




import os
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 logging


_UpperCAmelCase		:  List[str]	=   logging.get_logger(__name__)

_UpperCAmelCase		:  int	=   {"vocab_file": "sentencepiece.bpe.model"}

_UpperCAmelCase		:  Optional[Any]	=   {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

_UpperCAmelCase		:  Optional[int]	=   {
    "camembert-base": 512,
}

_UpperCAmelCase		:  Tuple	=   "▁"




class       __lowerCAmelCase				(							SCREAMING_SNAKE_CASE__):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a           =  ['''input_ids''', '''attention_mask''']






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Union[str, Any]="<s>"				,    _lowerCAmelCase:    Any="</s>"				,    _lowerCAmelCase:    Optional[Any]="</s>"				,    _lowerCAmelCase:    List[Any]="<s>"				,    _lowerCAmelCase:    List[str]="<unk>"				,    _lowerCAmelCase:    Optional[Any]="<pad>"				,    _lowerCAmelCase:    Dict="<mask>"				,    _lowerCAmelCase:    Any=["<s>NOTUSED", "</s>NOTUSED"]				,    _lowerCAmelCase:    Optional[Dict[str, Any]] = None				,    **_lowerCAmelCase:    Any				,    ):
         # Mask token behave like a normal word, i.e. include the space before it
         lowercase			:List[Any]							=  AddedToken(_lowerCAmelCase				,    lstrip=_lowerCAmelCase				,    rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ) else mask_token

         lowercase			:str							=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase				,    eos_token=_lowerCAmelCase				,    unk_token=_lowerCAmelCase				,    sep_token=_lowerCAmelCase				,    cls_token=_lowerCAmelCase				,    pad_token=_lowerCAmelCase				,    mask_token=_lowerCAmelCase				,    additional_special_tokens=_lowerCAmelCase				,    sp_model_kwargs=self.sp_model_kwargs				,    **_lowerCAmelCase				,    )
         lowercase			:Optional[int]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(_lowerCAmelCase ) )
         lowercase			:Dict							=  vocab_file
         # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
         # sentencepiece vocabulary (this is the case for <s> and </s>
         lowercase			:Tuple							=  {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
         lowercase			:Optional[int]							=  len(self.fairseq_tokens_to_ids )
         lowercase			:Dict							=  len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
         lowercase			:str							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase			:str							=  [self.cls_token_id]
         lowercase			:Tuple							=  [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None				,    _lowerCAmelCase:    bool = False ):
         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_lowerCAmelCase				,    token_ids_a=_lowerCAmelCase				,    already_has_special_tokens=_lowerCAmelCase )

         if token_ids_a is None:
           return [1] + ([0] * len(_lowerCAmelCase )) + [1]
         return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase )) + [1]






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):
         lowercase			:int							=  [self.sep_token_id]
         lowercase			:Optional[Any]							=  [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 + sep + token_ids_a + sep ) * [0]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Tuple							=  {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    str ):
         return self.sp_model.encode(_lowerCAmelCase				,    out_type=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    List[Any] ):
         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(_lowerCAmelCase ) == 0:
           # Convert sentence piece unk token to fairseq unk token index
           return self.unk_token_id
         return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Union[str, Any] ):
         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    List[Any] ):
         lowercase			:Tuple							=  []
         lowercase			:Tuple							=  ""
         lowercase			:Dict							=  False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             if not prev_is_special:
               out_string += " "
             out_string += self.sp_model.decode(_lowerCAmelCase ) + token
             lowercase			:Any							=  True
             lowercase			:int							=  []
           else:
             current_sub_tokens.append(_lowerCAmelCase )
             lowercase			:Union[str, Any]							=  False
         out_string += self.sp_model.decode(_lowerCAmelCase )
         return out_string.strip()






       def __getstate__(      self:    Union[str, Any] ):
         lowercase			:Optional[int]							=  self.__dict__.copy()
         lowercase			:List[str]							=  None
         return state






       def __setstate__(      self:    List[str]				,    _lowerCAmelCase:    int ):
         lowercase			:List[Any]							=  d

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

         lowercase			:int							=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):
         if not os.path.isdir(_lowerCAmelCase ):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
           return
         lowercase			:Union[str, Any]							=  os.path.join(
             _lowerCAmelCase				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

         return (out_vocab_file,)




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A   :     Tuple       =				get_tests_dir("fixtures")


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Union[str, Any]:
        # A mock response for an HTTP head request to emulate server down
        SCREAMING_SNAKE_CASE_      =     mock.Mock()
        SCREAMING_SNAKE_CASE_      =     500
        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     HTTPError
        SCREAMING_SNAKE_CASE_      =     {}

        # Download this model to make sure it's in the cache.
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch("requests.Session.request"     ,   return_value=__magic_name__      ) as mock_head:
              SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
              # This check we did call the fake head request
              mock_head.assert_called()







  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        # This test is for deprecated behavior and can be removed in v5
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(
            "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"      )



@is_staging_test
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  @classmethod
  def       __A   (							cls       :				Any      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     TOKEN
        HfFolder.save_token(__magic_name__      )







  @classmethod
  def       __A   (							cls       :				int      )     ->		Optional[Any]:
        try:
              delete_repo(token=cls._token     ,   repo_id="test-feature-extractor"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="valid_org/test-feature-extractor-org"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="test-dynamic-feature-extractor"      )
        except HTTPError:
              pass







  def       __A   (							self       :				str      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="test-feature-extractor"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				Dict      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("valid_org/test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="valid_org/test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="valid_org/test-feature-extractor-org"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        CustomFeatureExtractor.register_for_auto_class()
        SCREAMING_SNAKE_CASE_      =     CustomFeatureExtractor.from_pretrained(__magic_name__      )

        feature_extractor.push_to_hub("test-dynamic-feature-extractor"     ,   use_auth_token=self._token      )

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map     ,   {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}     ,   )

        SCREAMING_SNAKE_CASE_      =     AutoFeatureExtractor.from_pretrained(
            F'''{USER}/test-dynamic-feature-extractor'''     ,   trust_remote_code=__magic_name__      )
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__     ,   "CustomFeatureExtractor"      )


