


"""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 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







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



				def __init__(						self       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		**UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				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__       ,		)
												SCREAMING_SNAKE_CASE__     							=				kwargs.pop("feature_extractor"     )

								SCREAMING_SNAKE_CASE__     							=				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__       ,		):
								# 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
								SCREAMING_SNAKE_CASE__     							=				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__     ):
																SCREAMING_SNAKE_CASE__     							=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
												SCREAMING_SNAKE_CASE__     							=				features["words"]

								SCREAMING_SNAKE_CASE__     							=				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
								SCREAMING_SNAKE_CASE__     							=				features.pop("pixel_values"     )
								if return_overflowing_tokens is True:
												SCREAMING_SNAKE_CASE__     							=				self.get_overflowing_images(UpperCAmelCase__       ,		encoded_inputs["overflow_to_sample_mapping"]     )
								SCREAMING_SNAKE_CASE__     							=				images

								return encoded_inputs



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								SCREAMING_SNAKE_CASE__     							=				[]
								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   lowerCAmelCase__		(						self       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								return self.tokenizer.batch_decode(*UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								return self.tokenizer.decode(*UpperCAmelCase__       ,		**UpperCAmelCase__     )



				@property
				def   lowerCAmelCase__		(						self     ):
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]



				@property
				def   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self     ):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,		UpperCAmelCase__       ,		)
								return self.image_processor




"""simple docstring"""






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					CustomTokenizer
				pass
