





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     SCREAMING_SNAKE_CASE__ (						__A      ):
 __SCREAMING_SNAKE_CASE							       =	["image_processor", "tokenizer"]
 __SCREAMING_SNAKE_CASE							       =	"LayoutLMv3ImageProcessor"
 __SCREAMING_SNAKE_CASE							       =	("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,**__lowerCamelCase ):
        A__											=						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_,)
               A__											=						kwargs.pop('''feature_extractor''' )

        A__											=						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,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = 0,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = True,__lowerCamelCase = None,**__lowerCamelCase,):
        # 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
        A__											=						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_ ):
                      A__											=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
               A__											=						features["words"]

        A__											=						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
        A__											=						features.pop('''pixel_values''' )
        if return_overflowing_tokens is True:
               A__											=						self.get_overflowing_images(lowercase_,encoded_inputs['''overflow_to_sample_mapping'''] )
        A__											=						images

        return encoded_inputs



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



 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):
        return self.tokenizer.decode(*lowercase_,**lowercase_ )



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



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






import operator

def 			lowercase_				(			_lowerCamelCase							:       list		,	_lowerCamelCase							:       bool = False		,	_lowerCamelCase							:       list | None = None):
    lowercase__ :   int    					=				operator.lt if reverse else operator.gt
    lowercase__ :   str    					=				solution or []

    if not arr:
        return solution

    lowercase__ :   List[str]    					=				[arr.pop(0)]
    for i, item in enumerate(_lowerCamelCase):
        if _operator(_lowerCamelCase		,	sublist[-1]):
            sublist.append(_lowerCamelCase)
            arr.pop(_lowerCamelCase)

    #  merging sublist into solution list
    if not solution:
        solution.extend(_lowerCamelCase)
    else:
        while sublist:
            lowercase__ :   str    					=				sublist.pop(0)
            for i, xx in enumerate(_lowerCamelCase):
                if not _operator(_lowerCamelCase		,	_lowerCamelCase):
                    solution.insert(_lowerCamelCase		,	_lowerCamelCase)
                    break
            else:
                solution.append(_lowerCamelCase)

    strand_sort(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)
    return solution


if __name__ == "__main__":
      assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
      assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]



