

'''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    			=						None


_lowerCAmelCase    			=						logging.get_logger(__name__)

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

_lowerCAmelCase    			=						{
    '''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    			=						{
    '''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    (    __a	):
 __SCREAMING_SNAKE_CASE     :Dict			       =     VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE     :Union[str, Any]			       =     PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE     :Tuple			       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE     :int			       =     ["""input_ids""", """attention_mask"""]
 __SCREAMING_SNAKE_CASE     :int			       =     XLMRobertaTokenizer



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

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

      __magic_name__						   =				vocab_file
      __magic_name__						   =				False if not self.vocab_file else True



 def  snake_case__  (    self		:    Any ,      a__		:    int ,      a__		:    Dict = None			):

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



 def  snake_case__  (    self		:    Tuple ,      a__		:    Any ,      a__		:    Tuple = None			):
      __magic_name__						   =				[self.sep_token_id]
      __magic_name__						   =				[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  snake_case__  (    self		:    str ,      a__		:    Dict ,      a__		:    Optional[int] = 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(UpperCAmelCase_			):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''			)
           return
      __magic_name__						   =				os.path.join(
          UpperCAmelCase_ ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

      return (out_vocab_file,)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__  :	Optional[Any]     ={'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =['''PegasusTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	int     =['''PegasusTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	str     =[
							    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''PegasusForCausalLM''',
							    '''PegasusForConditionalGeneration''',
							    '''PegasusModel''',
							    '''PegasusPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''TFPegasusForConditionalGeneration''',
							    '''TFPegasusModel''',
							    '''TFPegasusPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Dict     =[
							    '''FlaxPegasusForConditionalGeneration''',
							    '''FlaxPegasusModel''',
							    '''FlaxPegasusPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus import PegasusTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus_fast import PegasusTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus import (
														    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusForCausalLM,
														    PegasusForConditionalGeneration,
														    PegasusModel,
														    PegasusPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_pegasus import (
														    FlaxPegasusForConditionalGeneration,
														    FlaxPegasusModel,
														    FlaxPegasusPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__  :	Union[str, Any]     =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





