
import functools
from typing import Any




def 							a		(				snake_case__:   str		,  snake_case__:   list[str]   ):


				'''simple docstring'''

				# Validation
				if not isinstance(snake_case__		,  snake_case__   ) or len(snake_case__   ) == 0:
								raise ValueError('''the string should be not empty string'''   )

				if not isinstance(snake_case__		,  snake_case__   ) or not all(
				    isinstance(snake_case__		,  snake_case__   ) and len(snake_case__   ) > 0 for item in words   ):
								raise ValueError('''the words should be a list of non-empty strings'''   )

				# Build trie
				lowercase_	    =   {}
				lowercase_	    =   '''WORD_KEEPER'''

				for word in words:
								lowercase_	    =   trie
								for c in word:
												if c not in trie_node:
																lowercase_	    =   {}

												lowercase_	    =   trie_node[c]

								lowercase_	    =   True

				lowercase_	    =   len(snake_case__   )

				# Dynamic programming method
				@functools.cache
				def is_breakable(snake_case__:   int   ) -> bool:
								if index == len_string:
												return True

								lowercase_	    =   trie
								for i in range(snake_case__		,  snake_case__   ):
												lowercase_	    =   trie_node.get(string[i]		,  snake_case__   )

												if trie_node is None:
																return False

												if trie_node.get(snake_case__		,  snake_case__   ) and is_breakable(i + 1   ):
																return True

								return False

				return is_breakable(0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()











'''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		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["image_processor", "tokenizer"]
     snake_case_						      =   "LayoutLMv2ImageProcessor"
     snake_case_						      =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



     def __init__(   self							:			List[Any]   ,       __snake_case							:			Union[str, Any]=None   ,       __snake_case							:			Optional[int]=None   ,       **__snake_case							:			Union[str, Any]						)->	str:
        if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,       __snake_case   ,       )
           snake_case 						=   kwargs.pop("""feature_extractor"""						)

        snake_case 						=   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__(__snake_case   ,       __snake_case						)



     def __call__(   self							:			Optional[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,       __snake_case							:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,       __snake_case							:			Union[List[List[int]], List[List[List[int]]]] = None   ,       __snake_case							:			Optional[Union[List[int], List[List[int]]]] = None   ,       __snake_case							:			bool = True   ,       __snake_case							:			Union[bool, str, PaddingStrategy] = False   ,       __snake_case							:			Union[bool, str, TruncationStrategy] = None   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			int = 0   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = True   ,       __snake_case							:			Optional[Union[str, TensorType]] = None   ,       **__snake_case							:			List[str]   ,       )->	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
        snake_case 						=   self.image_processor(images=__snake_case   ,       return_tensors=__snake_case						)

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

        snake_case 						=   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=__snake_case   ,       add_special_tokens=__snake_case   ,       padding=__snake_case   ,       truncation=__snake_case   ,       max_length=__snake_case   ,       stride=__snake_case   ,       pad_to_multiple_of=__snake_case   ,       return_token_type_ids=__snake_case   ,       return_attention_mask=__snake_case   ,       return_overflowing_tokens=__snake_case   ,       return_special_tokens_mask=__snake_case   ,       return_offsets_mapping=__snake_case   ,       return_length=__snake_case   ,       verbose=__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case   ,       )

        # add pixel values
        snake_case 						=   features.pop("""pixel_values"""						)
        if return_overflowing_tokens is True:
           snake_case 						=   self.get_overflowing_images(__snake_case   ,       encoded_inputs["""overflow_to_sample_mapping"""]						)
        snake_case 						=   images

        return encoded_inputs



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			List[Any]   ,       __snake_case							:			str						)->	List[str]:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        snake_case 						=   []
        for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

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

        return images_with_overflow



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			Tuple						)->	str:
        return self.tokenizer.batch_decode(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       *__snake_case							:			Tuple   ,       **__snake_case							:			Optional[int]						)->	List[Any]:
        return self.tokenizer.decode(*__snake_case   ,       **__snake_case						)



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



     @property
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,       __snake_case   ,       )
        return self.image_processor_class



     @property
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Tuple:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,       __snake_case   ,       )
        return self.image_processor

