





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Tuple			 =	['image_processor', 'tokenizer']
   _UpperCamelCase   :		Union[str, Any]			 =	'BlipImageProcessor'
   _UpperCamelCase   :		Union[str, Any]			 =	('BertTokenizer', 'BertTokenizerFast')


   def __init__(   self      :    List[Any]      ,					a      :    int      ,					a      :    List[Any]	)->       Any:





     """simple docstring"""







     lowercase__           =  False
     super().__init__(a      ,					a	)
     lowercase__           =  self.image_processor


   def __call__(   self      :    int      ,					a      :    ImageInput = None      ,					a      :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,					a      :    bool = True      ,					a      :    Union[bool, str, PaddingStrategy] = False      ,					a      :    Union[bool, str, TruncationStrategy] = None      ,					a      :    Optional[int] = None      ,					a      :    int = 0      ,					a      :    Optional[int] = None      ,					a      :    Optional[bool] = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = True      ,					a      :    Optional[Union[str, TensorType]] = None      ,					**a      :    Tuple      ,					)->       BatchEncoding:





     """simple docstring"""







     if images is None and text is None:
       raise ValueError('You have to specify either images or text.'	)

     # Get only text
     if images is None:
       lowercase__           =  self.tokenizer
       lowercase__           =  self.tokenizer(
           text=a      ,					add_special_tokens=a      ,					padding=a      ,					truncation=a      ,					max_length=a      ,					stride=a      ,					pad_to_multiple_of=a      ,					return_attention_mask=a      ,					return_overflowing_tokens=a      ,					return_special_tokens_mask=a      ,					return_offsets_mapping=a      ,					return_token_type_ids=a      ,					return_length=a      ,					verbose=a      ,					return_tensors=a      ,					**a      ,					)
       return text_encoding

     # add pixel_values
     lowercase__           =  self.image_processor(a      ,					return_tensors=a	)

     if text is not None:
       lowercase__           =  self.tokenizer(
           text=a      ,					add_special_tokens=a      ,					padding=a      ,					truncation=a      ,					max_length=a      ,					stride=a      ,					pad_to_multiple_of=a      ,					return_attention_mask=a      ,					return_overflowing_tokens=a      ,					return_special_tokens_mask=a      ,					return_offsets_mapping=a      ,					return_token_type_ids=a      ,					return_length=a      ,					verbose=a      ,					return_tensors=a      ,					**a      ,					)
     else:
       lowercase__           =  None

     if text_encoding is not None:
       encoding_image_processor.update(a	)

     return encoding_image_processor


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					*a      :    List[str]      ,					**a      :    Optional[Any]	)->       Optional[Any]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					*a      :    Union[str, Any]      ,					**a      :    Tuple	)->       Optional[Any]:





     """simple docstring"""







     return self.tokenizer.decode(*a      ,					**a	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  self.tokenizer.model_input_names
     lowercase__           =  self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)









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_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_          =		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Dict			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Dict			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Dict			 =	['input_ids', 'attention_mask']
   _UpperCamelCase   :		Optional[int]			 =	CamembertTokenizer


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





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





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

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

     return (out_vocab_file,)


