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 (  lowerCamelCase__ ):
 __lowerCamelCase     : Tuple 	=   ["""image_processor""", """tokenizer"""]
 __lowerCamelCase     : Union[str, Any] 	=   """LayoutLMv2ImageProcessor"""
 __lowerCamelCase     : List[Any] 	=   ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
 def __init__(	self		, snake_case__=None		, snake_case__=None		, **snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''		, lowerCamelCase__		, )
             UpperCAmelCase     :			Tuple     	=kwargs.pop('''feature_extractor'''    )

       UpperCAmelCase     :			Optional[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__(lowerCamelCase__		, lowerCamelCase__    )
 def __call__(	self		, snake_case__		, snake_case__ = None		, snake_case__ = None		, snake_case__ = None		, snake_case__ = None		, snake_case__ = True		, snake_case__ = False		, snake_case__ = False		, snake_case__ = None		, snake_case__ = 0		, snake_case__ = None		, snake_case__ = None		, snake_case__ = None		, snake_case__ = False		, snake_case__ = False		, snake_case__ = False		, snake_case__ = False		, snake_case__ = True		, snake_case__ = None		, **snake_case__		, )						->				Dict:
       '''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
       UpperCAmelCase     :			Dict     	=self.image_processor(images=lowerCamelCase__		, return_tensors=lowerCamelCase__    )

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

       UpperCAmelCase     :			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=lowerCamelCase__		, add_special_tokens=lowerCamelCase__		, padding=lowerCamelCase__		, truncation=lowerCamelCase__		, max_length=lowerCamelCase__		, stride=lowerCamelCase__		, pad_to_multiple_of=lowerCamelCase__		, return_token_type_ids=lowerCamelCase__		, return_attention_mask=lowerCamelCase__		, return_overflowing_tokens=lowerCamelCase__		, return_special_tokens_mask=lowerCamelCase__		, return_offsets_mapping=lowerCamelCase__		, return_length=lowerCamelCase__		, verbose=lowerCamelCase__		, return_tensors=lowerCamelCase__		, **lowerCamelCase__		, )

       # add pixel values
       UpperCAmelCase     :			Tuple     	=features.pop('''pixel_values'''    )
       if return_overflowing_tokens is True:
             UpperCAmelCase     :			List[str]     	=self.get_overflowing_images(lowerCamelCase__		, encoded_inputs['''overflow_to_sample_mapping''']    )
       UpperCAmelCase     :			Tuple     	=images

       return encoded_inputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





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

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

       return images_with_overflow
 def 						UpperCAmelCase__							(	self		, *snake_case__		, **snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       return self.tokenizer.batch_decode(*lowerCamelCase__		, **lowerCamelCase__    )
 def 						UpperCAmelCase__							(	self		, *snake_case__		, **snake_case__    )						->				List[str]:
       '''simple docstring'''





       return self.tokenizer.decode(*lowerCamelCase__		, **lowerCamelCase__    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
 @property
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





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






 @property
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





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








from __future__ import annotations






def 				A	(			a_				,a_   )	->		list[int]:
					__UpperCamelCase :   dict[int, int]									={}
					for index, val in enumerate(a_   ):
										__UpperCamelCase :   Union[str, Any]									=target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										__UpperCamelCase :   List[Any]									=index
					return []


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"{two_sum([2, 7, 11, 15], 9) = }")

