




'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


lowercase__								=						logging.get_logger(__name__)

lowercase__								=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__								=						{
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase__								=						{
    '''microsoft/layoutlm-base-uncased''': 5_1_2,
    '''microsoft/layoutlm-large-uncased''': 5_1_2,
}

lowercase__								=						{
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	VOCAB_FILES_NAMES
      __lowercase					=	PRETRAINED_VOCAB_FILES_MAP
      __lowercase					=	PRETRAINED_INIT_CONFIGURATION
      __lowercase					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase					=	LayoutLMTokenizer






      def __init__(self			,	a__=None			,	a__=None			,	a__=True			,	a__="[UNK]"			,	a__="[SEP]"			,	a__="[PAD]"			,	a__="[CLS]"			,	a__="[MASK]"			,	a__=True			,	a__=None			,	**a__			,	)							-> int:
         super().__init__(
             a__			,	tokenizer_file=a__			,	do_lower_case=a__			,	unk_token=a__			,	sep_token=a__			,	pad_token=a__			,	cls_token=a__			,	mask_token=a__			,	tokenize_chinese_chars=a__			,	strip_accents=a__			,	**a__			,	)

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get('''lowercase'''			,	a__       ) != do_lower_case
             or normalizer_state.get('''strip_accents'''			,	a__       ) != strip_accents
             or normalizer_state.get('''handle_chinese_chars'''			,	a__       ) != tokenize_chinese_chars
         ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			getattr(a__			,	normalizer_state.pop('''type'''       )       )
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			do_lower_case
            SCREAMING_SNAKE_CASE__      :					Dict							    =			strip_accents
            SCREAMING_SNAKE_CASE__      :					int							    =			tokenize_chinese_chars
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			normalizer_class(**a__       )

         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			do_lower_case






      def  A__(self			,	a__			,	a__=None       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

         return output






      def  A__(self			,	a__			,	a__ = None       )							-> List[int]:
         SCREAMING_SNAKE_CASE__      :					str							    =			[self.sep_token_id]
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			[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  A__(self			,	a__			,	a__ = None       )							-> Tuple[str]:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self._tokenizer.model.save(a__			,	name=a__       )
         return tuple(a__       )







'''simple docstring'''





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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[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.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					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			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> 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
         SCREAMING_SNAKE_CASE__      :					str							    =			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__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					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
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         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  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> 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  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

