transformer_dir,"models/bert/modeling_bert.py"     ),)



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   List[str]								=   "src/transformers"
          shutil.rmtree(self.transformer_dir     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       Dict,__A      :       List[Any],__A      :       Union[str, Any],__A      :       Any=None     ):
          _lowerCamelCase				:   List[Any]								=   comment + f'\nclass {class_name}(nn.Module):\n' + class_code
          if overwrite_result is not None:
                _lowerCamelCase				:   Union[str, Any]								=   comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
          _lowerCamelCase				:   Tuple								=   black.Mode(target_versions={black.TargetVersion.PYaa},line_length=1_1_9     )
          _lowerCamelCase				:   Union[str, Any]								=   black.format_str(__A,mode=__A     )
          _lowerCamelCase				:   Dict								=   os.path.join(self.transformer_dir,"new_code.py"     )
          with open(__A,"w",newline="\n"     ) as f:
                f.write(__A     )
          if overwrite_result is None:
                self.assertTrue(len(check_copies.is_copy_consistent(__A     )     ) == 0     )
          else:
                check_copies.is_copy_consistent(f.name,overwrite=__A     )
                with open(__A,"r"     ) as f:
                      self.assertTrue(f.read(),__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   List[str]								=   check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"     )
          self.assertEqual(__A,__A     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          # Base copy consistency
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead","BertLMPredictionHead",REFERENCE_CODE + "\n",)

          # With no empty line at the end
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead","BertLMPredictionHead",__A,)

          # Copy consistency with rename
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel","TestModelLMPredictionHead",re.sub("Bert","TestModel",__A     ),)

          # Copy consistency with a really long name
          _lowerCamelCase				:   Tuple								=   "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
          self.check_copy_consistency(
              f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}',f'{long_class_name}LMPredictionHead',re.sub("Bert",__A,__A     ),)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel","TestModelLMPredictionHead",__A,overwrite_result=re.sub("Bert","TestModel",__A     ),)



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Any								=   check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          _lowerCamelCase				:   int								=   (
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
              " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
              " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
              " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
              " Luong, Quoc V. Le, Christopher D. Manning."
          )
          _lowerCamelCase				:   Optional[Any]								=   (
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          _lowerCamelCase				:   List[str]								=   (
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
              " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
              " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
              " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
              " Christopher D. Manning 发布。\n"
          )

          _lowerCamelCase				:   Any								=   check_copies.convert_to_localized_md(
              __A,__A,localized_readme["format_model_list"]     )

          self.assertFalse(__A     )
          self.assertEqual(__A,__A     )

          _lowerCamelCase				:   Tuple								=   check_copies.convert_to_localized_md(
              __A,__A,localized_readme["format_model_list"]     )

          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(__A     )

          _lowerCamelCase				:   Tuple								=   (
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
          )
          _lowerCamelCase				:   Tuple								=   (
              "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
              " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          _lowerCamelCase				:   Optional[Any]								=   (
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )

          _lowerCamelCase				:   List[Any]								=   check_copies.convert_to_localized_md(
              __A,__A,localized_readme["format_model_list"]     )

          # Check if the model link is synchronized.
          self.assertEqual(__A,__A     )

'''simple docstring'''






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


UpperCAmelCase_       :   str   =       logging.get_logger(__name__)
UpperCAmelCase_       :   Optional[int]   =       {'vocab_file': 'spiece.model'}

UpperCAmelCase_       :   Any   =       {
    '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',
    }
}

UpperCAmelCase_       :   str   =       {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		['input_ids', 'attention_mask']



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

          _lowerCamelCase				:   int								=   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"     )
                _lowerCamelCase				:   Union[str, Any]								=   "None"

          # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
          _lowerCamelCase				:   Tuple								=   "<|endoftext|>" if eos_token is None else eos_token
          _lowerCamelCase				:   List[str]								=   "<unk>" if unk_token is None else unk_token
          if "gpt-sw3-7b" in name_or_path:
                _lowerCamelCase				:   Union[str, Any]								=   unk_token if pad_token is None else pad_token
                _lowerCamelCase				:   str								=   eos_token if bos_token is None else bos_token
          else:
                _lowerCamelCase				:   List[str]								=   "<pad>" if pad_token is None else pad_token
                _lowerCamelCase				:   str								=   "<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,)

          _lowerCamelCase				:   Union[str, Any]								=   do_lower_case
          _lowerCamelCase				:   List[Any]								=   remove_space
          _lowerCamelCase				:   str								=   keep_accents
          _lowerCamelCase				:   List[Any]								=   vocab_file

          _lowerCamelCase				:   int								=   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.Load(__A     )

          # Used for whitespace normalization in input texts
          # fmt : off
          _lowerCamelCase				:   Union[str, Any]								=   {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
          # fmt : on

          # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
          _lowerCamelCase				:   int								=   re.compile(
              f'[{"".join(map(__A,list(range(0,9     )     ) + list(range(1_1,3_2     )     ) + list(range(1_2_7,1_6_0     )     ) + [1_6_0, 1_7_3, 8_2_0_3]     )     )}]'     )



    def __getstate__(	self      :       Dict     ):
          _lowerCamelCase				:   int								=   self.__dict__.copy()
          _lowerCamelCase				:   Optional[Any]								=   None
          return state



    def __setstate__(	self      :       Tuple,__A      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   d

          # for backward compatibility
          if not hasattr(self,"sp_model_kwargs"     ):
                _lowerCamelCase				:   List[str]								=   {}

          _lowerCamelCase				:   Tuple								=   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   lowerCamelCase_  (	self      :       int     ):
          return len(self.sp_model     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       str     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.non_printing_characters_re.sub("",__A     )

          # Normalize whitespaces
          _lowerCamelCase				:   Optional[Any]								=   "".join([char if char not in self.whitespaces else " " for char in text]     )

          # NFC Unicode normalization
          _lowerCamelCase				:   Optional[Any]								=   unicodedata.normalize("NFC",__A     )
          return text



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       str,**__A      :       Optional[int]     ):
          _lowerCamelCase				:   str								=   self.preprocess_text(__A     )
          return self.sp_model.encode(__A,out_type=__A     )



    def   lowerCamelCase_  (	self      :       int,__A      :       str     ):
          return self.sp_model.PieceToId(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          return self.sp_model.IdToPiece(__A     )



    @staticmethod
    def   lowerCamelCase_  (	__A      :       str     ):
          return out_string



    def   lowerCamelCase_  (	self      :       str,__A      :       List[str]     ):
          _lowerCamelCase				:   str								=   []
          _lowerCamelCase				:   List[Any]								=   ""
          _lowerCamelCase				:   Tuple								=   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
                      _lowerCamelCase				:   Optional[int]								=   True
                      _lowerCamelCase				:   Optional[Any]								=   []
                else:
                      current_sub_tokens.append(__A     )
                      _lowerCamelCase				:   str								=   False
          out_string += self.sp_model.decode(__A     )

          return out_string



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   Optional[int]								=   {self.convert_ids_to_tokens(__A     ): i for i in range(self.vocab_size     )}
          vocab.update(self.added_tokens_encoder     )
          return vocab



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       str,__A      :       Optional[str] = None     ):
          if not os.path.isdir(__A     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
          _lowerCamelCase				:   List[Any]								=   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:
                      _lowerCamelCase				:   str								=   self.sp_model.serialized_model_proto()
                      fi.write(__A     )

          return (out_vocab_file,)



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       Union[str, List[str]],__A      :       Union[str, bool] = False     ):

          if isinstance(__A,__A     ):
                _lowerCamelCase				:   List[Any]								=   self.preprocess_text(__A     )
                _lowerCamelCase				:   Optional[Any]								=   self.sp_model.encode(__A     )
          else:
                _lowerCamelCase				:   List[str]								=   [self.preprocess_text(__A     ) for t in text]
                _lowerCamelCase				:   int								=   self.sp_model.encode(__A     )

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

          return token_ids



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       Union[int, List[int]]     ):
          return self.sp_model.decode(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       "Conversation"     ):
          _lowerCamelCase				:   Any								=   [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
          _lowerCamelCase				:   Tuple								=   (
              f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(__A     ) + f'{self.bos_token}Bot:'
          )
          return self.encode(text=__A     )