
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_rembert import RemBertTokenizer
else:
 _snake_case      = None

_snake_case      = logging.get_logger(__name__)
_snake_case      = {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_snake_case      = {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

_snake_case      = {
    '''google/rembert''': 256,
}

_snake_case      = '''▁'''
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_				:					int       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_				:					Tuple       						=RemBertTokenizer

 def __init__(						self			:			int					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=None					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=None					, SCREAMING_SNAKE_CASE__			:			str=True					, SCREAMING_SNAKE_CASE__			:			Dict=True					, SCREAMING_SNAKE_CASE__			:			Any=False					, SCREAMING_SNAKE_CASE__			:			List[Any]="[CLS]"					, SCREAMING_SNAKE_CASE__			:			Dict="[SEP]"					, SCREAMING_SNAKE_CASE__			:			List[Any]="<unk>"					, SCREAMING_SNAKE_CASE__			:			str="[SEP]"					, SCREAMING_SNAKE_CASE__			:			List[str]="<pad>"					, SCREAMING_SNAKE_CASE__			:			str="[CLS]"					, SCREAMING_SNAKE_CASE__			:			Optional[Any]="[MASK]"					, **SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					AddedToken(SCREAMING_SNAKE_CASE__					, lstrip=SCREAMING_SNAKE_CASE__					, rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) else mask_token

      super().__init__(
          SCREAMING_SNAKE_CASE__					, tokenizer_file=SCREAMING_SNAKE_CASE__					, do_lower_case=SCREAMING_SNAKE_CASE__					, remove_space=SCREAMING_SNAKE_CASE__					, keep_accents=SCREAMING_SNAKE_CASE__					, bos_token=SCREAMING_SNAKE_CASE__					, eos_token=SCREAMING_SNAKE_CASE__					, unk_token=SCREAMING_SNAKE_CASE__					, sep_token=SCREAMING_SNAKE_CASE__					, pad_token=SCREAMING_SNAKE_CASE__					, cls_token=SCREAMING_SNAKE_CASE__					, mask_token=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					do_lower_case
      UpperCamelCase						    =					remove_space
      UpperCamelCase						    =					keep_accents
      UpperCamelCase						    =					vocab_file
      UpperCamelCase						    =					False if not self.vocab_file else True

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			List[int]					, SCREAMING_SNAKE_CASE__			:			Optional[List[int]] = None   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			List[int]					, SCREAMING_SNAKE_CASE__			:			Optional[List[int]] = None					, SCREAMING_SNAKE_CASE__			:			bool = False   ):






      """simple docstring"""



      if already_has_special_tokens:
           if token_ids_a is not None:
                raise ValueError(
                    'You should not supply a second sequence if the provided sequence of '
                    'ids is already formatted with special tokens for the model.'   )
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is not None:
           return [1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1]
      return [1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1]

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[int]					, SCREAMING_SNAKE_CASE__			:			Optional[List[int]] = None   ):






      """simple docstring"""



      UpperCamelCase						    =					[self.sep_token_id]
      UpperCamelCase						    =					[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   ) * [0] + len(token_ids_a + sep   ) * [1]






 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Optional[str] = None   ):






      """simple docstring"""



      if not os.path.isdir(SCREAMING_SNAKE_CASE__   ):
           logger.error('Vocabulary path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE__   )   )
           return
      UpperCamelCase						    =					os.path.join(
          SCREAMING_SNAKE_CASE__					, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(SCREAMING_SNAKE_CASE__   ):
           copyfile(self.vocab_file					, SCREAMING_SNAKE_CASE__   )

      return (out_vocab_file,)




# 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


_snake_case      = {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = [
     '''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

 _snake_case      = _LazyModule(__name__, globals()['''__file__'''], _import_structure)


