
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_xlm_roberta import XLMRobertaTokenizer
else:
     __magic_name__           =					None


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__           =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

__magic_name__           =					{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Dict      		=       VOCAB_FILES_NAMES
      _A  :						List[str]      		=       PRETRAINED_VOCAB_FILES_MAP
      _A  :						int      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A  :						List[str]      		=       ['input_ids', 'attention_mask']
      _A  :						str      		=       XLMRobertaTokenizer
      def __init__(     self						,		lowerCamelCase=None						,		lowerCamelCase=None						,		lowerCamelCase="<s>"						,		lowerCamelCase="</s>"						,		lowerCamelCase="</s>"						,		lowerCamelCase="<s>"						,		lowerCamelCase="<unk>"						,		lowerCamelCase="<pad>"						,		lowerCamelCase="<mask>"						,		**lowerCamelCase						,		):
          # Mask token behave like a normal word, i.e. include the space before it
          snake_case__  =			AddedToken(lowerCamelCase						,		lstrip=lowerCamelCase						,		rstrip=lowerCamelCase	) if isinstance(lowerCamelCase						,		lowerCamelCase	) else mask_token

          super().__init__(
              lowerCamelCase						,		tokenizer_file=lowerCamelCase						,		bos_token=lowerCamelCase						,		eos_token=lowerCamelCase						,		sep_token=lowerCamelCase						,		cls_token=lowerCamelCase						,		unk_token=lowerCamelCase						,		pad_token=lowerCamelCase						,		mask_token=lowerCamelCase						,		**lowerCamelCase						,		)

          snake_case__  =			vocab_file
          snake_case__  =			False if not self.vocab_file else True
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):

          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          snake_case__  =			[self.cls_token_id]
          snake_case__  =			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):
          snake_case__  =			[self.sep_token_id]
          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]



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."	)

          if not os.path.isdir(lowerCamelCase	):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""	)
              return
          snake_case__  =			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	):
              copyfile(self.vocab_file						,		lowerCamelCase	)

          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,
)


__magic_name__           =					{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

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



