
'''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__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''image_processor''', '''tokenizer''']
					lowerCAmelCase_					       =					'''LayoutLMv2ImageProcessor'''
					lowerCAmelCase_					       =					('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






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


											"""simple docstring"""





											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''    ,							__SCREAMING_SNAKE_CASE    ,							)
																	lowercase_     :					Optional[int]													=	kwargs.pop('''feature_extractor'''		)

											lowercase_     :					Dict													=	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__(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def __call__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = 0    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""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.'''		)

											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
											lowercase_     :					Union[str, Any]													=	self.image_processor(images=__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE		)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
																	if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							lowercase_     :					Dict													=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
																	lowercase_     :					Optional[int]													=	features['''words''']

											lowercase_     :					Dict													=	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=__SCREAMING_SNAKE_CASE    ,							add_special_tokens=__SCREAMING_SNAKE_CASE    ,							padding=__SCREAMING_SNAKE_CASE    ,							truncation=__SCREAMING_SNAKE_CASE    ,							max_length=__SCREAMING_SNAKE_CASE    ,							stride=__SCREAMING_SNAKE_CASE    ,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE    ,							return_token_type_ids=__SCREAMING_SNAKE_CASE    ,							return_attention_mask=__SCREAMING_SNAKE_CASE    ,							return_overflowing_tokens=__SCREAMING_SNAKE_CASE    ,							return_special_tokens_mask=__SCREAMING_SNAKE_CASE    ,							return_offsets_mapping=__SCREAMING_SNAKE_CASE    ,							return_length=__SCREAMING_SNAKE_CASE    ,							verbose=__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)

											# add pixel values
											lowercase_     :					List[Any]													=	features.pop('''pixel_values'''		)
											if return_overflowing_tokens is True:
																	lowercase_     :					Dict													=	self.get_overflowing_images(__SCREAMING_SNAKE_CASE    ,							encoded_inputs['''overflow_to_sample_mapping''']		)
											lowercase_     :					int													=	images

											return encoded_inputs






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	[]
											for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx]		)

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

											return images_with_overflow






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return ["input_ids", "bbox", "attention_mask", "image"]






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





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






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





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





'''simple docstring'''







from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	prime_factors(__SCREAMING_SNAKE_CASE							)
						if is_square_free(__SCREAMING_SNAKE_CASE							):
												return -1 if len(__SCREAMING_SNAKE_CASE							) % 2 else 1
						return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()



