




"""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__ =     ["""image_processor""", """tokenizer"""]
			lowerCamelCase__ =     """LayoutLMv3ImageProcessor"""
			lowerCamelCase__ =     ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
			def __init__(    self    , lowercase=None    , lowercase=None    , **lowercase    ):
							_lowerCamelCase	:     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.'    , lowercase    , )
											_lowerCamelCase	:     Dict       =       kwargs.pop('feature_extractor'    )

							_lowerCamelCase	:     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__(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    , ):
							# 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
							_lowerCamelCase	:     Tuple       =       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    ):
															_lowerCamelCase	:     Dict       =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
											_lowerCamelCase	:     int       =       features['words']

							_lowerCamelCase	:     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=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
							_lowerCamelCase	:     Optional[Any]       =       features.pop('pixel_values'    )
							if return_overflowing_tokens is True:
											_lowerCamelCase	:     Any       =       self.get_overflowing_images(lowercase    , encoded_inputs['overflow_to_sample_mapping']    )
							_lowerCamelCase	:     Tuple       =       images

							return encoded_inputs
			def    A_       (    self    , lowercase    , lowercase    ):
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							_lowerCamelCase	:     List[Any]       =       []
							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    A_       (    self    , *lowercase    , **lowercase    ):
							return self.tokenizer.batch_decode(*lowercase    , **lowercase    )
			def    A_       (    self    , *lowercase    , **lowercase    ):
							return self.tokenizer.decode(*lowercase    , **lowercase    )
			@property
			def    A_       (    self    ):
							return ["input_ids", "bbox", "attention_mask", "pixel_values"]
			@property
			def    A_       (    self    ):
							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    A_       (    self    ):
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    , lowercase    , )
							return self.image_processor





"""simple docstring"""






from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class lowerCAmelCase__      :






			'''simple docstring'''



			pass