

'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCamelCase :      Optional[Any]					 =     logging.get_logger(__name__)


lowerCamelCase :      Optional[int]					 =     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase :      Any					 =     {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

lowerCamelCase :      Any					 =     {
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}







class 							A__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				['input_ids', 'attention_mask']
  A__		 =				BartTokenizer





  def __init__(		self      :	List[Any]							,					_a      :	Any=None							,					_a      :	str=None							,					_a      :	Any=None							,					_a      :	Dict="replace"							,					_a      :	List[Any]="<s>"							,					_a      :	List[Any]="</s>"							,					_a      :	Optional[Any]="</s>"							,					_a      :	str="<s>"							,					_a      :	Optional[Any]="<unk>"							,					_a      :	Any="<pad>"							,					_a      :	List[str]="<mask>"							,					_a      :	Union[str, Any]=False							,					_a      :	List[Any]=True							,					**_a      :	Tuple							,					)	->    List[str]:





       '''simple docstring'''






       super().__init__(
           _a							,					_a							,					tokenizer_file=_a							,					errors=_a							,					bos_token=_a							,					eos_token=_a							,					sep_token=_a							,					cls_token=_a							,					unk_token=_a							,					pad_token=_a							,					mask_token=_a							,					add_prefix_space=_a							,					trim_offsets=_a							,					**_a							,					)

       _SCREAMING_SNAKE_CASE							    =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get('add_prefix_space'							,					_a ) != add_prefix_space:
            _SCREAMING_SNAKE_CASE							    =getattr(_a							,					pre_tok_state.pop('type' ) )
            _SCREAMING_SNAKE_CASE							    =add_prefix_space
            _SCREAMING_SNAKE_CASE							    =pre_tok_class(**_a )

       _SCREAMING_SNAKE_CASE							    =add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       _SCREAMING_SNAKE_CASE							    ='post_processor'
       _SCREAMING_SNAKE_CASE							    =getattr(self.backend_tokenizer							,					_a							,					_a )
       if tokenizer_component_instance:
            _SCREAMING_SNAKE_CASE							    =json.loads(tokenizer_component_instance.__getstate__() )

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                 _SCREAMING_SNAKE_CASE							    =tuple(state['sep'] )
            if "cls" in state:
                 _SCREAMING_SNAKE_CASE							    =tuple(state['cls'] )

            _SCREAMING_SNAKE_CASE							    =False

            if state.get('add_prefix_space'							,					_a ) != add_prefix_space:
                 _SCREAMING_SNAKE_CASE							    =add_prefix_space
                 _SCREAMING_SNAKE_CASE							    =True

            if state.get('trim_offsets'							,					_a ) != trim_offsets:
                 _SCREAMING_SNAKE_CASE							    =trim_offsets
                 _SCREAMING_SNAKE_CASE							    =True

            if changes_to_apply:
                 _SCREAMING_SNAKE_CASE							    =getattr(_a							,					state.pop('type' ) )
                 _SCREAMING_SNAKE_CASE							    =component_class(**_a )
                 setattr(self.backend_tokenizer							,					_a							,					_a )





  @property
  def 						A  (		self      :	Tuple )	->    str:





       '''simple docstring'''






       if self._mask_token is None:
            if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.' )
            return None
       return str(self._mask_token )





  @mask_token.setter
  def 						A  (		self      :	List[str]							,					_a      :	Union[str, Any] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else value
       _SCREAMING_SNAKE_CASE							    =value





  def 						A  (		self      :	Dict							,					*_a      :	int							,					**_a      :	str )	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.get('is_split_into_words'							,					_a )

       if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                'to use it with pretokenized inputs.' )

       return super()._batch_encode_plus(*_a							,					**_a )





  def 						A  (		self      :	Optional[int]							,					*_a      :	Any							,					**_a      :	str )	->    BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =kwargs.get('is_split_into_words'							,					_a )

       if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                'to use it with pretokenized inputs.' )

       return super()._encode_plus(*_a							,					**_a )





  def 						A  (		self      :	List[str]							,					_a      :	str							,					_a      :	Optional[str] = None )	->    Tuple[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._tokenizer.model.save(_a							,					name=_a )
       return tuple(_a )





  def 						A  (		self      :	Tuple							,					_a      :	str							,					_a      :	int=None )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def 						A  (		self      :	Dict							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.sep_token_id]
       _SCREAMING_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]



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
