


'''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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    ["image_processor", "tokenizer"]
     __magic_name__    			=    "LayoutLMv3ImageProcessor"
     __magic_name__    			=    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")







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

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.'      ,	snake_case__      ,	)
                   _lowerCAmelCase    :							int   			=       kwargs.pop('feature_extractor'   )

            _lowerCAmelCase    :							Tuple   			=       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__(snake_case__      ,	snake_case__   )







     def __call__( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = True      ,	snake_case__ = False      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = 0      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = True      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''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
            _lowerCAmelCase    :							Optional[Any]   			=       self.image_processor(images=snake_case__      ,	return_tensors=snake_case__   )

            # second, apply the tokenizer
            if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(snake_case__      ,	snake_case__   ):
                          _lowerCAmelCase    :							Union[str, Any]   			=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
                   _lowerCAmelCase    :							List[str]   			=       features['words']

            _lowerCAmelCase    :							List[str]   			=       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=snake_case__      ,	add_special_tokens=snake_case__      ,	padding=snake_case__      ,	truncation=snake_case__      ,	max_length=snake_case__      ,	stride=snake_case__      ,	pad_to_multiple_of=snake_case__      ,	return_token_type_ids=snake_case__      ,	return_attention_mask=snake_case__      ,	return_overflowing_tokens=snake_case__      ,	return_special_tokens_mask=snake_case__      ,	return_offsets_mapping=snake_case__      ,	return_length=snake_case__      ,	verbose=snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__      ,	)

            # add pixel values
            _lowerCAmelCase    :							Dict   			=       features.pop('pixel_values'   )
            if return_overflowing_tokens is True:
                   _lowerCAmelCase    :							Any   			=       self.get_overflowing_images(snake_case__      ,	encoded_inputs['overflow_to_sample_mapping']   )
            _lowerCAmelCase    :							Any   			=       images

            return encoded_inputs







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       []
            for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]   )

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

            return images_with_overflow







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     @property
     def a	( self   ):

            '''simple docstring'''

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







     @property
     def a	( self   ):

            '''simple docstring'''

            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,	snake_case__      ,	)
            return self.image_processor_class







     @property
     def a	( self   ):

            '''simple docstring'''

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





'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

