





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_rembert import RemBertTokenizer
else:
    lowercase :			Optional[Any]     		=    None

lowercase :			int     		=    logging.get_logger(__name__)
lowercase :			Optional[Any]     		=    {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase :			List[Any]     		=    {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase :			str     		=    {
    'google/rembert': 2_5_6,
}

lowercase :			List[str]     		=    '▁'





class 					A__       (				a__      ):




   """simple docstring"""



   __A  :							List[str]			    =    VOCAB_FILES_NAMES
   __A  :							Any			    =    PRETRAINED_VOCAB_FILES_MAP
   __A  :							str			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A  :							Optional[Any]			    =    RemBertTokenizer

   def __init__(   self   ,    lowercase=None   ,    lowercase=None   ,    lowercase=True   ,    lowercase=True   ,    lowercase=False   ,    lowercase="[CLS]"   ,    lowercase="[SEP]"   ,    lowercase="<unk>"   ,    lowercase="[SEP]"   ,    lowercase="<pad>"   ,    lowercase="[CLS]"   ,    lowercase="[MASK]"   ,    **lowercase   ,    )     ->		List[Any]:







          '''simple docstring'''

          a__		:      Optional[int]					=							AddedToken(__a   ,    lstrip=__a   ,    rstrip=__a) if isinstance(__a   ,    __a) else mask_token

          super().__init__(
              __a   ,    tokenizer_file=__a   ,    do_lower_case=__a   ,    remove_space=__a   ,    keep_accents=__a   ,    bos_token=__a   ,    eos_token=__a   ,    unk_token=__a   ,    sep_token=__a   ,    pad_token=__a   ,    cls_token=__a   ,    mask_token=__a   ,    **__a   ,    )

          a__		:      List[str]					=							do_lower_case
          a__		:      Dict					=							remove_space
          a__		:      Optional[Any]					=							keep_accents
          a__		:      int					=							vocab_file
          a__		:      List[str]					=							False if not self.vocab_file else True

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		List[int]:







          '''simple docstring'''

          a__		:      Dict					=							[self.sep_token_id]
          a__		:      List[Any]					=							[self.cls_token_id]
          if token_ids_a is None:
                 return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None   ,    lowercase = False)     ->		List[int]:







          '''simple docstring'''

          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            'You should not supply a second sequence if the provided sequence of '
                            'ids is already formatted with special tokens for the model.')
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
                 return [1] + ([0] * len(__a)) + [1] + ([0] * len(__a)) + [1]
          return [1] + ([0] * len(__a)) + [1]

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		List[int]:







          '''simple docstring'''

          a__		:      Optional[Any]					=							[self.sep_token_id]
          a__		:      Dict					=							[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) * [0] + len(token_ids_a + sep) * [1]

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		Tuple[str]:







          '''simple docstring'''

          if not os.path.isdir(__a):
                 logger.error('Vocabulary path ({}) should be a directory'.format(__a))
                 return
          a__		:      Dict					=							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,)






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   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



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



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

          lowerCamelCase_						:   List[Any]		= 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__(__a		,				__a							)



       def __call__(						self				:				List[str]		,				__a				:				List[Any]		,				__a				:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				__a				:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				__a				:				Union[List[List[int]], List[List[List[int]]]] = None		,				__a				:				Optional[Union[List[int], List[List[int]]]] = 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				:				Optional[bool] = None		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = True		,				__a				:				Optional[Union[str, TensorType]] = None		,				**__a				:				Any		,				)					->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."""							)

          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
          lowerCamelCase_						:   Tuple		= self.image_processor(images=__a		,				return_tensors=__a							)

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

          lowerCamelCase_						:   int		= 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=__a		,				add_special_tokens=__a		,				padding=__a		,				truncation=__a		,				max_length=__a		,				stride=__a		,				pad_to_multiple_of=__a		,				return_token_type_ids=__a		,				return_attention_mask=__a		,				return_overflowing_tokens=__a		,				return_special_tokens_mask=__a		,				return_offsets_mapping=__a		,				return_length=__a		,				verbose=__a		,				return_tensors=__a		,				**__a		,				)

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

          return encoded_inputs



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

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

          return images_with_overflow



       def 				_lowerCAmelCase							(						self				:				Dict		,				*__a				:				Dict		,				**__a				:				int							)					->int:
          return self.tokenizer.batch_decode(*__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				*__a				:				List[str]		,				**__a				:				int							)					->List[Any]:
          return self.tokenizer.decode(*__a		,				**__a							)



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->List[str]:
          return ["input_ids", "bbox", "attention_mask", "image"]



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->str:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,				__a		,				)
          return self.image_processor_class




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





