

'''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 					A__      (      _snake_case			):
    lowercase						=		["image_processor", "tokenizer"]
    lowercase						=		"LayoutLMv3ImageProcessor"
    lowercase						=		("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



    def __init__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           A_						=      None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""		,				UpperCamelCase__		,				)
                  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__(UpperCamelCase__		,				UpperCamelCase__       )



    def __call__(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = True		,				UpperCamelCase__ = False		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = 0		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = False		,				UpperCamelCase__ = False		,				UpperCamelCase__ = False		,				UpperCamelCase__ = False		,				UpperCamelCase__ = True		,				UpperCamelCase__ = None		,				**UpperCamelCase__		,				)						->      BatchEncoding:





           '''simple docstring'''
           # 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."""       )

           # first, apply the image processor
           A_						=      self.image_processor(images=UpperCamelCase__		,				return_tensors=UpperCamelCase__       )

           # second, apply the tokenizer
           if text is not None and self.image_processor.apply_ocr and text_pair is None:
                  if isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                         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=UpperCamelCase__		,				add_special_tokens=UpperCamelCase__		,				padding=UpperCamelCase__		,				truncation=UpperCamelCase__		,				max_length=UpperCamelCase__		,				stride=UpperCamelCase__		,				pad_to_multiple_of=UpperCamelCase__		,				return_token_type_ids=UpperCamelCase__		,				return_attention_mask=UpperCamelCase__		,				return_overflowing_tokens=UpperCamelCase__		,				return_special_tokens_mask=UpperCamelCase__		,				return_offsets_mapping=UpperCamelCase__		,				return_length=UpperCamelCase__		,				verbose=UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__		,				)

           # add pixel values
           A_						=      features.pop("""pixel_values"""       )
           if return_overflowing_tokens is True:
                  A_						=      self.get_overflowing_images(UpperCamelCase__		,				encoded_inputs["""overflow_to_sample_mapping"""]       )
           A_						=      images

           return encoded_inputs



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
           A_						=      []
           for sample_idx in overflow_to_sample_mapping:
                  images_with_overflow.append(images[sample_idx]       )

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

           return images_with_overflow



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    @property
    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           return ["input_ids", "bbox", "attention_mask", "pixel_values"]



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





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







    @property
    def 					snake_case_			(					self       )						->      Dict:





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



'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      None:





           '''simple docstring'''
           warnings.warn(
               """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
               """ use BeitImageProcessor instead."""		,				UpperCamelCase__		,				)
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
