"""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        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""image_processor""", """tokenizer"""]
      a__				=		"""LayoutLMv3ImageProcessor"""
      a__				=		("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      **lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Tuple      							=     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   ,      )
          a__:					Union[str, Any]      							=     kwargs.pop('feature_extractor')

        a__:					Optional[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__(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   ,      )     ->      BatchEncoding:





        '''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.')

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

        a__:					Dict      							=     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__:					List[str]      							=     features.pop('pixel_values')
        if return_overflowing_tokens is True:
          a__:					str      							=     self.get_overflowing_images(lowercase   ,      encoded_inputs['overflow_to_sample_mapping'])
        a__:					Optional[Any]      							=     images

        return encoded_inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[int]      							=     []
        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   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''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   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





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



"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


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

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

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': 512,
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		PRETRAINED_INIT_CONFIGURATION
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		LxmertTokenizer



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase=True   ,      lowercase="[UNK]"   ,      lowercase="[SEP]"   ,      lowercase="[PAD]"   ,      lowercase="[CLS]"   ,      lowercase="[MASK]"   ,      lowercase=True   ,      lowercase=None   ,      **lowercase   ,      )     ->      Dict:





        '''simple docstring'''





        super().__init__(
            lowercase   ,      tokenizer_file=lowercase   ,      do_lower_case=lowercase   ,      unk_token=lowercase   ,      sep_token=lowercase   ,      pad_token=lowercase   ,      cls_token=lowercase   ,      mask_token=lowercase   ,      tokenize_chinese_chars=lowercase   ,      strip_accents=lowercase   ,      **lowercase   ,      )

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

        a__:					Optional[int]      							=     do_lower_case



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=None)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     [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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.sep_token_id]
        a__:					List[Any]      							=     [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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        a__:					List[Any]      							=     self._tokenizer.model.save(lowercase   ,      name=lowercase)
        return tuple(lowercase)


