





"""simple docstring"""




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:
   lowerCamelCase_	:		Dict         =   None


lowerCamelCase_	:		List[Any]         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[str]         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase_	:		Dict         =   {
    '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'
        ),
    },
}

lowerCamelCase_	:		Dict         =   {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  VOCAB_FILES_NAMES
      lowercase_ :					str             =  PRETRAINED_VOCAB_FILES_MAP
      lowercase_ :					Dict             =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase_ :					List[Any]             =  ["""input_ids""", """attention_mask"""]
      lowercase_ :					Union[str, Any]             =  XLMRobertaTokenizer

      def __init__(	self   , snake_case_=None   , snake_case_=None   , snake_case_="<s>"   , snake_case_="</s>"   , snake_case_="</s>"   , snake_case_="<s>"   , snake_case_="<unk>"   , snake_case_="<pad>"   , snake_case_="<mask>"   , **snake_case_   , ):

        """simple docstring"""

        A_    :			List[Any]									=    AddedToken(snake_case_   , lstrip=snake_case_   , rstrip=snake_case_    ) if isinstance(snake_case_   , snake_case_    ) else mask_token

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

        A_    :			Any									=    vocab_file
        A_    :			str									=    False if not self.vocab_file else True

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        A_    :			int									=    [self.sep_token_id]
        A_    :			str									=    [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    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        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(snake_case_    ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""    )
          return
        A_    :			Tuple									=    os.path.join(
            snake_case_   , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

        return (out_vocab_file,)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_					:			Any     						=				{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					lowerCamelCase_					:			Optional[Any]     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
