


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 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Dict       =				["""image_processor""", """tokenizer"""]
						SCREAMING_SNAKE_CASE__   :	Any       =				"""LayoutLMv2ImageProcessor"""
						SCREAMING_SNAKE_CASE__   :	List[Any]       =				("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")




						def __init__(					self ,    __lowercase=None ,    __lowercase=None ,    **__lowercase				)       ->     str:
										lowerCAmelCase_			:   Tuple     					=					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_			:   str     					=					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 = False ,    __lowercase = None ,    __lowercase = 0 ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = False ,    __lowercase = False ,    __lowercase = False ,    __lowercase = False ,    __lowercase = True ,    __lowercase = None ,    **__lowercase ,    )       ->     BatchEncoding:
										# 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.'''				)

										if return_overflowing_tokens is True and return_offsets_mapping is False:
														raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''				)

										# first, apply the image processor
										lowerCAmelCase_			:   Optional[Any]     					=					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_			:   List[Any]     					=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
														lowerCAmelCase_			:   str     					=					features['''words''']

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

										return encoded_inputs




						def     lowercase_			(					self ,    __lowercase ,    __lowercase				)       ->     int:
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										lowerCAmelCase_			:   str     					=					[]
										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     lowercase_			(					self ,    *__lowercase ,    **__lowercase				)       ->     Union[str, Any]:
										return self.tokenizer.batch_decode(*__lowercase ,    **__lowercase				)




						def     lowercase_			(					self ,    *__lowercase ,    **__lowercase				)       ->     Any:
										return self.tokenizer.decode(*__lowercase ,    **__lowercase				)




						@property
						def     lowercase_			(					self				)       ->     int:
										return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




						@property
						def     lowercase_			(					self				)       ->     Dict:
										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     lowercase_			(					self				)       ->     Any:
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,    __lowercase ,    )
										return self.image_processor



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			set:
				assert isinstance(lowerCAmelCase_			, lowerCAmelCase_			), f"""The input value of [set_a={set_a}] is not a set"""
				assert isinstance(lowerCAmelCase_			, lowerCAmelCase_			), f"""The input value of [set_b={set_b}] is not a set"""

				return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
						from doctest import testmod

						testmod()