



lowerCamelCase		       =      [
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages







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						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       ["image_processor", "tokenizer"]
							A				:Any =       "LayoutLMv3ImageProcessor"
							A				:str =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



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







											"""simple docstring"""



											a__	:							str      						= 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__	:							List[str]      						= kwargs.pop("feature_extractor"				)

											a__	:							Optional[int]      						= 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 ,					):







											"""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__	:							List[str]      						= 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__	:							Union[str, Any]      						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
															a__	:							Optional[Any]      						= features["words"]

											a__	:							Union[str, Any]      						= 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__	:							Dict      						= features.pop("pixel_values"				)
											if return_overflowing_tokens is True:
															a__	:							str      						= self.get_overflowing_images(__UpperCAmelCase ,					encoded_inputs["overflow_to_sample_mapping"]				)
											a__	:							Tuple      						= images

											return encoded_inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= []
											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 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



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



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



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



							@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." ,					__UpperCAmelCase ,					)
											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." ,					__UpperCAmelCase ,					)
											return self.image_processor


