

'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase :      str					 =     logging.get_logger(__name__)







class 							A__    (       A__  ):
  A__		 =				['input_ids', 'attention_mask']





  def __init__(		self      :	Any							,					_a      :	List[str]="</s>"							,					_a      :	Optional[int]="<unk>"							,					_a      :	Optional[Any]="<pad>"							,					_a      :	Optional[int]=125							,					_a      :	Optional[Any]=None							,					**_a      :	Optional[Any]							,					)	->    None:





       '''simple docstring'''






       if extra_ids > 0 and additional_special_tokens is None:
            _SCREAMING_SNAKE_CASE							    =[f"<extra_id_{i}>" for i in range(_a )]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            _SCREAMING_SNAKE_CASE							    =len(set(filter(lambda _a : bool('extra_id' in str(_a ) )							,					_a ) ) )
            if extra_tokens != extra_ids:
                 raise ValueError(
                     f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                     ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
                     ' extra_ids tokens' )

       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else pad_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else eos_token
       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else unk_token

       super().__init__(
           eos_token=_a							,					unk_token=_a							,					pad_token=_a							,					extra_ids=_a							,					additional_special_tokens=_a							,					**_a							,					)

       _SCREAMING_SNAKE_CASE							    =extra_ids

       _SCREAMING_SNAKE_CASE							    =2**8  # utf is 8 bits

       # define special tokens dict
       _SCREAMING_SNAKE_CASE							    ={
           self.pad_token: 0,
           self.eos_token: 1,
           self.unk_token: 2,
       }
       _SCREAMING_SNAKE_CASE							    =len(self.special_tokens_encoder )
       _SCREAMING_SNAKE_CASE							    =len(_a )
       for i, token in enumerate(_a ):
            _SCREAMING_SNAKE_CASE							    =self.vocab_size + i - n
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.special_tokens_encoder.items()}





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





       '''simple docstring'''






       return self._utf_vocab_size + self._num_special_tokens + self._extra_ids





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





       '''simple docstring'''






       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_a							,					token_ids_a=_a							,					already_has_special_tokens=_a )

       # normal case: some special tokens
       if token_ids_a is None:
            return ([0] * len(_a )) + [1]
       return ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]





  def 						A  (		self      :	str							,					_a      :	List[int] )	->    List[int]:





       '''simple docstring'''






       if len(_a ) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
                ' eos tokens being added.' )
            return token_ids
       else:
            return token_ids + [self.eos_token_id]





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.eos_token_id]

       if token_ids_a is None:
            return len(token_ids_a + eos ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos ) * [0]





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._add_eos_if_not_present(_a )
       if token_ids_a is None:
            return token_ids_a
       else:
            _SCREAMING_SNAKE_CASE							    =self._add_eos_if_not_present(_a )
            return token_ids_a + token_ids_a





  def 						A  (		self      :	List[Any]							,					_a      :	str )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[chr(_a ) for i in text.encode('utf-8' )]
       return tokens





  def 						A  (		self      :	List[Any]							,					_a      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






       if token in self.special_tokens_encoder:
            _SCREAMING_SNAKE_CASE							    =self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
            _SCREAMING_SNAKE_CASE							    =self.added_tokens_encoder[token]
       elif len(_a ) != 1:
            _SCREAMING_SNAKE_CASE							    =self.unk_token_id
       else:
            _SCREAMING_SNAKE_CASE							    =ord(_a ) + self._num_special_tokens
       return token_id





  def 						A  (		self      :	Tuple							,					_a      :	Optional[int] )	->    str:





       '''simple docstring'''






       if index in self.special_tokens_decoder:
            _SCREAMING_SNAKE_CASE							    =self.special_tokens_decoder[index]
       else:
            _SCREAMING_SNAKE_CASE							    =chr(index - self._num_special_tokens )
       return token





  def 						A  (		self      :	int							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =b''
       for token in tokens:
            if token in self.special_tokens_decoder:
                 _SCREAMING_SNAKE_CASE							    =self.special_tokens_decoder[token].encode('utf-8' )
            elif token in self.added_tokens_decoder:
                 _SCREAMING_SNAKE_CASE							    =self.special_tokens_decoder[token].encode('utf-8' )
            elif token in self.special_tokens_encoder:
                 _SCREAMING_SNAKE_CASE							    =token.encode('utf-8' )
            elif token in self.added_tokens_encoder:
                 _SCREAMING_SNAKE_CASE							    =token.encode('utf-8' )
            else:
                 _SCREAMING_SNAKE_CASE							    =bytes([ord(_a )] )
            bstring += tok_string
       _SCREAMING_SNAKE_CASE							    =bstring.decode('utf-8'							,					errors='ignore' )
       return string





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





       '''simple docstring'''






       return ()

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				["image_processor", "tokenizer"]
   __A	:		Tuple         =				"LayoutLMv3ImageProcessor"
   __A	:		List[Any]         =				("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int=None	,						lowercase_				:     str=None	,						**lowercase_				:     Optional[Any]  )					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."	,						lowercase_	,						)
           lowercase__ :   Optional[int]    					=				kwargs.pop("feature_extractor"  )

       lowercase__ :   int    					=				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."  )
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."  )

       super().__init__(lowercase_	,						lowercase_  )






   def __call__(      self				:     Dict	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						lowercase_				:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						lowercase_				:     Union[List[List[int]], List[List[List[int]]]] = None	,						lowercase_				:     Optional[Union[List[int], List[List[int]]]] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[bool, str, PaddingStrategy] = False	,						lowercase_				:     Union[bool, str, TruncationStrategy] = None	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     int = 0	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						**lowercase_				:     Dict	,						)					->						BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."  )

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."  )

       # first, apply the image processor
       lowercase__ :   Union[str, Any]    					=				self.image_processor(images=lowercase_	,						return_tensors=lowercase_  )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_	,						lowercase_  ):
               lowercase__ :   Optional[Any]    					=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__ :   Any    					=				features["words"]

       lowercase__ :   Tuple    					=				self.tokenizer(
           text=text if text is not None else features["words"]	,						text_pair=text_pair if text_pair is not None else None	,						boxes=boxes if boxes is not None else features["boxes"]	,						word_labels=lowercase_	,						add_special_tokens=lowercase_	,						padding=lowercase_	,						truncation=lowercase_	,						max_length=lowercase_	,						stride=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_token_type_ids=lowercase_	,						return_attention_mask=lowercase_	,						return_overflowing_tokens=lowercase_	,						return_special_tokens_mask=lowercase_	,						return_offsets_mapping=lowercase_	,						return_length=lowercase_	,						verbose=lowercase_	,						return_tensors=lowercase_	,						**lowercase_	,						)

       # add pixel values
       lowercase__ :   Optional[int]    					=				features.pop("pixel_values"  )
       if return_overflowing_tokens is True:
           lowercase__ :   Dict    					=				self.get_overflowing_images(lowercase_	,						encoded_inputs["overflow_to_sample_mapping"]  )
       lowercase__ :   str    					=				images

       return encoded_inputs






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     List[Any]  )					->						Dict:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__ :   Tuple    					=				[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]  )

       if len(lowercase_  ) != len(lowercase_  ):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_  )} and {len(lowercase_  )}'''  )

       return images_with_overflow






   def       __UpperCamelCase     (      self				:     int	,						*lowercase_				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						*lowercase_				:     str	,						**lowercase_				:     int  )					->						Dict:
       return self.tokenizer.decode(*lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Any:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,						lowercase_	,						)
       return self.image_processor_class







   @property
   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Tuple:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,						lowercase_	,						)
       return self.image_processor



