
'''simple docstring'''





from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)


SCREAMING_SNAKE_CASE     =       {
    '''nielsr/canine-s''': 2_0_4_8,
}

# Unicode defines 1,114,112 total “codepoints”
SCREAMING_SNAKE_CASE     =       1_1_1_4_1_1_2

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
SCREAMING_SNAKE_CASE     =       0

SCREAMING_SNAKE_CASE     =       0Xe000
SCREAMING_SNAKE_CASE     =       0Xe001
SCREAMING_SNAKE_CASE     =       0Xe002
SCREAMING_SNAKE_CASE     =       0Xe003
SCREAMING_SNAKE_CASE     =       0Xe004

# Maps special codepoints to human-readable names.
SCREAMING_SNAKE_CASE     =       {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
SCREAMING_SNAKE_CASE     =       {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







 def __init__(					self		:  int			,				snake_case__		:  Tuple=chr(lowercase__ )			,				snake_case__		:  Optional[int]=chr(lowercase__ )			,				snake_case__		:  List[str]=chr(lowercase__ )			,				snake_case__		:  Optional[Any]=chr(lowercase__ )			,				snake_case__		:  Any=chr(lowercase__ )			,				snake_case__		:  int=chr(lowercase__ )			,				snake_case__		:  Optional[Any]=False			,				snake_case__		:  Union[str, Any]=20_48			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							AddedToken(lowercase__			,				lstrip=lowercase__			,				rstrip=lowercase__ ) if isinstance(lowercase__			,				lowercase__ ) else bos_token
   UpperCAmelCase__      :					str				       =							AddedToken(lowercase__			,				lstrip=lowercase__			,				rstrip=lowercase__ ) if isinstance(lowercase__			,				lowercase__ ) else eos_token
   UpperCAmelCase__      :					int				       =							AddedToken(lowercase__			,				lstrip=lowercase__			,				rstrip=lowercase__ ) if isinstance(lowercase__			,				lowercase__ ) else sep_token
   UpperCAmelCase__      :					int				       =							AddedToken(lowercase__			,				lstrip=lowercase__			,				rstrip=lowercase__ ) if isinstance(lowercase__			,				lowercase__ ) else cls_token
   UpperCAmelCase__      :					Tuple				       =							AddedToken(lowercase__			,				lstrip=lowercase__			,				rstrip=lowercase__ ) if isinstance(lowercase__			,				lowercase__ ) else pad_token

   # Mask token behave like a normal word, i.e. include the space before it
   UpperCAmelCase__      :					int				       =							AddedToken(lowercase__			,				lstrip=lowercase__			,				rstrip=lowercase__ ) if isinstance(lowercase__			,				lowercase__ ) else mask_token

   super().__init__(
       bos_token=lowercase__			,				eos_token=lowercase__			,				sep_token=lowercase__			,				cls_token=lowercase__			,				pad_token=lowercase__			,				mask_token=lowercase__			,				add_prefix_space=lowercase__			,				model_max_length=lowercase__			,				**lowercase__			,				)

   # Creates a mapping for looking up the IDs of special symbols.
   UpperCAmelCase__      :					List[str]				       =							{}
   for codepoint, name in SPECIAL_CODEPOINTS.items():
     UpperCAmelCase__      :					Any				       =							codepoint

   # Creates a mapping for looking up the string forms of special symbol IDs.
   UpperCAmelCase__      :					Tuple				       =							{
       codepoint: name for name, codepoint in self._special_codepoints.items()
   }

   UpperCAmelCase__      :					str				       =							UNICODE_VOCAB_SIZE
   UpperCAmelCase__      :					Union[str, Any]				       =							len(self._special_codepoints )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self._unicode_vocab_size







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   return list(lowercase__ )







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  List[Any] ):



   '''simple docstring'''




   try:
     return ord(lowercase__ )
   except TypeError:
     raise ValueError(F"""invalid token: \'{token}\'""" )







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  int ):



   '''simple docstring'''




   try:
     if index in SPECIAL_CODEPOINTS:
       return SPECIAL_CODEPOINTS[index]
     return chr(lowercase__ )
   except TypeError:
     raise ValueError(F"""invalid id: {index}""" )







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  List[Any] ):



   '''simple docstring'''




   return "".join(lowercase__ )







 def 							UpperCamelCase  (					self		:  str			,				snake_case__		:  str			,				snake_case__		:  List[Any] = None ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							[self.sep_token_id]
   UpperCAmelCase__      :					Any				       =							[self.cls_token_id]

   UpperCAmelCase__      :					List[Any]				       =							cls + token_ids_a + sep
   if token_ids_a is not None:
     result += token_ids_a + sep
   return result







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  str			,				snake_case__		:  Tuple = None			,				snake_case__		:  Tuple = False ):



   '''simple docstring'''




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

   UpperCAmelCase__      :					List[str]				       =							[1] + ([0] * len(lowercase__ )) + [1]
   if token_ids_a is not None:
     result += ([0] * len(lowercase__ )) + [1]
   return result







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Dict			,				snake_case__		:  int = None ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							[self.sep_token_id]
   UpperCAmelCase__      :					Any				       =							[self.cls_token_id]

   UpperCAmelCase__      :					Any				       =							len(cls + token_ids_a + sep ) * [0]
   if token_ids_a is not None:
     result += len(token_ids_a + sep ) * [1]
   return result







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  Dict			,				snake_case__		:  Any = None ):



   '''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        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =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 ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             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.'''     )

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                   raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''     )

             # first, apply the image processor
             __A    =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__     ):
                         __A    =[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   __A    =features['''words''']

             __A    =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
             __A    =features.pop('''pixel_values'''     )
             if return_overflowing_tokens is True:
                   __A    =self.get_overflowing_images(lowercase__ ,   encoded_inputs['''overflow_to_sample_mapping''']     )
             __A    =images

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             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 ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return ["input_ids", "bbox", "attention_mask", "image"]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase__ ,   )
             return self.image_processor


