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


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

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

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

A   :     Optional[int]       =				{
    "camembert-base": 5_12,
}

A   :     Tuple       =				"▁"


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  VOCAB_FILES_NAMES
  lowerCamelCase__   =  PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase__   =  ['''input_ids''', '''attention_mask''']







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

        SCREAMING_SNAKE_CASE_      =     {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__magic_name__     ,   eos_token=__magic_name__     ,   unk_token=__magic_name__     ,   sep_token=__magic_name__     ,   cls_token=__magic_name__     ,   pad_token=__magic_name__     ,   mask_token=__magic_name__     ,   additional_special_tokens=__magic_name__     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(str(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     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>
        SCREAMING_SNAKE_CASE_      =     {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
        SCREAMING_SNAKE_CASE_      =     len(self.fairseq_tokens_to_ids      )
        SCREAMING_SNAKE_CASE_      =     len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
        SCREAMING_SNAKE_CASE_      =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None      )     ->		List[int]:

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







  def       __A   (							self       :				Dict     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None     ,   __magic_name__       :				bool = False      )     ->		List[int]:
        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__magic_name__     ,   token_ids_a=__magic_name__     ,   already_has_special_tokens=__magic_name__      )

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







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None      )     ->		List[int]:
        SCREAMING_SNAKE_CASE_      =     [self.sep_token_id]
        SCREAMING_SNAKE_CASE_      =     [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       __A   (							self       :				str      )     ->		Optional[int]:
        return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )







  def       __A   (							self       :				Optional[int]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     {self.convert_ids_to_tokens(__magic_name__      ): i for i in range(self.vocab_size      )}
        vocab.update(self.added_tokens_encoder      )
        return vocab







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				str      )     ->		List[str]:
        return self.sp_model.encode(__magic_name__     ,   out_type=__magic_name__      )







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







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		Tuple:
        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       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				List[Any]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     ""
        SCREAMING_SNAKE_CASE_      =     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(__magic_name__      ) + token
                    SCREAMING_SNAKE_CASE_      =     True
                    SCREAMING_SNAKE_CASE_      =     []
              else:
                    current_sub_tokens.append(__magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     False
        out_string += self.sp_model.decode(__magic_name__      )
        return out_string.strip()







  def __getstate__(							self       :				Union[str, Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.__dict__.copy()
        SCREAMING_SNAKE_CASE_      =     None
        return state







  def __setstate__(							self       :				List[str]     ,   __magic_name__       :				int      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     d

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

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







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

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

        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"      )


