






# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase								=		{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		["EfficientNetImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		[
							    "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EfficientNetForImageClassification",
							    "EfficientNetModel",
							    "EfficientNetPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_efficientnet import (
							    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EfficientNetConfig,
							    EfficientNetOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_efficientnet import EfficientNetImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_efficientnet import (
														    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EfficientNetForImageClassification,
														    EfficientNetModel,
														    EfficientNetPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase								=		_LazyModule(__name__, globals()["__file__"], _import_structure)








"""simple docstring"""


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


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


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

    lowercase__       :       Dict				=						{} 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__       :       str				=						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__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[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 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						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__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


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


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

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

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       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,)